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The description will be given with reference. First, when a processing start command for the wafer W is sent to the main control device 20 of the exposure apparatus via the LAN and the exposure process control controller , the main control device 20 Based on the start command, various control signals for causing the exposure apparatus , the coater unit , the developer unit , and the inline measuring device to perform processing on the wafer W in a predetermined procedure are output.

When this control signal is output, one wafer W taken out from the wafer carrier is applied with the photoresist transferred to the resist coater via the transfer line , and the pre-beta device along the transfer line is applied. After the resist processing is performed through and the cooling device S 10 , it is put into the stage device of the in-line measuring instrument and the in-line pre-measurement processing for the wafer W is performed S However, here, the pre-measurement process S11 is performed after the resist process S10 , but this order may be reversed.

However, if the pre-measurement processing is performed after resist coating, the measurement on wafer W in the state when it is actually put into the exposure system that is, the state where resist is applied measurement affected by resist Thus, it is advantageous in terms of measurement accuracy.

This measurement will be described later. The measurement results for example, the center position and rotation amount of the wafer W, the coordinate position information of the mark, etc. Based on these notified data, main controller 20 performs a process of optimizing the measurement conditions when measuring the edge or mark of wafer W when alignment is performed by exposure apparatus S In order to reduce the processing load on the main controller 20, the analysis system may perform part or all of such optimization processing, and the analysis result may be sent to the main controller It is.

The transfer robot 70 transfers the received wafer W onto the table 61, and the table 61 holds the wafer W by suction. At this point, it is assumed that the wafer W is roughly adjusted in its X-axis direction, Y-axis direction, and notch or OF direction by an alignment device not shown. Then, the wafer transfer arm 64 holding the wafer W is driven to a predetermined position a position where the wafer W can be delivered in the space of the loading arm 36 waiting above the wafer load position.

The wafer W held on the arm 64 is put into the space portion of the above-described loading arm The wafer is loaded until the wafer W is completely supported by the loading arm When the system 36 is raised, the stage controller 19 retracts the wafer transfer arm 64 to the -Y side.

Thereby, the delivery of the wafer W from the wafer transfer arm 64 to the loading arm 36 is completed. The stage controller 19 is instructed to move insert the background plates 41a to 41c below the wafer W. Instructs alignment measurement. Based on this instruction, the stage control device 19 starts measuring the outer edge of the wafer W using the measurement units 40 a to 40 c constituting the preparation device That is, the wafer W pre-alignment using the wafer bri alignment device 32 is started in this manner S The imaging result is sent to the bri alignment device main body 34, and the bri alignment device main body 34 calculates the center position and rotation amount of the wafer W held by the loading arm 36 based on the image data.

To the main controller 20 via the stage controller Measurement units 40a, 40b, and 40c are set to capture the image at , , and when the wafer W is at the design position dotted line position. In this embodiment, the position P of the notch of the wafer W is obtained based on the imaging results of the imaging fields of view VA to VC, and two points edge points P X, Y of the other wafer W edge based on the notch reference.

As a premise, point P and point P are their distance 2d force. In the next step , the imaging result VB. Next step 5. In the next step If this determination is affirmed, the process returns to step , and if not, the process proceeds to step Here, since it is still the first time, the determination is affirmed and the process returns to step Then, after the processing of step to step is performed again, the determination is denied in step and the process proceeds to step The position of this notch P force The reference point on the outer edge of the surface to be measured of the object.

Based on the notch position P and the edge points P and P, the wafer W. As described above, in the bri alignment, the outer shape of the wafer is first detected, and the center position and the rotation amount of the wafer W in the coordinate system outer shape reference coordinate system based on the outer shape are calculated.

In Fig. Note that the loop from step to step may be repeated several times. The background plates 41 a to 41 c are retracted based on an instruction from the main controller Thereby, the stage control device 19 moves the wafer stage WST to the wafer loading position via the wafer driving device 15 while monitoring the measurement value of the wafer laser interferometer With the above operation, the loading arm 36 and the wafer stage WST are overlapped in the vertical direction at and above the wafer loading position.

The wafer stage WST may be positioned. At this point, the wafer stage WST position is designed without considering the bristle alignment result offset. If the values are as specified, the position of the wafer stage WST at the time of measurement of a search alignment mark, which will be described later, may be adjusted based on the information on the X and Y errors.

At this time, as described later, the displacement amount of the search alignment mark in the external reference coordinate system of the wafer W, which is measured by the inline measuring instrument or offline measuring instrument , is canceled.

As described above, the position of the wafer stage WST may be further corrected. At this time, when the loading arm 36 reaches a predetermined position, the stage control device 19 releases the vacuum suction of the wafer W on the loading arm 36, and immediately after that, the stage controller 19 applies the wafer W by the center table 30 at an appropriate timing.

Start vacuum adsorption. At this time, the stage control device 19 releases the vacuum suction of the center table 30 when the center table 30 reaches a predetermined position, and starts the vacuum suction by the wafer holder 18 at an appropriate timing. The wafer W is sucked and held by Wafer W is held at a desired position on wafer stage WST. On the other hand, the closing arm 36 is raised to the original position. The right direction on the horizontal axis is the Z direction.

In the section including the position where the wafer W is transferred to the center table 30, that is, the section L 2, the loading arm 36 is lowered at a constant low speed in order to transfer the wafer W without being displaced. Furthermore, after the wafer W is sufficiently separated from the hook portion of the loading arm 36 and the wafer W is completely transferred to the center table 30, a section until a predetermined gap is formed between the wafer W and W, that is, the section L3 With respect to, the input arm 36 is lowered again while accelerating and decelerating.

By specifying the downward movement of the loading arm 36 in this manner, the wafer W can be accurately transferred from the loading arm 36 to the center table 30, and the transfer time can be shortened. Can do. As shown in Fig. Length of section L1-L3. Table 1 below shows the inputs. As for the closing arm vacuum-off position, a measurement tool is also provided to measure the position of the conventional force. By using this measurement tool, the optimal closing arm vacuum-off position can be measured.

Center table 30 In the section including the position where the wafer is transferred to the wafer holder 18, that is, in the section L5, the center table 30 is lowered at a constant low speed so that the wafer W can be transferred without causing a displacement.

The If the lowering operation of the center table 30 is defined in this way, the wafer W can be accurately transferred from the center table 30 to the wafer holder 18 and the transfer time can be shortened.

The length of sections L4 and L5 is determined by the set values of parameters P to P. Table 2 below shows the device parameters for the lowering operation of the center table Since the substance of the drive mechanism that lowers the center table 30 is a cam mechanism and a link mechanism that are driven by the rotation of the motor, the parameter names for the acceleration, speed, and deceleration of the center table 30 are shown in Table 2.

If these parameters are not adjusted properly, for example, the vacuum release between the loading arm 36 and the center table 30 will not be correct, and vibration will occur in the wafer and the wafer W. Input reproducibility may be bad. As described above, the wafer input parameter affects the reproducibility of the wafer W input. If it is determined in the wafer loading reproducibility measurement process of the present embodiment described later that the wafer loading reproducibility is bad, the wafer loading parameters should be adjusted in the subsequent maintenance.

Therefore, only these two parameters may be set to different values for each device, and other parameters may be set to values common to the devices. To do. Thereafter, the processing shifts to a wafer W alignment sequence. Move to. At this time, wafer stage WST moves in the opposite direction along the same path as when moving to the wafer loading position in the X direction by a predetermined distance from the standby position.

Here, Ueno and W will be described on the assumption that the exposure of the first layer has already been completed and the exposure of the second and subsequent layers is performed.

On wafer W, Fig. Upon receiving this instruction, the stage control device 19 moves the wafer stage WST via the wafer drive device When the wafer W is loaded, if the position of the wafer stage WST is not corrected based on the pre-alignment result X, Y error information , the wafer stage WST is The position may be corrected.

In addition, as described later, the wafer stage is adjusted so that the amount of displacement of the search mark in the external reference coordinate system of the wafer W measured by the inline measuring device or the offline measuring device is canceled.

The WST position may be further corrected. The imaging result is transmitted to the main controller 20, and the main controller 20 obtains the position X, Y of the search alignment mark SYM on the stage coordinate system XY coordinate system based on the imaging result. Pre-alignment results and in-line measurement. The position of the search alignment mark is determined by image processing using statistical methods, for example, a correlation algorithm such as template matching, or a waveform.

It would be desirable to be able to obtain it with high accuracy based on waveform processing algorithms such as slicing at different slice levels or extracting edges by differentiating the waveform.

An error message is displayed and the process is interrupted the operation of the exposure apparatus is stopped determination process after input. In this case, the operator refers to the error display, performs maintenance on the exposure apparatus , and investigates the cause of the error. At this time, for example, adjustment of the inclination of the background plate, the imaging optical system telecentricity, the position adjustment of the projection arm 36, and the like in the bria alignment device 32 may be performed.

In this case as well, the wafer stage WST position correction relative position adjustment between the alignment system ALG and the wafer W can be performed based on the result of bri-alignment and the pre-measurement result of the in-line measuring device described later. This imaging result is sent to the main controller Global alignment Fine alignment is performed S detection process.

Here, the X and Y positions of the wafer stage WST during wafer alignment are the measured values on the measurement axis in the Y-axis direction passing through the optical axis AX of the projection optical system PL and the detection center of the alignment system ALG, and the alignment system. It is managed based on the measured value on the measurement axis in the X-axis direction that passes through the ALG detection center! As long as the national laws of the designated country or selected selected country designated in this international application allow, the above publications and the corresponding disclosure in the US patent application or the disclosure in the US patent are incorporated herein.

However, since the height of the surface of the wafer W and the height of each measurement axis of the wafer laser interferometer 24 are different, the stage controller 19 uses the measured values of the multiple measurement axes as described above to determine the pitching amount and rolling. The amount is obtained, and based on these, the Abbe error in the vertical direction that occurs when the wafer holder 18 is tilted is corrected.

Exposure is performed by superimposing on the position S However, since the exposure apparatus performs scanning exposure, the wafer stage WST is moved at the center of the shot obtained by moving the wafer stage WST by the baseline from the position determined by the EGA during the actual exposure described later.

The position force of each is shifted to the scanning start position acceleration start position for exposure of each shot shifted in the scanning direction by a predetermined distance. Then, the measurement conditions for the edge of the wafer W in the bri alignment device 32 and the measurement conditions for the search alignment mark in the alignment system ALG are optimized based on the detection result at that time S Further, in the exposure apparatus , the wafer W is transferred from the wafer transfer arm 64 to the input arm 36 of the bri alignment apparatus 34 under the control of the stage controller 19 under the instruction of the main controller 20, and the wafer W is subjected to the optimized measurement conditions.

