• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1101-1106
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• 2005

The purpose of this study was to develop a process management system to manage ingot fabrication and the quality of the ingot. The ingot is the first manufactured material of wafers. Operating data (trace parameters) were collected on-line but quality data (measurement parameters) were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Thus, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were employed for data generation, and then modeling was accomplished, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to the control parameters. The dynamic polynomial neural network (DPNN) was used for data modeling that used the ingot fabrication data.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.239-245
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• 2012

This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.383-389
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• 2016

This paper presents a novel automatic white balance (AWB) algorithm for consumer imaging devices. While existing AWB methods require reference white patches to correct color, the proposed method performs the AWB function using only an input image in two steps: i) white point detection, and ii) color constancy gain computation. Based on the dark channel prior assumption, a white point or region can be accurately extracted, because the intensity of a sufficiently bright achromatic region is higher than that of other regions in all color channels. In order to finally correct the color, the proposed method computes color constancy gain values based on the Y component in the XYZ color space. Experimental results show that the proposed method gives better color-corrected images than recent existing methods. Moreover, the proposed method is suitable for real-time implementation, since it does not need a frame memory for iterative optimization. As a result, it can be applied to various consumer imaging devices, including mobile phone cameras, compact digital cameras, and computational cameras with coded color.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.62-68
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• 2009

Two new generation gap models with modified parent-centric recombination(PCX) operator are proposed. First, the self-adaptation generation gap(SGG) model is a control method that keeps a replaced probability of parents by offspring to a certain level which obtains better performance. Second, virtual cluster generation gap(VCGG) is provided to extend distances among parents using clustering, which causes it to diversify individuals. In this model, distances among parents can be controlled by size of clusters. To demonstrate the effectiveness of our two proposed approaches, experiments for three standard test problems are executed and compared to most competing current approaches, CMA-ES and Generalized Generation Gap(G3) with PCX. It is shown two proposed methods are superior to consistently other approaches in the study.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.834-844
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• 2012

Nonlinear electronic loads draw harmonic currents from the power grids that can cause energy loss, miss-operation of power equipment, and other serious problems in the power grids. This paper proposes a harmonic compensation method using multiple distributed resources (DRs) such as small distributed generators (DGs) and battery energy storage systems (BESSs) that are integrated to the power grids through power inverters. For harmonic compensation, DRs should inject additional apparent power to the grids so that certain DRs, especially operated in proximity to their rated power, may possibly reach their maximum current limits. Therefore, intelligent coordination methods of multiple DRs are required for efficient harmonic current compensation considering the power margins of DRs, energy cost, and the battery state-of-charge. The proposed method is based on fuzzy multi-objective optimization so that DRs can cooperate with other DRs to eliminate harmonic currents with optimizing mutually conflicting multi-objectives.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.6-11
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• 2005

In this paper, we propose a new fuzzy clustering scheme that optimizes the initial structure and the parameters to improve the performance of a intelligent systems. The proposed method keeps the good properties of clustering, and improves the total systems' performance at the same time, Especially, the proposed algorithm not only keeps robust to change threshold value in the optimization process, but also improves the performance of a system through the process of the self-organizing and the converging intelligent systems in its structure of cluster. In experiments, the superiority of the proposed scheme is presented by comparing its performance with other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.603-610
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• 2001

Industrial processes are normally operated by skilled humans who have the cumulative and logical information about the system. Fuzzy control has been investigated for many application. Intelligent control approaches based on fuzzy logic have a chance to include human thinking. This paper represents modeling approach based upon operators knowledge without mathematical model of the system and optimize the controller. The experimented system is constructed for sending a ball to the goal position using wind of two DC motors in the predefined path. A vision camera to mimic human eyes detects the ball position. The system used in this experiment could be hardly modeled by mathematical methods and ould not be easily controlled by conventional manners. The controller is designed based on the input-output data and experimental knowledge obtained by trials, and optimized under the predefined performance criterion. And this paper shows the data adaptation for changeable operating condition. When the system is driven in the abnormal condition with unconsidered noise, the new optimal operating parameters could be defined by adjusting membership functions. Thus, this technique could be applied in industrial fields.

• ETRI Journal
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• v.43 no.4
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• pp.603-616
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• 2021

Over the last few years, autonomous vehicles have progressed very rapidly. The odometry technique that estimates displacement from consecutive sensor inputs is an essential technique for autonomous driving. In this article, we propose a fast, robust, and accurate odometry technique. The proposed technique is light detection and ranging (LiDAR)-based direct odometry, which uses a spherical range image (SRI) that projects a three-dimensional point cloud onto a two-dimensional spherical image plane. Direct odometry is developed in a vision-based method, and a fast execution speed can be expected. However, applying LiDAR data is difficult because of the sparsity. To solve this problem, we propose an SRI generation method and mathematical analysis, two key point sampling methods using SRI to increase precision and robustness, and a fast optimization method. The proposed technique was tested with the KITTI dataset and real environments. Evaluation results yielded a translation error of 0.69%, a rotation error of 0.0031°/m in the KITTI training dataset, and an execution time of 17 ms. The results demonstrated high precision comparable with state-of-the-art and remarkably higher speed than conventional techniques.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.