• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.97-102
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• 2011

Lots of 2D vision algorithms have been applied for inspection. However, these 2D vision algorithms have limitation in inspection applications which require 3D information data such as the height of semiconductor chips. Stereo vision is a well known method to measure the distance from the camera to the object to be measured. But it is difficult to apply for inspection directly because of its measurement error. In this paper, we propose two adjustment methods to reduce the error of the measured height data for stereo vision. The weight value based model is used to minimize the mean squared error. The average value based model is used with simple concept to reduce the measured error. The effect of these algorithms has been proved through the experiments which measure the height of semiconductor chips.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.448-457
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• 1998

A neural network based control algorithm with fuzzy compensation is proposed for the automated adjustment in the production of electronic end-products. The process of adjustment is to tune the variable devices in order to examine the specified performances of the products ready prior to packing. Camcorder is considered as a target product. The required test and adjustment system is developed. The adjustment system consists of a NNC(neural network controller), a sub-NNC, and an auxiliary algorithm utilizing the fuzzy logic. The neural network is trained by means of errors between the outputs of the real system and the network, as well as on the errors between the changing rate of the outputs. Control algorithm is derived to speed up the learning dynamics and to avoid the local minima at higher energy level, and is able to converge to the global minimum at lower energy level. Many unexpected problems in the application of the real system are resolved by the auxiliary algorithms. As the adjustments of multiple items are related to each other, but the significant effect of performance by any specific item is not observed. The experimental result shows that the proposed method performs very effectively and are advantageous in simple architecture, extracting easily the training data without expertise, adapting to the unstable system that the input-output properties of each products are slightly different, with a wide application to other similar adjustment processes.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.1-12
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• 1992

Parallel Genetic Algorithms (GAs) are developed to solve a single machine n-job scheduling problem which is to minimize the sum of absolute deviations of completion times from a common due date. (0, 1) binary scheme is employed to represent the n-job schedule. Two selection methods, best individual selection and simple selection are examined. The effect of crossover operator, due date adjustment mutation and due date adjustment reordering are discussed. The performance of the parallel genetic algorithm is illustrated with some example problems.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.1095-1103
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• 1992

An approach to fuse the fuzzy inference system which is able to deal with imprecise and uncertain information and genetic algorithms which display the excellent robustness in complex optimization problems is presented in this paper. In order to combine genetic algorithms and fuzzy inference engine effectively the new reasoning method is suggested. The efficient identification method of fuzzy rules is proposed through the adjustment of search areas of genetic algorithms. The feasibilty of the proposed approach is evaluated through simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.607-612
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• 1998

In this paper, a controller using fuzzy-genetic algorithms is proposed for pat-tracking of WMR. A fuzzy controller is implemented so as to adjust appropriate crossover rate and mutation rate. A genetic algorithms is also implemented to have adaptive adjustment of control gain during optimizing process. To check effectiveness of this algorithms, computer simulation is applied.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.561-566
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• 2021

The optimum kernel size for error-distribution estimation with given error samples cannot be used in the weight adjustment of minimum Euclidean distance between error distributions (MED) algorithms. In this paper, a new adaptive kernel estimation method for convergence enhancement of MED algorithms is proposed. The proposed method uses the average rate of change in error power with respect to a small interval of the kernel width for weight adjustment of the MED learning algorithm. The proposed kernel adjustment method is applied to experiments in communication channel compensation, and performance improvement is demonstrated. Unlike the conventional method yielding a very small kernel calculated through optimum estimation of error distribution, the proposed method converges to an appropriate kernel size for weight adjustment of the MED algorithm. The experimental results confirm that the proposed kernel estimation method for MED can be considered a method that can solve the sensitivity problem from choosing an appropriate kernel size for the MED algorithm.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.4
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• pp.47-55
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• 2004

Most questions made for remote examinations on web-based education system use methods of making questions using fixed questions or randomly using item pools or automatically using degree of difficulty. Particularly, automatically selection methods using degree of difficulty is the kernel of a question that objectivity of examination questions by degree of difficulty adjustment based result of examination. This paper is use automatically selection methods for examination on web-based education system. Therefore we present new algorithms of mediateness degree of difficulty as regards education ability of students for adjust the degree of difficulty. We identified this algorithms is more effective as compared with previously algorithms on web-based education system

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.310-313
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• 1996

In this paper, a neural control algorithm is proposed on the automation of adjustment process. The adjustment processes in camcoder production line are modelled, and the processes are adjusted automatically by means of off-line supervisory trained multi-layer neural network. We have made many experiments on the several adjustment processes by using the control algorithm. There are many unexpected troubles to achieve the desirable adjust time in the practical application. To overcome those, some auxiliary algorithms are demanded. As a result, our proposed algorithm has some advantages - simple architecture, easy extraction of the training data without expertises, adaptability to the varying systems, and wide application for the other resemble processes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.160-164
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• 2008

Most questions made for remote examinations on E-learning databank based selection system use methods of making questions automatically using degree of difficulty. This methods is the kernel of a question selection that degree of difficulty as make test questions, and then needs continuous management for degree of difficulty. This paper present improved algorithms for dynamically adjustment of degree of difficulty based on examination result that is more efficient set of question. We identified this algorithms is more effective as compared with previously algorithms on web-based education system.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.135-151
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• 2022

The generalized displacement method is a nonlinear solution scheme that follows the equilibrium path of the structure based on the development of the generalized displacement. This method traces the path uniformly with a constant amount of generalized displacement. In this article, we first develop higher-order generalized displacement methods based on multi-point techniques. According to the concept of generalized stiffness, a relation is proposed to adjust the generalized displacement during the path-following. This formulation provides the possibility to change the amount of generalized displacement along the path due to changes in generalized stiffness. We, then, introduce higher-order algorithms of variable generalized displacement method using multi-point methods. Finally, we demonstrate with numerical examples that the presented algorithms, including multi-point generalized displacement methods and multi-point variable generalized displacement methods, are capable of following the equilibrium path. A comparison with the arc length method, generalized displacement method, and multi-point arc-length methods illustrates that the adjustment of generalized displacement significantly reduces the number of steps during the path-following. We also demonstrate that the application of multi-point methods reduces the number of iterations.