This edge is measured by the measurement units 40a, 40b, and 40c, and so-called pre-alignment is performed in which the center position and the rotation amount of the wafer W in the wafer external reference coordinate system based on the measurement result are calculated S Then, based on the calculation result, wafer stage WST and its sensor The wafer W is loaded on the wafer holder 18 of the wafer stage WST by the cooperative operation of the table 30 and the like S Furthermore, after the search alignment process S15 including the mark measurement under optimized measurement conditions, the fine alignment process S16 , etc.

Is transferred S There is. In the present embodiment, the target position of the wafer and stage WST at this time is the search alignment mark when the pre-alignment is based on the external reference coordinate system based on the center position and the rotation amount of the detected wafer W. As will be described later, the actual position coordinates of the search alignment mark in the external reference coordinate system of the wafer W measured in advance by the in-line measuring instrument V are taken into consideration.

Can be determined. That is, since the alignment system ALG is fixed, it is sufficient to shift the position of the wafer stage WST when loading the wafer W by the in-line measuring instrument by the above-mentioned positional deviation amount.

The image is sent to the imaging device via and the cooling device Then, in the developing device , a resist pattern image corresponding to the reticle device pattern is developed on each shot area of the wafer W.

S19 o The developed wafer W is a pattern formed as necessary. When an in-line measuring instrument or a separate measuring device for line width, overlay error, etc.

The resist is removed. By repeating the processes of S10 to S21 described above, for example, one lot of wafers stored in the wafer carrier are multiplexed. A circuit pattern is formed. However, it can be said that using the inline measuring device in the track for pre-measurement is advantageous in terms of throughput because pre-measurement can be performed immediately after the resist processing.

The exposure process control controller has various information for controlling the process for each lot or each wafer processed by the processing system in the storage device attached thereto, various parameters for that, or exposure history. Accumulate various information such as data. Based on these pieces of information, each exposure apparatus is controlled and managed so that each lot is appropriately processed.

In addition, the exposure process management controller is used for the alignment conditions used for alignment processing in each exposure apparatus various conditions used for alignment measurement number and arrangement of sample shots, multi-point method in a shot or one-point method.

The algorithm used in signal processing is obtained and registered in each exposure apparatus The exposure process management controller accumulates various data such as EGA log data measured by the exposure apparatus , and appropriately controls and manages each exposure apparatus based on these data.

Collect various data via network and conduct analysis. Is possible. This will be described with reference to FIG. In these five processes, several wafers W three in the same figure are processed in parallel.

Specifically, the pre-measurement process B of the wafer W is performed in parallel with the preceding wafer exposure process C, so that the influence on the overall throughput can be minimized.

By measuring in a pipeline with the instrument , there is no need to provide a separate resist dimension measuring device, so the cost of the device can be reduced and the throughput is not adversely affected. Of course. A series of processes including will be described in more detail.

First, as shown in FIG. In the next step , the in-line measuring instrument communicates with the exposure apparatus , the analysis system , or the in-plant production management host system to obtain the wafer size 12 inch Z8 inch Z6 of the wafer W to be exposed.

Inch , wafer type notch ZOF , wafer loading direction 6 o'clock Z3 o'clock , etc. Next, in step , the pre-measurement control device of the in-line measuring instrument determines the outer edge of the wafer W held on the turntable TT based on the acquired wafer outer shape measurement parameter usually, the notch ZOF portion.

Including 3 locations. For example, if the wafer outline measurement parameter is set to wafer W when the 8-inch Z-notch Z6 is set, as shown in Fig.

Image nearby. This calculation method is the same as the calculation method of the center position and rotation amount of the wafer W in the briaming apparatus 32 shown in the flowchart of FIG. Here, the external reference coordinate system of the wafer W is determined based on the obtained center position and rotation amount of the wafer W. The evaluation is performed according to a predetermined evaluation standard. Here, the evaluation is performed in a score format. The explanation of the edge detection result score will be described later.

That is, here, when the edge detection result score is better than a predetermined threshold, information OK indicating that the edge is appropriate as a mark to be measured by the exposure apparatus is displayed in the exposure apparatus. Sent to device or analysis system Also good. In principle, it is desirable to transmit the image data of all the edges measured by the inline measuring instrument to the exposure apparatus However, if the image data is transmitted for all edges, the communication time becomes long.

There is a risk that throughput may be reduced, and there is a burden that a storage medium having a large storage must be prepared on the receiving side of the imaging data. For this reason, in this embodiment, it is desirable to transmit imaging data only for edges that are determined to be inappropriate. Thereby, it is possible to determine whether or not the edge of the wafer W has been normally detected in the inline measuring instrument or the like based on the edge detection result score.

This step needs to be done at least before brialignment. If this judgment is affirmed, the process proceeds to step , and the detection result raw signal waveform data of the wafer W is sent, and if it is sent, the data is sent.

Based on at least one of the edge detection score as the evaluation result sent from the device , the wafer outline measurement parameter optimization process is executed. If raw edge waveform data has not been sent, the relevant data may be acquired from the inline measuring instrument and the optimization process may be executed based on that data.

If the determination in step is negative, that is, if there is no edge detection error, the process proceeds to step FIG. If the determination is negative, Proceed to step Fig. This step can be said to be a process of re-evaluating the detection result of the wafer edge as a result of the optimization, and determining whether or not the wafer can be normally exposed based on the evaluation result.

Post-optimization decision process. Specify the measurement parameters, notify the in-line measuring instrument , return to step in Fig. In step , if the number of rejected wafers W exceeds a preset number predetermined number , all the wafers W in the lot including the wafer W are rejected. In Step , the in-line measuring device preliminary measurement control device moves the stage device 1ST through the stage driving device , and the above-mentioned Step is performed.

In step , the position coordinates of the search alignment mark in the outer shape reference coordinate system are measured from the detection result, and the position coordinate force of the search alignment mark design in the outer shape reference coordinate system is determined from the measurement result. The amount of misalignment is calculated. In the present embodiment, this evaluation and score calculation are performed by the pre-measurement control device , but when all the pre-measurement results are transmitted to the analysis system and the exposure apparatus main control device May perform these evaluations and score calculations on the receiving side main controller 20 or analysis system The mark detection score will be described later.

In other words, if the mark detection result score is better than a predetermined threshold, the information indicating that the score and the mark are appropriate as a mark to be measured by the exposure apparatus OK If the score is worse than a predetermined threshold value, the score and the mark are not suitable as a mark to be measured by the exposure apparatus Send the indicated information NG.

If it is determined as defective, raw signal waveform data imaging data of the mark signal may be transmitted together with the score and NG information. In the present embodiment, the measured mark raw waveform signal data is transmitted only for the mark determined as inappropriate or the mark determined as impossible to measure measurement error mark.

Such information and information notified to the exposure apparatus from the in-line measuring instrument , which will be described later, may be notified to the exposure apparatus via the analysis system In the following description, it is assumed that the exposure apparatus is notified directly.

When information is sent to the exposure apparatus via the analysis system , a part or all of the processing performed by the exposure apparatus is performed by the analysis system and the result is sent to the exposure apparatus If the mark detection error is more than the set allowable number and the mark raw signal waveform data is sent, it is not sent based on the data In this case, in step , mark imaging data raw signal waveform data for all or part of the corresponding inline measuring instrument is acquired, and the mark detection parameter optimization process is executed.

The mark detection parameter optimization process may be performed by the preliminary measurement control device of the inline measuring instrument In step , if the mark detection error has not reached the set allowable number and the determination is negative, the process proceeds to wafer processing in step If the mark detection parameter is optimized, the process proceeds to step , where it is determined whether or not the power of searching for another mark is determined according to the information registered in advance.

Judgment is made according to the priority order set in advance in the design coordinate position. Here, another mark is selected from the candidates. If there is, the process proceeds to step and rejects the wafer W without transferring the wafer W into the exposure apparatus excluding the processing power. If the number of rejected wafers W exceeds the preset number in step , all wafers W in the lot including the wafer W are rejected.

Results of all prior measurements performed by the inline measuring instrument etc. The wafer processing in step corresponds to the processing in steps S 13 pre-alignment to S 17 exposure of the wafer process in FIG.

S13 , the edge of the wafer W is detected as described above, and the center position and the rotation amount of the wafer W are calculated based on the detection result. Then, in the wafer load S14 or the search alignment HS 15 , the wafer stage WST is equivalent to the amount of the displacement of the search alignment mark in the external reference coordinate system of the wafer W measured in advance by the in-line measuring instrument Adjust the position.

In this way, the relative position between wafer stage WST and alignment system ALG is adjusted by the amount of misalignment, so it depends on A. It is possible to avoid the occurrence of search measurement errors due to the displacement of the search alignment mark in the outline reference coordinate system. An example of a misaligned search alignment mark is shown.

If the certainty of reliably capturing the search alignment mark within the measurement field of view is increased in this way, the imaging magnification of the alignment ALG can be set higher.

In this way, the search alignment mark detection accuracy can be further increased. Therefore, for example, even with a wafer W that can be measured without problems in the exposure apparatus , A situation where the in-line measuring instrument cannot measure normally must be avoided. Therefore, it is necessary to ensure consistency between the preliminary measurement by the in-line measuring instrument and the main measurement by the exposure apparatus In the present embodiment, various detection scores as evaluation results for the detection result by the pre-measurement sensor in the pre-measurement process by the in-line measuring device and evaluation for the detection result of the edge of the wafer W in the measurement process by the exposure apparatus The resulting various detection scores are compared to match both scores for the same wafer for example, a reference wafer matching step.

And including differences in signal processing algorithms. Keep it. Then, the imaging data, the wafer outline measurement parameter, and the detection error information are transmitted to the analysis system or the inline measuring instrument , and the edge imaging data raw signal waveform data measured by the inline measuring instrument and the exposure apparatus are transmitted.

The edge raw signal waveform data for the same edge by the pre-alignment devices 32 measurement units 40a to 40c are collated, and the score correction value is optimized so as to match the score based on the measurement result of the inline measuring instrument In this way, for the same wafer W, the score for the imaging data of at least a part of the edge of the wafer in the pre-measurement step and the score for the imaging data of the same edge in the bri alignment can be made to substantially coincide.

Effective pre-measurement becomes possible. Based on the measurement result of the inline measuring instrument , the waveform data of the mark sent from the inline measuring instrument is compared with the detection result mark raw signal waveform data for the same mark by the exposure device alignment ALG. The score correction value is optimized so that the scores match.

Normally, the main controller 20 of the exposure apparatus detects at least a mark in which a detection error has occurred in the alignment process. The result of detection mark raw signal waveform data is stored in the storage device 21, so the mark raw signal waveform data, detection parameters, and detection error information are analyzed by the analysis system or in-line measurement.

