• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.331-337
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• 2004

In the analysis of massive volume data, a neural network model is a useful tool. To implement the Neural network model, it is important to select initial value. Since the initial values are generally used as random value in the neural network, the convergent performance and the prediction rate of model are not stable. To overcome the drawback a possible method use samples randomly selected from the whole data set. That is, coefficients estimated by logistic regression based on the samples are the initial values.

• Journal of the Korean Chemical Society
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• v.14 no.4
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• pp.297-308
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• 1970

In order to search a method of manufacturing better activated clay in an effcient way, attempt was made to select particularly samples for activation to concentrate the research upon them with varied activation to concentrate the research upon them with varied activation conditions. Special attention was also made to the low quality materials because they may become good activated clay if treated under a suitable activation condition.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.11 no.18
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• pp.59-69
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• 1988

This study represents the method of application of W.S(Work Sampling) to determine job sequence. The result shows job sequence which has the came performance measure of optimal job sequence is selected by average number of 199 sampling. In the case, the optimal job sequence is not selected within the sampling number of 921 which satisfy the reliability of 99.5% and precision of 99%, the deviation is very little which 0.73%. This improves the possibility of application of W.S method to select optimal job sequence is very high.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.34-36
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• 2006

In this paper we present a method of feature extraction for motor imagery single trial EEG classification, where we exploit nonnegative matrix factorization (NMF) to select discriminative features in the time-frequency representation of EEG. Experimental results with motor Imagery EEG data in BCI competition 2003. show that the method indeed finds meaningful EEG features automatically, while some existing methods should undergo cross-validation to find them.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.861-865
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• 1994

By using Neural Networks, a 8$\times$8 Banyan network controller is designed and implemented. In order to solve internal blocking and output blocking, Winner-Take-All method is used. The longer queue takes higher priority. First-in-first-out method is used among the non-blocking cells in the queue selected.The required time to select a cell is 2.7 $\mu$sec for 155Mbps. The implemented controller using Xilinx FPGA chip selects cells within 2.5$\mu$sec.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.175-178
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• 1996

Power system is becomming more and more complex and large. So system is stressed increasingly. This paper presents a method to select contingency ranking in power systems for EMS operation. Firstly, the proposed method is applied line outage using Thevenin equivalent circuit. Secondly, Contingency harmful to system is selected by loss variation between base case and fault. Thirdly, this paper prescribed simulated line sequence. Therefore this algorithm shows higher computation speed and effective memory use.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.199-203
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• 2016

A brain-computer interface or BCI provides an alternative method for acting on the world. Brain signals can be recorded from the electrical activity along the scalp using an electrode cap. By analyzing the EEG, it is possible to determine whether a person is thinking about his/her hand or foot movement and this information can be transferred to a machine and then translated into commands. However, we do not know which information relates to motor imagery and which channel is good for extracting features. A general approach is to use all electronic channels to analyze the EEG signals, but this causes many problems, such as overfitting and problems removing noisy and artificial signals. To overcome these problems, in this paper we used a new optimization method called the Fruit Fly optimization algorithm (FOA) to select the best channels and then combine them with CSP method to extract features to improve the classification accuracy by linear discriminant analysis. We also used particle swarm optimization (PSO) and a genetic algorithm (GA) to select the optimal EEG channel and compared the performance with that of the FOA algorithm. The results show that for some subjects, the FOA algorithm is a better method for selecting the optimal EEG channel in a short time.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.279-284
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• 2010

The plane curve approach which was proposed by Boukharouba et. al. is a multi-threshold selection method through searching peak-valley based on histogram cumulative distribution function. However the method is required to select parameters to compose plane curve, and the shape of plane curve is affected according to parameters. Therefore detection of peak-valley is effected by parameters. In this paper, we propose an entropy maximizing-based method to select optimal plane curve parameters, and propose a multi-thresholding method based on the selected parameters. The effectiveness of the proposed method is demonstrated by multi-thresholding experiments on various images and comparison with other conventional thresholding methods based on histogram.

• Journal of Korean Society for Quality Management
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• v.17 no.1
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• pp.69-81
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• 1989

This study is concerned with a job sequencing method using the concept of sampling technique in the case of Job Shop. This is the follow study of Kang and Ro (1988) which examined the possibility of application of sampling technique to determine the Job Sequence in the case of Flow Shop. Not only it is very difficult, but also it takes too much time to develop the appropriate job schedules that satisfy the complex work conditions. The most job sequencing algorithms have been developed to determine the best or good solution under the special conditions or assumptions. The application areas of these algorithms are also very narrow, so it is very hard to find the appropriate algorithm which satisfy the complex work conditions. In this case it is very desirable to develop a simple job sequencing method which can select the optimal job sequence or near optimal job sequence with a little effort. This study is to examine the effect of sampling job sequencing which can select the good job of 0.01%~5% upper good group. The result shows that there is the sets of 0.05%~23% job sequence group which has the same amount of performance measure with the optimal job sequence in the case of experiment of N/M/G/$F_{max}$. This indicates that the sampling job sequencing method is a useful job sequencing method to find the optimal or good job sequence with consuming a small amount of time. The results of ANOVA show that the only one factor, number of machines is the significant factor for determining the job sequence at ${\alpha}=0.01$. It takes about 10 minutes to compare the number of 10,000 samples of job sequence by personal computer and it is proved that the selection rate of the same job sequence with optimal job sequence is 23.0%, 3.9% and 0.065% in the case of 2 machines, 3 machines and 4 machines, respectively. The area of application can readily be extended to the other work condition.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.919-929
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• 2013

We compared two basic methods, combine-accumulate method and accumulate-combine method, using the past quality information in multivariate quality control procedure for monitoring mean vector of multivariate normal process. When small or moderate shifts have occurred, accumulate-combine method yields smaller average run length (ARL) and average time to signal (ATS) than combine-accumulate method. On the other hand, we have found from our numerical results that combine-accumulate method has better performances in terms of switching behavior than accumulate-combine method. In industry, a quality engineer could select one of the two method under the comprehensive consideration about the required time to signal, switching behavior, and other physical factors in the production process.