The score correction value may be optimized so that the detection score for the same mark matches by sending it to the instrument and comparing it with the mark raw signal waveform data measured by the in-line instrument The difference in characteristics between the sensors can be similarly performed. This edge detection result score is obtained according to the predetermined evaluation criteria as described above in step see FIG.

In the present embodiment, a plurality of feature amounts relating to the detection state of the edge of the wafer W including the notch portion in the imaging data of the edge of the wafer W are used as a predetermined evaluation criterion. This feature amount is an amount that serves as a measure of whether or not the feature of the edge of the wafer has been accurately detected. For example, the feature value related to the edge of the wafer is the intensity of the wafer edge pattern that is, the contrast between the bright and dark areas near the edge , the variation in the intensity of the wafer edge pattern, and the imaging data.

Examples include the curvature of the edge of the wafer to be obtained and the variation between the approximate curve approximate straight line is acceptable obtained by approximating the edge of the wafer and multiple edge positions. In the present embodiment, after obtaining the plurality of feature amounts, the weighting optimized for each feature amount is performed, and the total value obtained by summing is defined as an edge detection result score as an evaluation result.

This mark detection result is also obtained according to a predetermined evaluation standard. The feature value in the mark signal pattern After obtaining a number of features such as a certain mark pattern width error, mark pattern edge spacing error, mark pattern edge strength, and variations in mark pattern edge strength for each pattern, weighting is optimized for each feature amount The total value obtained from the above is defined as the mark detection result score, and compared with a preset threshold value to determine the presence or absence of a mark without Z.

Hope to do. The boundary with the non-pattern part. At this time, it is also possible to perform processing to remove noise contained in the signal. In this case, the lower the score, the better. Conversely, when using a so-called correlation algorithm that performs template matching between a detected waveform and a reference waveform for mark detection, this correlation value can be used as a score.

In this case, the higher the score, the better. In this embodiment, the input reproducibility measurement of the wafer W to the exposure apparatus is performed during lot processing. That is, in this embodiment, each time a wafer W is loaded into the exposure apparatus according to the flowchart of FIG. Do it. Ie, measurement is performed between the loading of wafer W at S14 and the unloading of wafer W at S However, even for wafers W in the same lot, the outer shape of the wafer and the mark state on the wafer W the formed marks are incomplete, unsatisfactory disintegration , and how the mark position varies due to wafer deformation, etc.

Compared to the case where the input wafer is input multiple times and the input repeatability is measured, factors other than the input repeatability of the exposure apparatus are the measurement results of the mark position on the wafer W. If Case Air compatibility is shown below, Air Direct users will have access to use the Air Remote Mobile App High-quality photos taken with Nikon camera can be shared via e-mail or social media just as you would photos taken with your smart device.

Simple interface, great camera control, satisfied clients at my end. There are multiple Release mode options on your Nikon D, some even allowing remote-control access.

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The camera is very user friendly. Operation by wired and wireless LAN when using a wireless transmitter. At this setting, the shutter opens when the shutter-release button on the optional ML-L3 remote control is pressed 2 seconds after the button is pressed in delayed remote mode and remains open until the remote-control shutter-release button is pressed a Remote control of most Nikon digital SLR settings, including exposure mode, shutter speed and aperture, from a computer.

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I've got a Nikon D, no flip out screen. Ideal for subjects that are difficult to approach or for minimizing vibrations. Learn how to use them to your advantage with our tips and tricks. For those using Android 8 or later OS When the location synchronization function is on, SnapBridge notifications are now displayed at all times due to the change of User Experience Policy on Android. Nikon D vs. Just flip the Live View lever and press the record Nikon D's Bluetooth connectivity feature comes handy for connecting other devices that supports it.

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Features: Slim wireless remote allows you to trigger the shutter remotely to prevent camera movement when using slower shutter speeds. Officially, the only way to remotely trigger your D is with the Snapbridge app. You can even use the smartphone or tablet to remotely trigger the D to take pictures. Air Direct Wireless Tethering System is ideal for camera control and image transfer using a Mac or Windows computer and your tethering software of choice.

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Angle adjustment for the saddle surface. Fig 8. Optimized tool path with minimum error of thesaddle surface. T'' 2! R8 C"! F-' 1-! H ZZZK8! Aggregate Production Planning APP aims todetermine the production quantity and inventory levelin an aggregate term, for example, tons, dollars, orproduction hours. Data in the APP is usually monthlydata. The APP is greatly needed when the demand ishighly seasonal. Master Production Scheduling MPS aims todetermine production quantities and timing of individual end products.

The planning horizon of theMPS should be longer than the cumulative productionand purchasing lead-time. Theschedule is obtained by applying MRP logic or the pullsystem of Kanban. This paper is organized as follows. Some possible ways to solve or alleviate the problemsare also proposed.

The complexity of the production system is dividedinto 2 types, namely simple and complex. Thecomplex one is characterized by a long setup time andlarge production lot size, wide variety of products,complicated products having many components, andinsufficient production capacity of some processes. The simple one is the opposite. Therefore, the companies should tryto simplify the production process, e.

This result corresponds to that of Krajewski et al. This indicates that the perception of persons in the company is also important. This perception should be altered. APP is not performed explicitly andsystematically. APP is performed without explicitlyconsidering related costs, such as, regular andovertime labor costs of permanent andtemporary workers, inventory holding cost, andsubcontracting cost.

The current policy of varying the productionrate and keeping inventory results in relativelyhigh total costs. This means that the currentpolicy is not optimal. Mathematical models for determining anoptimal APP are not applied.

Techawiboonwong and Yenradee developed aspreadsheet APP model based on general requirementsand constraints of Thai industries. The model candetermine the optimal way of varying production rateand keeping inventory that minimizes total relatedcosts. Most participants feel that the model can beapplied to the real cases of their companies if it isslightly modified.

The mathematical model has not been appliedfor disaggregating the APP into the MPSconsidering related objective and constraints. Some well-known models, such as that of Haxand Meal , Bitran et al. The author and his doctoral student are studying theseproblems and developing a workable model andmethod for determining the MPS based on the APP forThai industries.

Receiving customer orders. Issuing invoices and updating stock of finished products. Issuing purchase orders of raw materials andcomponents of the products. Receiving the purchased items based on thepurchase orders and updating stock of thepurchased parts.

Issuing work orders for manufactured itemsand printing pick-lists for withdrawing therequired parts into the work orders. Withdrawing the required parts into the workorders. They will become the work-inprocess. Completing the work orders to receive themanufactured parts to stock and reducing thework-in-process level of the required parts.

Checking stock of all items and makingadjustments if it is necessary. If any inventory transaction stated above is notperformed correctly, accurately, and promptly, the data of the system will be inaccurate. When the data isinaccurate, the MRP-II system will not be able togenerate reliable detailed production and purchasingschedules. Even if the data of the MRP-II system is accurate, itmay not be able to generate reliable production andpurchasing schedules because of the following reasons.

The MRP-II system assumes a fixedproduction lead-time regardless of the lotsize, load on work centers, and priority of jobs. Alternative routings are notconsidered. Thus, heavy load on the firstpriority machine cannot be allocated to thesecond priority machine with light load. The detailed production plan is generated byassuming that all work centers have unlimitedcapacity. Capacity checking is performedafterward.

When a capacity problem isevident, the planner has to solve it manually. There is no automatic routine to avoid thecapacity problem. There are many attempts to modify the MRP-II systemto be able to generate reliable production andpurchasing schedules by considering finite capacity of key work centers.

Pandey, Yenradee, andArchariyapruek developed a finite capacitymaterial requirement planning FCMRP algorithm,which is executed in two stages. First, capacity-basedproduction schedules are generated from the input data. Second, the algorithm produces an appropriate materialrequirement plan to satisfy the schedules obtained fromstage 1. The author and a doctoral student are developing analgorithm and a computer program to determine theproduction and purchasing schedules considering finitecapacity of the bottleneck work centers based on theschedules generated by the MRP system.

Alternateproduction routes are considered and some jobs on thebottleneck may be allocated to the non-bottlenecks. The excessive loads in some periods on the bottleneckwill be shifted earlier or later to smooth the loads.

Finally, the production schedules of other items, whichare not produced on the bottleneck, and the purchasingschedules of all items will be revised accordingly. Thisprogram is appropriate for small industries facingcapacity problems.

Following a wrong implementationsequence not only results in excessive implementationtime but also degrades the performance e. Then, overall direct andindirect relationships are determined using a procedurebased on max-min fuzzy composition. Those in Sector 4 can beimplemented at any time see Table 2. The techniquesin Sector 4 can be implemented when it is convenient any time.

This will make the implementation processprogress smoothly since the techniques that support orare prerequisite of others have been alreadyimplemented. Some mathematical models for this purpose should bedeveloped by considering real objective and constraints of Thai industries. The MRP-II system has a drawback in that it cannotgenerate reliable production and purchasing scheduleswhen a bottleneck exists in a shop floor.

From the fact that industries withdifferent requirements may need different FCMRPalgorithms, the FCMRP system with a number of selectable algorithms should be developed based onvarious requirements of Thai industries.

It requires someengineering and management techniques to help solvethe problems. Practical methods for solving theproblems should be further developed based on thecontext of Thai industries, which is sometimesdifferent from that of other developed countries.

ReferencesBartezzaghi, E. Bitran, G. Fearon, H. New York: West Publishing. Hax, A. HierarchicalIntegration of Production Planning and Scheduling. North Holland, Amsterdam. Hernandez, A. Hobbs, O. Krajewski, L. Pandey, P. Schonberger, R. Stein, R. New York: Marcel Dekker. Techawiboonwong, A. Voss, C. Wantuck, K. Yenradee, P. Harrison, A. As examples, two promising nature-inspired techniques are selected for detailed discussion.

In the paper, the concepts of the two techniques,which are inspired by real models in nature, are carefully explained and the implementations of the techniques forstructural design problems are also described. The discussion on the efficiency of both techniques is included as well.

IntroductionMain concerns of structural engineers when designingstructures include, among others, performance of thestructures as well as cost to construct them. Gooddesigners are actually those who can balance these twoconcerns properly.

Giving too much weight on the costmay result in unsafe structures while too much concernon the performance may yield expensive structures. Thispredicament can be resolved by employing optimization. Unfortunately, this is something that is easy to say butdifficult to do. The biggest obstacle to achieve theoptimal structural design is the nature of the structuraldesign process itself.

For example, to be able to designmembers of a steel structure, it is necessary to know theinternal forces of each member of the structure. Once theinternal forces of the members are known, the sectionalsizes and other details of the members can be computed. However, to be able to obtain the internal forces, priorknowledge of the sizes of the members is required. Tocircumvent this dilemma, the designer will have toestimate the sizes of the members first.

After that, theassumed sizes will be used for the calculation of theinternal forces. The obtained internal forces will in turnbe used to calculate the appropriate sizes of the members.

The process can be repeated until a satisfactory result isobtained. Although this technique can be considered asan optimization technique, it is a crude iterative process,which does not guarantee convergence and can be veryinefficient.

The first estimation of the sectional sizes isthe most crucial step and it usually requires a great deal of experience. If the first estimation is far from theoptimal solution, there is a high possibility that the finalresult will be poor. Even though it is obvious that the popular process of structural design mentioned above is not efficient andthere are quite a number of other long-establishedoptimization techniques see, for example, Rao ,these techniques are rarely used in the design process.

The main reason is that these conventional techniquesare generally gradient-based optimization techniques. The gradient-based optimization originates from aproblem of finding the conditions that give themaximum or minimum value of a function in calculus.

The concept is simple; the gradients of a function with To find theminimum, the search in each step is directed into adirection that has the strongest negative gradient until aminimum point where the gradients vanish is found.

T of ind the maximum, a direction with the strongest positivegradient can be considered, instead. Since the gradientbasedtechniques employ calculus, they are generallysuitable for problems with continuous design variables.

Unfortunately, structural optimization problems dealmostly with discrete design variables. For example, if thedesign variables are sectional sizes, only those sizesavailable in the market can be selected. Another problemwith the gradient-based techniques is that they search fora local optimum solution, not the global one.

Thisdeficiency in the gradient-based techniques is inheritedfrom calculus. Vanishing of the gradients is in fact anecessary condition for local extremums but it isdefinitely not a sufficient condition for the globalextremum.

As a result, the quality of the obtained resultcan very much depend on the starting search point. If thestarting point is close to a local optimum, the search maybe trapped in that local optimum.

Recently, several new optimization techniques that arenot based on calculus have been proposed. Many of these techniques are inspired by nature. The simulated annealing is inspired bythermal annealing of critically heated solids. The antcolony optimization mimics the foraging behavior of antcolonies in the real world. The inspirations of thesetechniques are in a sense optimization processesperformed by nature. With the current rapid advances inthe field of computer science, many natural behaviorshave been artificially simulated by computers.

Theoriginal aim of the simulations is either to understandthese natural behaviors better or to actually inventartificial lives. The nature-inspired optimizationtechniques are, to a certain degree, byproducts of theseresearches. In the optimization field, these techniques aregaining popularity over the calculus-based techniquesdue to the fact that they are generally more robust thanthe calculus-based techniques, meaning that theirperformances are nearly problem-independent.

Also,these techniques are more efficient than the calculusbasedtechniques in searching for the global optimum. Inaddition, they are suitable for problems with discretevariables; consequently, they are also suitable forstructural design optimization problems. This paper discusses two of these nature-inspiredoptimization techniques, i.

The paper aims to demonstratethe potential of nature-inspired optimization techniquesin solving structural design optimization problems. These two techniques are selected because of theirstriking resemblance to their corresponding naturalmodels as well as their promising capability inoptimization.

In this paper, only structural designapplications will be discussed. Genetic Algorithms for StructuralDesign OptimizationGenetic algorithms GAs are global probabilistic searchalgorithms inspired by the survival- of -the-fittest theory Goldberg The algorithms have receivedconsiderable attention because of their versatileapplication in several fields Grefenstette , Goldberg, Deb , Marcelin et al.

GAs start their search from many points in search spaceat the same time. These starting points are usuallyselected randomly and known as the initial population. Through the consideration of fitness values of thesesearch points, which are given based on their merit, andthe randomized information exchange among the points,a new set of search points with higher merit is created.

The process is then repeated until a satisfactory result isobtained. Since the technique utilizes information frommany search points at the same time, there is less chancefor the search to be trapped in any of local optimalpoints.

Another distinguishing characteristic of GAs isthat the algorithms do not directly work with designvariables. Rather, they work with codes that represent thevariables. Generally, binary coding is used. Because of the discrete nature of coding, GAs are the perfect choicefor those problems with discrete variables.

To understand the concept of GAs in structural designoptimization, it is advisable to begin with coding. Asalready mentioned, GAs employ codes to representdesign variables and consequently to represent designsolutions, which are in fact combinations of designvariables. Consider a truss structure shown in Figure 1. The structure has three members. Assume that the section of each member is to be selected from a list of fouravailable sections, i. Since there are four available sections, a two-bitbinary string can be used to code them; i.

To begin the calculation, the initial population will haveto be created at random. In the truss example shown inFigure 1, several six-bit binary strings can be randomlycreated to form the first population. Each six-bit stringrepresents one truss structure and is called an individualin the GA terminology.

To be able to use the survival- of the-fittesttheory, it is necessary to define the fitness of each individual or each design. This can be done bytaking into account the admissibility and the objective For example, in sizingoptimization, designs with higher fitness are those thatsatisfy all design criteria and have small weight. With themeasure of fitness specified, the survival- of -the-fittesttheory can then be applied and the evolution process canbe artificially created.

After the first population is randomly obtained, thefollowing three basic operators, i. Thereproduction operator defines a process in whichindividuals are selected, based on their fitness, formating and subsequent genetic actions. Individuals withhigher fitness will have higher chance to be selected intothis mating pool.

Consequently, highly fit individualscan reproduce and pass their genes to the next generationwhile less fit individuals simply disappear. Afterreproduction, the crossover operator is implemented. Inthe crossover operator, new individuals are created byexchanging information among the individuals in themating pool generated by the reproduction operator.

There are many crossover operators available in theliterature Goldberg , Jenkins , Camp et al. Nevertheless, in most crossover operators, twoindividuals are selected at random from the mating pooland some portions of their strings are exchanged. Thetwo original strings are called parent strings and the tworesulting strings are called child strings. Figure 2 showsan example of one of the simplest crossover operators,namely the one-point crossover. In this type of crossover, a crossing site is randomly selected and allbits on one side of the selected site of the two strings areswapped to create two child strings.

It is clear thatcrossover may yield better or worse child strings. To beable to adjust the degree of uncertainty of the crossoverphase, it is not necessary to use all individuals in themating pool in the operator. This is done by adjusting theprobability that crossover is performed crossoverprobability. The last GA operator is the mutationoperator. The operator is usedsparingly with a small probability mutation probability.

Mutation allows for the possibility that features that donot exist in both parent strings may be created andpassed to their children. After reproduction, crossover,and mutation, a new generation is obtained.

The processis repeated until a satisfactory result is achieved. Conceptually, reproduction exploits available goodstrings while crossover and mutation explore newstrings. Exploitation and exploration must be kept inbalance in order to obtain good results.

The success of hiswork became the turning point in the field of structuraldesign optimization. Many studies have been done toimprove the efficiency of GAs for structural designoptimization problems for example, Jenkins ,Rajeev and Krishnamoorthy , Camp et al.

The application of GAs to solve relatively large structural systems has alsobeen explored Rajan , Galante GAs havebeen well accepted among researchers as a high-qualitytechnique that is not difficult to implement and use. Ant Colony Optimization for StructuralDesign OptimizationAnt colonies can collectively perform complicated taskseven with a low intelligent level of each individual ant. One of the examples is the foraging behavior of ants. Anant colony is capable of finding the shortest path betweenits nest and a food source without using visual clues.

Thiscapability of the colony is achieved by indirectcommunication between ants via the use of pheromone. Itis well-known that ants lay and follow pheromone trails. These simple trail-laying and trail-following mechanismsenable the colony to seek out the shortest path from allpaths that the ants in the colony have ever explored. Consider a colony of ants shown in Figure 3. At the beginning, theants will select the two paths with equal probability,meaning that there will be approximately half of the antsselecting each path.

Since the shorter path requires lesstime to complete, for the same amount of time, the antson the shorter path will be able to complete more rounds. As a result, the quantity of pheromone on the shorter pathgrows faster than on the longer one. Finally, the levels of pheromone on the two paths will be so different thatvirtually all ants will select the shorter one. C NestFoodFig. B Pheromone isdeposited faster on the shorter path. More ants select theshorter path.

C Finally, all ants select the shorter path. It is important to note that pheromone trails establishedby ants do not last forever but rather they evaporate. Thispheromone evaporation is also an important mechanismsince it avoids too rapid a convergence towards a suboptimalpath.

The three aforementioned mechanisms, i. Recently, the ACO technique is becoming popularamong researchers in the field of heuristic optimization see, for example, a survey in Dorigo et al. Theproblem that seems to fit the technique naturally is thetraveling salesman problem Dorigo and Gambardella Nevertheless, the technique has been applied tovarious types of problem, such as the quadraticassignment problem Talbi et al.

The application of thetechnique in the field of civil engineering is still rare see,for example, Abbaspour et al. The ACO technique has been developed forcombinatorial optimization problems. Most of practicalstructural design optimization problems consider onlysizing optimization, which is basically combinatorialoptimization. As a result, the ACO technique can beapplied to solve them. To this end, structural designoptimization problems under consideration have to beprepared in such a suitable way that the problems fit theACO technique.

After that, a simple ACO algorithm canbe implemented. To understand the concept of the ACOfor structural design optimization, consider in Figure This sizing optimization problem is acombinatorial optimization problem and can be thought of as a foraging problem of an ant colony. As shown inFigure 4, an artificial nest and a food source can beestablished. In the figure, node 1 represents the nest andnode 4 represents the food source. The ants will have tomove from node 1 to node 4 by passing all other nodes inbetween.

Between each pair of nodes, there are fouravailable sub-paths, representing four different availablesections for each design variable. The partial walk of theants between nodes 1 and 2 represents the selection forthe design variable A 1 , and the partial walks between thesubsequent nodes are for the subsequent design variables.

For the ACO to work, artificial ants will have to makemany artificial tours and they must obey the followingsimple rules; i. In other words, paths withhigher pheromone level will have a higher chance tobe selected by ants. If the path is of high quality, the ant that haswalked the path will lay a large amount of pheromone on the path. For structural designoptimization, a path is considered high quality if itrepresents an admissible structure with low weight. These two rules, though simple, are enough for thecolony to perform its task.

It can be seen that the concept of the ACO is very simpleand its implementation is straightforward. Theperformance of the technique has been found to becomparable with that of GAs when used in structuraldesign optimization. Even though the technique is still inits infancy, its potential is apparent. As the application of the technique in the field of civil engineering, not tomention the field of structural engineering, is still rare, alot of improvements are to be expected.

Concluding RemarksThis paper demonstrates the potential of nature-inspiredoptimization techniques in solving structural designoptimization problems. Two promising nature-inspiredtechniques are discussed; they are genetic algorithms GAs and the ant colony optimization ACO. When GAs areused in structural design optimization, a population of structures is created and an evolution process under thesurvival- of -the-fittest rule is applied to the population.

The evolution process is controlled by threeoperators, i. GAshave been used successfully in many different fields,including structural design.

Compared with GAs, theACO is a newer technique and has not been used much inthe field of civil engineering. The ACO is inspired by theway ant colonies function in the real world. An antcolony is capable of finding the shortest path between itsnest and a food source by the use of pheromone.

In fact,the task of finding the shortest path is achieved by usingthree basic mechanisms, i. These three mechanisms constitute the ACO. When theACO is applied to structural design optimization, theoptimization problem under consideration is transformedinto a foraging problem of an ant colony.

A betterdesign will be made to be equivalent to a shorter route. Nevertheless, thetechnique is still new and much progress can beexpected. Recommendations for Future Researchand DevelopmentThe two natural-inspired optimization techniquesdiscussed in this paper, i. Their searches are considered global because bothtechniques consider many search points at the same time.

This will naturally allow the search to avoid beingtrapped in any local optimal points. If the advantage of existing local search algorithms in fine-tuning localoptimal solutions is combined with the global searchcapability of these two techniques, even better resultsmay be expected.

This idea can possibly be implementedby using genetic algorithms or the ant colonyoptimization to globally search for the neighborhood of the global optimal solution first.

After that, a local searchalgorithm can be used to improve the solution obtainedfrom the global search. ReferencesAbbaspour, K. Schulin, and M. Estimating unsaturated soilhydraulic parameters using ant colony optimization. Advances in Water Resources, Vol. Camp, C.

Pezeshk, and G. Optimizeddesign of two-dimensional structures using a geneticalgorithm. Journal of Structural Engineering, Vol. Dawid, H. Adaptive learning by geneticalgorithms: analytical results and applications toeconomic models. New York: Springer. Deb, K. Optimization for engineering design:algorithms and examples. Dorigo, M. Ant coloniesfor the travelling salesman problem. BioSystems, Vol. Caro, and L. Ant algorithms for discrete optimization.

ArtificialLife, Vol. Galante, M. Genetic algorithms as an approach tooptimize real-world trusses. Goldberg, D. Genetic algorithms in search,optimization, and machine learning. Reading,Massachusetts: Addison-Wesley. Grefenstette, J. Optimization of controlparameters for genetic algorithms. Jayaraman, V. Kulkarni, S.

Karale, and P. Ant colony framework for optimaldesign and scheduling of batch plants. Computers andChemical Engineering, Vol.

Jenkins, W. On the application of naturalalgorithms to structural design optimization. Engineering Structures, Vol. Marcelin, J. Trompette, and R.

Optimization of composite beam structures using agenetic algorithm. Structural Optimization, Vol. McMullen, P. An ant colony optimizationapproach to addressing a JIT sequencing problemwith multiple objectives.

Artificial Intelligence inEngineering, Vol. Nanakorn, P. An adaptivepenalty function in genetic algorithms for structuraldesign optimization. Computers and Structures, Vol. Rajan, S. Sizing, shape, and topology designoptimization of trusses using genetic algorithm.

Rajeev, S. Discreteoptimization of structures using genetic algorithms. Rao, S. Engineering optimization: theory andpractice. Talbi, E. Roux, C. Fonlupt, and D. Parallel ant colonies for the quadraticassignment problem. Future Generation ComputerSystems, Vol. QR0 S:! J0 -0?! I,J '!

LO; Z M!! L:" ,0! L ;IM! Z BW:! O,H, H,?! P' 77 9- D! BW BB?! F C! F C K VVVL! K VVbL! A D K VVgL! K VV"L! B 'B'7. B 'F1. L' QB,. B ';,6. T0X9 T0X! However, such a strategy invokes a substantialincrease of the CL-points and consequently asubstantial increase of the machining time. Therefore,recent papers have displayed a number of sophisticatedmethods to optimize a zigzag or spiral patterncombined with techniques dealing with the geometriccomplexity of the workpiece, for instance, Ahmadi andMatsuo , Altan et.

Besides, there exists a variety of of f-linemethods to generate a suitable non-uniform tool-path,for instance: the neural network modeling approach Suh and Shin, and the Voronoi diagramtechnique Jeong and Kim, However, a robustalgorithm to generate such complicated patterns is stillan open problem. Preliminary ExamplesGrid generation techniques are surprisingly welladaptedto tool-path optimizations, containing almostall the main ingredients of the tool-path planning.

Thekinematics equations imply that the deviation of thetool from the linear trajectory increases with thevariation of the rotation angles.

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Having maximum number of vanishing moment[2]: This property plays a significant role ingenerating the wavelet bases with maximalsmoothness. It is also linked to the flatness of passband of the filters in the filter bank. Nonseparable: It is favorable to utilize nonseparablefilters to form the filter bank since it givesoverall better performance due to its better adaptationto the human visual system and highernumbers of degrees of freedom in the design.

Notethat a n-D filter is separable if its wavenumberresponse satisfiesH z 1 ,z 2 ,.. In the remaining of this section, a summary of thecurrent progress in the area of filter bank design ispresented in the following two main categories Finite Impulse Response FIR For a pair of FIR synthesis and analysis filter banks tosatisfy a perfect reconstruction property, the polyphasematrix associated with the synthesis and thus, alsoanalysis filter banks must be unimodular, or in moregeneral algebraic terms, they must be units in the ring.

The problem of parameterizing the class of FIR filterbanks with perfect reconstruction property can beequivalently reformulated as the unimodular matrixcompletion problem for multivariate polynomial matrix[21], [25], [24]. Although, the problem is satisfactorily solved for themultiband 1-D case in [3] and the two-band multidimensionalcase in [ 10 ], the more general problemassociated with the multidimensional multibandcounterpart is still open. The linear phase property is, however, not dealt with inthe design since it is almost impossible to obtain.

In thetrade of f with linear phase property, IIR filter generallyhas fewer coefficients and thus computationallyefficient implementation is obtainable. Similar to the FIR case, an m-band n-D PR IIR analysis synthesis filter bank can be described by an m x mpolyphase matrix H z whose elements belong to thering of n-variate rational function K z 1, z 2, … z n. Forexample for a two-band 3-dimensional IIR filter bank,the analysis polyphase matrix may take the formwhere h ij z 1, z 2, z 3 's are rational functions in z 1, z 2, z 3.

Let S be the set of all structurally stable properrational functions in reduced form i. Therefore, structural stability impliesbounded-input bounded-output BIBO stability but notvice versa [5]. This set S is known to form acommutative ring [2]. Definition 2: A minimum-phase rational function is astable rational function with a stable inverse. In the context of PR subband coding, the framework of IIR filter bank design then reduces to the construction of a polyphase matrix, associated with the analysisfilter bank, whose determinant is a minimum-phaserational function [2].

Usually the first row of theanalysis polyphase matrix is obtained by computing thepolyphase representation of a low-pass nonseparablemultidimensional filter which can be designed using astandard method as shown in [18]. Toillustrate this, let express the polyphase matrix aswhere n 00 z , d 00 z , n 01 z , d 01 z are known. By Hilbert's Nullstellensatz, it follows that there existsan integer N and polynomials p z 1, z 2 andq z 1, z 2 such that Eq. Whenthis is not the case, a constructive procedure forobtaining the n-variate counterpart of the right-handside of Eq.

Open ProblemsIn this section three open problems are summarizedand a brief description is also given. However, other special case may also beconsidered. Multidimensional Multiband FIR Filter Bank DesignGiven the first column-symmetric row of the mxmmultivariate polynomial matrix H z , whose elementsare zero-coprime, find the algorithm to complete theremaining m - 1 row of the matrix H z such that1.

The designs of the multidimensional filter bank areinherently linked to the coprimeness and its variants of multivariate polynomial matrix. For the n-D FIR filterbank design, the problem can be increasinglycomplicated when the linear phase constraint is posed. Although a numerical method has recently beenproposed to remedy this problem, the algebraicallyprecise constructive method is still rather unexplored.

For an application where the linear phase property isnot strictly enforced, the n-D IIR filter bank can beutilized instead. References[1] W. Adams and P. Basu and H. Reidel Publishing Co. Bose, C. Bose and C. Bose, ed.

Charoenlarpnopparut and N. Cox, J. Little and D. IT, Nov. Guiver and N. CAS, no. Johannesson and Z. Information Theory, vol. Logar and B. Mahapakulchai and R. IEEE Inter. ICC , June Massey and T. Park and C. Vetteri and J. Xu, J. Ying and O. Youla and G. Circuits andSystems, vol. Deregulation of the power industryhas made power quality a distinguishing feature of distribution service.

The distribution automation system DAS is thekey to address all these challenges to improve the operation of the distribution system and the quality of supply. Distribution Automation DA aims at conservation of energy, including reduction of consumption and losses in thedistribution and transmission circuits, reduction of peak load, improvement in the reliability and quality of service,deferral of new construction, and recovery of lost revenue.

Inorder to perform the desired functions of distribution management system DMS , a data acquisition system fordistribution networks is needed similar to the real time supervisory control and data acquisition system SCADA of energy management system EMS used for transmission networks. Unmanned substations have become an importantpart of distribution systems in many countries.

DAS is essential for monitoring, control and efficient operation of thedistribution networks. Keywords: Distribution automation system, Deregulation, Load flow, Reactive power compensation, Networkreconfiguration, State estimation, Network observability, Bad data processing, Power Quality.

IntroductionReal power losses in distribution systems in general arequite appreciable, constituting a major portion of theoverall power system losses. Power distribution systemsespecially in developing countries are characterized byincreased power losses, poor voltage pr of ile, inadequatemetering, frequent failure of major equipment mainly dueto non-availability of their loading information and lack of proper monitoring and co-ordinated controls.

Distribution networks have been enormously extended,probably with less attention paid to the optimum growth,to meet the rapidly growing demand. Distributionautomation DA is getting worldwide attention toovercome these problems.

Some of the DA application functions are distributionpower flow, distribution state estimation, distributionshort circuit analysis, distribution fault location,distribution feeder reconfiguration, and distributionfeeder voltage and var control.

This is because the basicstructure of the EMS on-line power flow and stateestimation is based on the assumptions which are notgenerally valid for distribution systems. The transmissionsystems are generally assumed to operate under balancedthree-phase conditions, and the network is a symmetricalthree-phase system that is fully described with its positivesequence network. The distribution systems alsorequire information of all the phasor quantities, as thesystem is generally unbalanced.

A Literature Review of DistributionAutomationThe past few years have seen an increasing interest inDA with the hope that automation will ultimately leadto a more efficient and economic operation of distribution circuits Narendranath et al. Rapid growth in industrialization and irrigation needshave resulted in an enormous increase in the demand forelectricity. All the three constituents of the power system,generation, transmission and distribution have increasedin size and complexity.

Generation and transmission haveexpanded in a relatively planned manner using moderntechnology. Expansion of the distribution system, on theother hand, has been mostly in an unplanned way indeveloping countries. These are interconnected in a hierarchicalmanner by a suitable communication network. Acomputer at the master control station is typically at the RTUs are interfaced to sensors,meters and control switches to monitor and control thedifferent equipment. RTUs at different levels may havedifferent capabilities depending on the need and cost.

DAS must meet the needs of the customers and of thedistribution, transmission, and generation systems of thepower utility. Modern computer-aided DASs canimprove the operation of distribution system and thequality of supply. This provides direct benefit not only topower utilities and consumers, but also has a directimpact on the national economy Gupta et al.

Advanced applications fall into two main categories,1. Applications that provide recommendations forimproving the reliability, security, and efficiency of the current and future operating conditions.

Advanced applications that are designed to define andanalyze the current operating conditions are Distribution state load estimation, whichestimates the current operating status of adistribution feeder by using limited real-timesupervisory control and data acquisition SCADA data and historical load data.

Distribution power flow, which analyzes what-ifscenario studies, based on real-time saved cases. Distribution short circuit analysis, which calculatesthree-phase, unbalanced fault currents. Distribution fault location, which determinespossible fault locations, based on the fault sensorsignals and fuzzy logic algorithm. Advanced applications that are designed to makerecommendations to improve the reliability, security,and the efficiency of the current operating conditionsare Distribution feeder voltage and var control, whichrecommends optimal tap positions and capacitorstatus to minimize losses and improve voltagepr of ile Shirmohammadi et al.

Electric power quality can be defined as a measure of how well electric power services can be utilized bycustomers. Losses in the transmission and distributionsystem have come under greater scrutiny in recentyears, and certain types of power quality degradationresults in losses.

A number of mitigation methods areavailable. Improving the performance of the powersystem by introducing automation reduces theoccurrence of faults and interruption and improvespower quality IEEE power engineering review, The development of new DA applications isconsiderably wide spread nowadays.

In this research,many components of DA application functions alongwith their interactions with one another have beendeveloped using C programming language. MethodologyA distribution automation system DAS aims for bettermanagement and control of the distribution networks. Anew and robust algorithm for network configuration,power flow analysis, reactive power compensation,state estimation, network observability analysis, baddata processing and fault analysis for application indistribution automation has been developed.

Thismethod exploits the radial nature of the network anduses forward and backward propagation scheme toestimate the line flows, node voltages and loads at eachnode, based on the measured quantities.

Nodenumbers are ordered to generate proper parent-childrelation based on the network topology. Themeasurement values are checked. If the measurementsare not available then the values are either calculated orpseudo measurements provided. Accordingly appropriateindex is provided to the measurement values. Selections of pseudo measurements, filling of missing data,providing appropriate weightage are the functions of theobservability analysis algorithm.

Backward propagationis used to calculate branch currents, branch flows and theaverage of calculated and measured branch flowsproviding weights. During the iterative process the baddata is detected and replaced by pseudo or calculatedvalues. Computation of errors between measured andestimated values, detection of bad measurements,replacement by pseudo or calculated values in the place of bad measurements are the functions of the bad dataprocessing algorithm.

Node voltages and load at each Voltage at each node iscomputed and the test for convergence is performed. Theabsolute errors of measured and calculated values of realand reactive power flows and injection are alsoperformed. Computation of branch losses, total losses,and quantity of unbalance in current and voltage is doneonce the program converges.

Ordering of node numbers to generate properparent-child relation based on the networkFigure 1: Basic block diagram of state estimator. Otherwisevoltage magnitudes are set to 1. V 1 is thesource node and its value is assumed to be known. During backward propagation, voltagevalues are held constant and information about branchcurrents and averaged flows are transmitted backwardalong the feeder using backward walk. During thispropagation the load current is calculated assuming theload as the demand measurement in each node.

In theexample network of Figure 2, the backwardpropagation starts from branch and proceeds alongthe path , , , , and Table 1 givesthe Parent child relationship. Parent node 1 2 2 4 4 5 5Child node 2 3 4 5 6 7 8Table 1: Parent child relationship. The feeder substation source voltageis set to its measured value. During forward propagation,the branch currents are calculated based on the averagedflows, are used to calculate the nodal voltages and hencethe loads at each node.

The convergence criterion isthat, voltage magnitudes of real and imaginary parts of complex voltage at each node are compared with itsprevious iteration values. Therefore the voltagemismatch for j th node during k th iteration is given byfollowing equations.

In addition tovoltage, the absolute errors of measured and calculatedvalues of real and reactive power flows, real and reactivepower injection in branch are checked. Change in the supply frequency, voltage and waveformoutside the normal range gives rise to power qualityproblems. Power quality problems solved at the service7 5 6 DA is the best solution tomonitor and control these parameters. In real time environmentdistribution network configuration changes dynamicallydue to switching.

Real time network model depends onthe correctness of the network topology determined fromthe telemetered data. This research presents a networktopology processing NTP algorithm suitable fordistribution networks. A simple data structure fordistribution network connectivity information storage isproposed for efficient implementation of networktopology processing. The developed method has beentested on a large practical distribution network withseveral feeders Thukaram et al, a.

An efficient load flow solution technique is required as apart of the distribution automation system for takingvarious control and operation decisions. A robust threephasepower flow algorithm is presented Thukaram etal, b. This method exploits the radial nature of thenetwork and uses forward and backward propagationtechnique to calculate branch currents and node voltages. The proposed method considers all aspects of threephasemodeling of branches and detailed load modeling.

The merits of the method are, guaranteed convergenceeven for heavily loaded network with poor voltagepr of ile. The method has been tested on practicaldistribution systems with many feeders emanating fromthe grid substation with large number of nodes andbranches.

The application of the proposed method was alsoextended to find optimum location for reactive powercompensation and network reconfiguration for planningand day-to-day operation of distribution networks Jerome, a. An efficient and robust state estimation solutionalgorithm has been presented Thukaram et al, The algorithm is based on forward and backwardpropagation.

Distribution state estimators DSE will also play a critical role in distribution managementsystem to estimate those real-time system states whichare unable to be obtained from the limited measurementinstruments in the distribution network.

The success of DAS largely depends on the availability of reliabledatabase of the control center and thus requires anefficient state estimation SE solution technique. Themethod estimates the line flows, node voltages and loadsat each node based on the measured quantities.

Real-time control of the distribution system requires anestimate of the system states. Distribution, state estimatorestimates the current operating status of a distributionfeeder by using limited real-time SCADA data andhistorical load data.

In modern energy managementsystem EMS , a state estimation SE program processesa set of raw measurement data and provides a real-timeload flow solution which is the basis of the advancedfunction for system security monitoring and control. SEis based on the mathematical relations between thesystem state variables node magnitudes and angles andthe measurements. As the automation of powerdistribution progresses, it will become necessary to applystate estimation techniques as part of the DAS. SEsuitable for unbalanced three-phase radial distributionnetworks has been developed and tested on practicaldistribution network and presented Thukaram et al,a.

The SE cannot be executed without an adequate number of measurements. The extension of the method to thenetwork observability analysis and bad data detection isalso discussed. The proposed method has been tested ona few sample and practical distribution networks withsimulated data for real-time measurements Jerome,b. Unlike in transmission system, distribution networks maynot be provided with protective devices or circuitbreakers in each branch of the feeder.

For anyfault in the feeder, a large part of the feeder, may beisolated depending on the circuit breaker installation. Forthe purpose of speedy repair work and maintenance, it isimportant to find the exact fault location and type of fault. An algorithmic approach for finding the locationand type of fault based on the three phase measurementsobtained for state estimation is presented.

Results of thesimulated fault conditions on practical distributionsystems are also presented Thukaram et al, b. The advent and widespread use of high-powersemiconductor switches at the utilization, distribution andtransmission levels have made non-sinusoidal loadcurrents more common.

Power quality engineering hasnow become a subject of more focused interest. Distribution Automation can improve the quality of power Jerome, c. When wave shapes are irregular,voltages are poorly regulated, harmonics and flicker arepresent, and then power utilization will be degraded.

Power Quality enhancement trends has become a timelytopic in power engineering Jerome, ConclusionsDistribution automation will play a vital role inderegulated power market to provide reliable, economic,secure, stable and quality power.

A new, simplerobust three-phase power flow model and solutionalgorithm is presented. The proposed method is extendedto reconfiguration and reactive power compensation. Anefficient three-phase state estimation algorithm forapplication to radial distribution networks is presented. Due to the radial nature of thedistribution system in some cases, it may be possible togenerate the required number of data using the availablemeasurements. The proposed method is extended to thenetwork observability analysis and bad data detection.

An algorithmic approach for finding the location andtype of fault based on the three-phase measurementsobtained for state estimation is also presented. Results of thestudies indicate that the developed algorithms aresuitable for application to practical systems. Thealgorithms have no convergence problems. Thecomputational time is fast and suitable for real timeapplications. ReferencesGupta R. P, Gopesh Tiwari, P. Reddy, R. Varma, and T. Prabhakar Scope for Future ResearchIn light of the present work, further investigations can becarried out in the following related areas Extension of the proposed methods to meshed networksis worth investigating.

This calls for additional functionsinvolving techno-economical aspects in the distributionsystems operation. Captive power plants, specificindustrial loads, custom power, power quality relatedaspects are to be addressed. Some of the analytical functions of DMSmay not perform properly in the absence of adequatereliable data. Using more realistic models, training patternscan be generated, which can be used to train the NeuralNetworks for application in DMS.

The electric power industry is in the midst of a majorrestructuring in which electric energy will be traded as acommodity. Electric power markets will foster openaccess to all suppliers of electric power. Discriminationagainst any user of transmission system will be reducedor eliminated. A competitive wholesale market at thenational level will be fostered to reduce prices and acompetitive retailed market at the state level will beencouraged to provide customer choice and competitionin service and reliability.

Ultimately, small customerswill be able to choose their electric suppliers much asthey currently select their long-distance telephonecarriers. Jerome Jovitha a. Jerome Jovitha b. Jerome Jovitha c. Jerome Jovitha Markushevich S. Nokhum, Ivan C. Herejk and Ron E. Nielsen Narendranath Udupa, D. Thukaram, K. Parthasarathyand G.

Raju Thukaram D. Surapong a. Surapong b. Wijekoon Banda,C. Surapong, S. Srivastava a. Wijekoon Banda b. Wijekoon Banda,S.

Srivastava Shirmohammadi Dariush, H. Hong, A. Semlyen andG. Luo A A-'! D ! A A-'6! A D0-! HWd K8!! Experimentation and ResultsThe objective of the pilot cutting experiments was thecalibration of the parameters involved in the inversekinematics.

The inverse kinematics transforms the toolreference vector x, y, z, i, j, k fixed to the workpieceinto the machine coordinates X, Y, Z, A, B fixed to themachine frame.

A parametric saddle surface designedfor a telephone set, which contains both convex maximum and concave minimum regions was usedas a case study to demonstrate the tool path simulationand error estimation and minimization. The experimentconstituted a basic test of how our graphic simulations of tware would detect such kinematics errors, locate theproblem areas, as well as minimize the errors.

Theparametric saddle surface is given below. Fig 6 illustrates the saddle surface produced by thevirtual machine without angle switching. Fig 7 showsthat the maximum error is at vertex number and thevirtual machine automatically locates the vicinity of thelarge milling errors to determine the source vertex as and the destination vertices as Note that, thelarge circle or loop represents the maximum error of thesaddle surface at vertex Error identified by the virtual machine for thesaddle surface.

Fig 4 The saddle surfaceThe a-angle and b-angle are computed using machineinverse kinematics described in section 2. A and B axisare rotating simultaneously ranging from 0 to andfrom 0 to respectively. Fig 4 shows the requiredsaddle surface. Fig 5 shows the corresponding graphs of the a-angles and the angle adjustment.

Note that theangle adjustment is required to eliminate sharpvariations of the rotation angles near minimum or themaximum of the surface. Thereforethe adjustment is not required. Fig 7. Maximum error identified for the saddle surface. New angleJump from max to minrequires angle adjustmentOriginal angleFig 5. Angle adjustment for the saddle surface. Fig 8. Optimized tool path with minimum error of thesaddle surface.

T'' 2! R8 C"! F-' 1-! H ZZZK8! Aggregate Production Planning APP aims todetermine the production quantity and inventory levelin an aggregate term, for example, tons, dollars, orproduction hours.

Data in the APP is usually monthlydata. The APP is greatly needed when the demand ishighly seasonal. Master Production Scheduling MPS aims todetermine production quantities and timing of individual end products. The planning horizon of theMPS should be longer than the cumulative productionand purchasing lead-time. Theschedule is obtained by applying MRP logic or the pullsystem of Kanban. This paper is organized as follows. Some possible ways to solve or alleviate the problemsare also proposed.

The complexity of the production system is dividedinto 2 types, namely simple and complex. Thecomplex one is characterized by a long setup time andlarge production lot size, wide variety of products,complicated products having many components, andinsufficient production capacity of some processes. The simple one is the opposite.

Therefore, the companies should tryto simplify the production process, e. This result corresponds to that of Krajewski et al. This indicates that the perception of persons in the company is also important. This perception should be altered. APP is not performed explicitly andsystematically. APP is performed without explicitlyconsidering related costs, such as, regular andovertime labor costs of permanent andtemporary workers, inventory holding cost, andsubcontracting cost.

The current policy of varying the productionrate and keeping inventory results in relativelyhigh total costs. This means that the currentpolicy is not optimal. Mathematical models for determining anoptimal APP are not applied. Techawiboonwong and Yenradee developed aspreadsheet APP model based on general requirementsand constraints of Thai industries. The model candetermine the optimal way of varying production rateand keeping inventory that minimizes total relatedcosts.

Most participants feel that the model can beapplied to the real cases of their companies if it isslightly modified. The mathematical model has not been appliedfor disaggregating the APP into the MPSconsidering related objective and constraints. Some well-known models, such as that of Haxand Meal , Bitran et al. The author and his doctoral student are studying theseproblems and developing a workable model andmethod for determining the MPS based on the APP forThai industries.

Receiving customer orders. Issuing invoices and updating stock of finished products. Issuing purchase orders of raw materials andcomponents of the products. Receiving the purchased items based on thepurchase orders and updating stock of thepurchased parts. Issuing work orders for manufactured itemsand printing pick-lists for withdrawing therequired parts into the work orders.

Withdrawing the required parts into the workorders. They will become the work-inprocess. Completing the work orders to receive themanufactured parts to stock and reducing thework-in-process level of the required parts.

Checking stock of all items and makingadjustments if it is necessary. If any inventory transaction stated above is notperformed correctly, accurately, and promptly, the data of the system will be inaccurate. When the data isinaccurate, the MRP-II system will not be able togenerate reliable detailed production and purchasingschedules.

Even if the data of the MRP-II system is accurate, itmay not be able to generate reliable production andpurchasing schedules because of the following reasons. The MRP-II system assumes a fixedproduction lead-time regardless of the lotsize, load on work centers, and priority of jobs.

Alternative routings are notconsidered. Thus, heavy load on the firstpriority machine cannot be allocated to thesecond priority machine with light load. The detailed production plan is generated byassuming that all work centers have unlimitedcapacity. Capacity checking is performedafterward.

When a capacity problem isevident, the planner has to solve it manually. There is no automatic routine to avoid thecapacity problem. There are many attempts to modify the MRP-II systemto be able to generate reliable production andpurchasing schedules by considering finite capacity of key work centers.

Pandey, Yenradee, andArchariyapruek developed a finite capacitymaterial requirement planning FCMRP algorithm,which is executed in two stages. First, capacity-basedproduction schedules are generated from the input data.

Second, the algorithm produces an appropriate materialrequirement plan to satisfy the schedules obtained fromstage 1. The author and a doctoral student are developing analgorithm and a computer program to determine theproduction and purchasing schedules considering finitecapacity of the bottleneck work centers based on theschedules generated by the MRP system. Alternateproduction routes are considered and some jobs on thebottleneck may be allocated to the non-bottlenecks.

The excessive loads in some periods on the bottleneckwill be shifted earlier or later to smooth the loads. Finally, the production schedules of other items, whichare not produced on the bottleneck, and the purchasingschedules of all items will be revised accordingly. Thisprogram is appropriate for small industries facingcapacity problems.

Following a wrong implementationsequence not only results in excessive implementationtime but also degrades the performance e. Then, overall direct andindirect relationships are determined using a procedurebased on max-min fuzzy composition. Those in Sector 4 can beimplemented at any time see Table 2.

The techniquesin Sector 4 can be implemented when it is convenient any time. This will make the implementation processprogress smoothly since the techniques that support orare prerequisite of others have been alreadyimplemented.

Some mathematical models for this purpose should bedeveloped by considering real objective and constraints of Thai industries. The MRP-II system has a drawback in that it cannotgenerate reliable production and purchasing scheduleswhen a bottleneck exists in a shop floor.

From the fact that industries withdifferent requirements may need different FCMRPalgorithms, the FCMRP system with a number of selectable algorithms should be developed based onvarious requirements of Thai industries. It requires someengineering and management techniques to help solvethe problems.

Practical methods for solving theproblems should be further developed based on thecontext of Thai industries, which is sometimesdifferent from that of other developed countries.

ReferencesBartezzaghi, E. Bitran, G. Fearon, H. New York: West Publishing. Hax, A. HierarchicalIntegration of Production Planning and Scheduling. North Holland, Amsterdam. Hernandez, A. Hobbs, O. Krajewski, L. Pandey, P. Schonberger, R. Stein, R. New York: Marcel Dekker. Techawiboonwong, A. Voss, C. Wantuck, K. Yenradee, P. Harrison, A. As examples, two promising nature-inspired techniques are selected for detailed discussion. In the paper, the concepts of the two techniques,which are inspired by real models in nature, are carefully explained and the implementations of the techniques forstructural design problems are also described.

The discussion on the efficiency of both techniques is included as well. IntroductionMain concerns of structural engineers when designingstructures include, among others, performance of thestructures as well as cost to construct them. Gooddesigners are actually those who can balance these twoconcerns properly.

Giving too much weight on the costmay result in unsafe structures while too much concernon the performance may yield expensive structures. Thispredicament can be resolved by employing optimization.

Unfortunately, this is something that is easy to say butdifficult to do. The biggest obstacle to achieve theoptimal structural design is the nature of the structuraldesign process itself. For example, to be able to designmembers of a steel structure, it is necessary to know theinternal forces of each member of the structure. Once theinternal forces of the members are known, the sectionalsizes and other details of the members can be computed. However, to be able to obtain the internal forces, priorknowledge of the sizes of the members is required.

Tocircumvent this dilemma, the designer will have toestimate the sizes of the members first. After that, theassumed sizes will be used for the calculation of theinternal forces. The obtained internal forces will in turnbe used to calculate the appropriate sizes of the members. The process can be repeated until a satisfactory result isobtained. Although this technique can be considered asan optimization technique, it is a crude iterative process,which does not guarantee convergence and can be veryinefficient.

The first estimation of the sectional sizes isthe most crucial step and it usually requires a great deal of experience. If the first estimation is far from theoptimal solution, there is a high possibility that the finalresult will be poor. Even though it is obvious that the popular process of structural design mentioned above is not efficient andthere are quite a number of other long-establishedoptimization techniques see, for example, Rao ,these techniques are rarely used in the design process.

The main reason is that these conventional techniquesare generally gradient-based optimization techniques. The gradient-based optimization originates from aproblem of finding the conditions that give themaximum or minimum value of a function in calculus.

The concept is simple; the gradients of a function with To find theminimum, the search in each step is directed into adirection that has the strongest negative gradient until aminimum point where the gradients vanish is found. T of ind the maximum, a direction with the strongest positivegradient can be considered, instead. Since the gradientbasedtechniques employ calculus, they are generallysuitable for problems with continuous design variables.

Unfortunately, structural optimization problems dealmostly with discrete design variables. For example, if thedesign variables are sectional sizes, only those sizesavailable in the market can be selected.

Another problemwith the gradient-based techniques is that they search fora local optimum solution, not the global one. Thisdeficiency in the gradient-based techniques is inheritedfrom calculus. Vanishing of the gradients is in fact anecessary condition for local extremums but it isdefinitely not a sufficient condition for the globalextremum. As a result, the quality of the obtained resultcan very much depend on the starting search point.

If thestarting point is close to a local optimum, the search maybe trapped in that local optimum. Recently, several new optimization techniques that arenot based on calculus have been proposed.

Many of these techniques are inspired by nature. The simulated annealing is inspired bythermal annealing of critically heated solids. The antcolony optimization mimics the foraging behavior of antcolonies in the real world. The inspirations of thesetechniques are in a sense optimization processesperformed by nature.

With the current rapid advances inthe field of computer science, many natural behaviorshave been artificially simulated by computers. Theoriginal aim of the simulations is either to understandthese natural behaviors better or to actually inventartificial lives. The nature-inspired optimizationtechniques are, to a certain degree, byproducts of theseresearches.

In the optimization field, these techniques aregaining popularity over the calculus-based techniquesdue to the fact that they are generally more robust thanthe calculus-based techniques, meaning that theirperformances are nearly problem-independent. Also,these techniques are more efficient than the calculusbasedtechniques in searching for the global optimum. Inaddition, they are suitable for problems with discretevariables; consequently, they are also suitable forstructural design optimization problems.

This paper discusses two of these nature-inspiredoptimization techniques, i. The paper aims to demonstratethe potential of nature-inspired optimization techniquesin solving structural design optimization problems.

These two techniques are selected because of theirstriking resemblance to their corresponding naturalmodels as well as their promising capability inoptimization. In this paper, only structural designapplications will be discussed. Genetic Algorithms for StructuralDesign OptimizationGenetic algorithms GAs are global probabilistic searchalgorithms inspired by the survival- of -the-fittest theory Goldberg The algorithms have receivedconsiderable attention because of their versatileapplication in several fields Grefenstette , Goldberg, Deb , Marcelin et al.

GAs start their search from many points in search spaceat the same time. These starting points are usuallyselected randomly and known as the initial population. Through the consideration of fitness values of thesesearch points, which are given based on their merit, andthe randomized information exchange among the points,a new set of search points with higher merit is created.

The process is then repeated until a satisfactory result isobtained. Since the technique utilizes information frommany search points at the same time, there is less chancefor the search to be trapped in any of local optimalpoints. Another distinguishing characteristic of GAs isthat the algorithms do not directly work with designvariables.

Rather, they work with codes that represent thevariables. Generally, binary coding is used. Because of the discrete nature of coding, GAs are the perfect choicefor those problems with discrete variables.

To understand the concept of GAs in structural designoptimization, it is advisable to begin with coding. Asalready mentioned, GAs employ codes to representdesign variables and consequently to represent designsolutions, which are in fact combinations of designvariables. Consider a truss structure shown in Figure 1. The structure has three members. Assume that the section of each member is to be selected from a list of fouravailable sections, i. Since there are four available sections, a two-bitbinary string can be used to code them; i.

To begin the calculation, the initial population will haveto be created at random. In the truss example shown inFigure 1, several six-bit binary strings can be randomlycreated to form the first population. Each six-bit stringrepresents one truss structure and is called an individualin the GA terminology. To be able to use the survival- of the-fittesttheory, it is necessary to define the fitness of each individual or each design.

This can be done bytaking into account the admissibility and the objective For example, in sizingoptimization, designs with higher fitness are those thatsatisfy all design criteria and have small weight.

With themeasure of fitness specified, the survival- of -the-fittesttheory can then be applied and the evolution process canbe artificially created. After the first population is randomly obtained, thefollowing three basic operators, i. Thereproduction operator defines a process in whichindividuals are selected, based on their fitness, formating and subsequent genetic actions. Individuals withhigher fitness will have higher chance to be selected intothis mating pool.

Consequently, highly fit individualscan reproduce and pass their genes to the next generationwhile less fit individuals simply disappear. Afterreproduction, the crossover operator is implemented. Inthe crossover operator, new individuals are created byexchanging information among the individuals in themating pool generated by the reproduction operator.

There are many crossover operators available in theliterature Goldberg , Jenkins , Camp et al. Nevertheless, in most crossover operators, twoindividuals are selected at random from the mating pooland some portions of their strings are exchanged. Thetwo original strings are called parent strings and the tworesulting strings are called child strings.

Figure 2 showsan example of one of the simplest crossover operators,namely the one-point crossover. In this type of crossover, a crossing site is randomly selected and allbits on one side of the selected site of the two strings areswapped to create two child strings. It is clear thatcrossover may yield better or worse child strings. To beable to adjust the degree of uncertainty of the crossoverphase, it is not necessary to use all individuals in themating pool in the operator.

This is done by adjusting theprobability that crossover is performed crossoverprobability. The last GA operator is the mutationoperator. The operator is usedsparingly with a small probability mutation probability.

Mutation allows for the possibility that features that donot exist in both parent strings may be created andpassed to their children. After reproduction, crossover,and mutation, a new generation is obtained. The processis repeated until a satisfactory result is achieved. Conceptually, reproduction exploits available goodstrings while crossover and mutation explore newstrings. Exploitation and exploration must be kept inbalance in order to obtain good results.

The success of hiswork became the turning point in the field of structuraldesign optimization. Many studies have been done toimprove the efficiency of GAs for structural designoptimization problems for example, Jenkins ,Rajeev and Krishnamoorthy , Camp et al. The application of GAs to solve relatively large structural systems has alsobeen explored Rajan , Galante GAs havebeen well accepted among researchers as a high-qualitytechnique that is not difficult to implement and use.

Ant Colony Optimization for StructuralDesign OptimizationAnt colonies can collectively perform complicated taskseven with a low intelligent level of each individual ant. One of the examples is the foraging behavior of ants. Anant colony is capable of finding the shortest path betweenits nest and a food source without using visual clues. Thiscapability of the colony is achieved by indirectcommunication between ants via the use of pheromone.

Itis well-known that ants lay and follow pheromone trails. These simple trail-laying and trail-following mechanismsenable the colony to seek out the shortest path from allpaths that the ants in the colony have ever explored. Consider a colony of ants shown in Figure 3. At the beginning, theants will select the two paths with equal probability,meaning that there will be approximately half of the antsselecting each path. Since the shorter path requires lesstime to complete, for the same amount of time, the antson the shorter path will be able to complete more rounds.

As a result, the quantity of pheromone on the shorter pathgrows faster than on the longer one. Finally, the levels of pheromone on the two paths will be so different thatvirtually all ants will select the shorter one.

C NestFoodFig. B Pheromone isdeposited faster on the shorter path. More ants select theshorter path. C Finally, all ants select the shorter path. It is important to note that pheromone trails establishedby ants do not last forever but rather they evaporate. Thispheromone evaporation is also an important mechanismsince it avoids too rapid a convergence towards a suboptimalpath. The three aforementioned mechanisms, i. Recently, the ACO technique is becoming popularamong researchers in the field of heuristic optimization see, for example, a survey in Dorigo et al.

Theproblem that seems to fit the technique naturally is thetraveling salesman problem Dorigo and Gambardella Nevertheless, the technique has been applied tovarious types of problem, such as the quadraticassignment problem Talbi et al. The application of thetechnique in the field of civil engineering is still rare see,for example, Abbaspour et al.

The ACO technique has been developed forcombinatorial optimization problems. Most of practicalstructural design optimization problems consider onlysizing optimization, which is basically combinatorialoptimization. As a result, the ACO technique can beapplied to solve them. To this end, structural designoptimization problems under consideration have to beprepared in such a suitable way that the problems fit theACO technique.

After that, a simple ACO algorithm canbe implemented. To understand the concept of the ACOfor structural design optimization, consider in Figure This sizing optimization problem is acombinatorial optimization problem and can be thought of as a foraging problem of an ant colony. As shown inFigure 4, an artificial nest and a food source can beestablished.

In the figure, node 1 represents the nest andnode 4 represents the food source. The ants will have tomove from node 1 to node 4 by passing all other nodes inbetween. Between each pair of nodes, there are fouravailable sub-paths, representing four different availablesections for each design variable. The partial walk of theants between nodes 1 and 2 represents the selection forthe design variable A 1 , and the partial walks between thesubsequent nodes are for the subsequent design variables.

For the ACO to work, artificial ants will have to makemany artificial tours and they must obey the followingsimple rules; i. In other words, paths withhigher pheromone level will have a higher chance tobe selected by ants. If the path is of high quality, the ant that haswalked the path will lay a large amount of pheromone on the path.

For structural designoptimization, a path is considered high quality if itrepresents an admissible structure with low weight. These two rules, though simple, are enough for thecolony to perform its task. It can be seen that the concept of the ACO is very simpleand its implementation is straightforward. Theperformance of the technique has been found to becomparable with that of GAs when used in structuraldesign optimization.

Even though the technique is still inits infancy, its potential is apparent. As the application of the technique in the field of civil engineering, not tomention the field of structural engineering, is still rare, alot of improvements are to be expected. Concluding RemarksThis paper demonstrates the potential of nature-inspiredoptimization techniques in solving structural designoptimization problems. Two promising nature-inspiredtechniques are discussed; they are genetic algorithms GAs and the ant colony optimization ACO.

When GAs areused in structural design optimization, a population of structures is created and an evolution process under thesurvival- of -the-fittest rule is applied to the population. The evolution process is controlled by threeoperators, i.

GAshave been used successfully in many different fields,including structural design. Compared with GAs, theACO is a newer technique and has not been used much inthe field of civil engineering. The ACO is inspired by theway ant colonies function in the real world. An antcolony is capable of finding the shortest path between itsnest and a food source by the use of pheromone. In fact,the task of finding the shortest path is achieved by usingthree basic mechanisms, i. These three mechanisms constitute the ACO.

When theACO is applied to structural design optimization, theoptimization problem under consideration is transformedinto a foraging problem of an ant colony. A betterdesign will be made to be equivalent to a shorter route. Nevertheless, thetechnique is still new and much progress can beexpected. Recommendations for Future Researchand DevelopmentThe two natural-inspired optimization techniquesdiscussed in this paper, i. Their searches are considered global because bothtechniques consider many search points at the same time.

This will naturally allow the search to avoid beingtrapped in any local optimal points. If the advantage of existing local search algorithms in fine-tuning localoptimal solutions is combined with the global searchcapability of these two techniques, even better resultsmay be expected. This idea can possibly be implementedby using genetic algorithms or the ant colonyoptimization to globally search for the neighborhood of the global optimal solution first.

After that, a local searchalgorithm can be used to improve the solution obtainedfrom the global search. ReferencesAbbaspour, K. Schulin, and M. Estimating unsaturated soilhydraulic parameters using ant colony optimization.

Advances in Water Resources, Vol. Camp, C. Pezeshk, and G. Optimizeddesign of two-dimensional structures using a geneticalgorithm. Journal of Structural Engineering, Vol. Dawid, H. Adaptive learning by geneticalgorithms: analytical results and applications toeconomic models. New York: Springer. Deb, K. Optimization for engineering design:algorithms and examples. Dorigo, M. Electronic Arts said that it is offering 16 of its most popular titles for download to iPhone users spend on your card the more points that you get in your account.

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