• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2033-2036
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• 2004

A novel approach to estimate the real time moving trajectory of an object is proposed in this paper. The object position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Kalman filter and neural networks are utilized. Since the Kalman filter needs to approximate a non-linear system into a linear model to estimate the states, there always exist errors as well as uncertainties again. To resolve this problem, the neural networks are adopted in this approach, which have high adaptability with the memory of the input-output relationship. Kohonen Network(Self-Organized Map) is selected to learn the motion trajectory since it is spatially oriented. The superiority of the proposed algorithm is demonstrated through the real experiments.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.316-318
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• 2001

본 논문에서는 자기조직화 형상지도(Self-organizing Feature Maps)를 사용하여 움직이는 물체에 대해 움직임의 특성을 자동으로 분석하였다. Kohonen Network는 자기조직을 형성하는 unsupervised learning 알고리즘으로서, 이 논문에서는 생태계에서의 데이터를 Patternizing하고, Clustering 하는데 사용한다. 본 논문에서 Kohonen 신경망의 학습에 사용한 데이터는 CCD 카메라로 물고기의 움직임을 추적한 좌표 데이터이며, diazinon 0.1 ppm을 처리한 물고기 점 데이터와 처리하지 않은 점 데이터를 각각 낮.밤 약 10시간동안 수집하여, \circled1처리전 낮 데이터 \circled2처리전 밤 데이터 \circled3처리전 낮 데이터 \circled4처리후 밤 데이터 각각 4개의 group으로 분류한 후, Kohonen Network을 사용하여 물고기의 행동 차이를 분석하였다.

• The Journal of the Acoustical Society of Korea
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• v.13 no.6
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• pp.83-90
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• 1994

In this paper, we propose a new learning algorithm, ASOFM(Adaptive Self Organizing Feature Map), to solve the defects of Kohonen's Self Organiaing Feature Map. Kohonen's algorithm is sometimes stranded on local minima for the initial weights. The proposed algorithm uses an object function which can evaluate the state of network in learning and adjusts the learning rate adaptively according to the evaluation of the object function. As a result, it is always guaranteed that the state of network is converged to the global minimum value and it has a capacity of generalized learning by adaptively. It is reduce that the learning time of our algorithm is about $30\%$ of Kohonen's.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.101-106
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• 2010

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.91-98
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• 2001

In this paper, center detection and motion analysis of a moving object are studied. Kohonen's self-organizing neural network models are used for the moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation. It is possible to distinguish 8 directions of a moving trajectory with two frames and 16 directions with three frames.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.6
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• pp.253-258
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• 2000

This paper presents an application of Kohonen neural networks to assess the dynamic security of power systems. The dynamic security assessment(DSA) is an important factor in power system operation, but conventional techniques have not achieved the desired speed and accuracy. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of Kohonen networks is a mapping of the pre-fault system conditions into the neurons based on the CCTs. The power flow on each line is used as the input data, and an activated output neuron has information of the CCT of each contingency. The trajectory of the activated neurons during load changes can be used in on-line DSA efficiently. The applicability of the proposed method is demonstrated using a 9-bus example.

• Journal of Navigation and Port Research
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• v.30 no.1 s.107
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• pp.45-51
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• 2006

This paper aims to analysis on evaluation factor for success of port innovative cluster. This paper is divided three factors such ac policy, source and operation In addition, three factors are divided into the twelve detail factors. From a total of 30 survey cases, 50 percent randomly selected as the training group and the other 50 percent as the validation group. cases in the training group were used in the development of the Kohonen Network The validation group was used to test the performance of this model. The major findings may be summarized as follows; The prediction accuracy rate is $73.33\%$ The weight of real root and detail factors is calculated by Kohonen Network At the result, success prediction group of port innovative cluster, this paper places the priority on the source factor.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.212-219
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• 1999

In this paper we present a neural network approach to select weighting matrices of Linear-Quadratic-Gaussian(LQG) controller for TCSC control. The selection of weighting matrices is usually carried out by trial and error. A weighting matrices of LQG control are selected effectively using Kohonen network. It is shown that simulation results in application of this method to three machine nine bus system are satisfactory.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.35-54
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• 2009

The recent economic crisis not only reduces the profit of department stores but also incurs the significance losses caused by the increasing late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened from the simple prediction of whether this customer has a good credit or not to the accurate prediction of how much profit can be gained from this customer. This study classifies the delinquent customers of credit card in a Korean department store into homogeneous clusters. Using this information, this study analyzes the repayment patterns for each cluster and develops the credit prediction system to manage the delinquent customers. The model presented by this study uses Kohonen network, which is one of artificial neural networks of data mining technique, to cluster the credit delinquent customers into clusters. Cox proportional hazard model is also used, which is one of survival analysis used in medical statistics, to analyze the repayment patterns of the delinquent customers in each cluster. The presented model estimates the repayment period of delinquent customers for each cluster and introduces the influencing variables on the repayment pattern prediction. Although there are some differences among clusters, the variables about the purchasing frequency in a month and the average number of installment repayment are the most predictive variables for the repayment pattern. The accuracy of the presented system leaches 97.5%.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.321-333
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• 2003

Self-organizing map (SOM), a unsupervised learning neural network, has been developed by T. Kohonen since 1980's. Main application areas were pattern recognition and text retrieval. Because of that, it has not been spread to statisticians until late. Recently, SOM's are frequently drawn in data mining fields. Kohonen's SOM, however, needs improvements to become a statistician's standard tool. First, there should be a good guideline as for the size of map. Second, an enhanced visualization mode is wanted. In this study, principal components self-organizing map (PC-SOM), a modification of Kohonen's SOM, is proposed to meet such needs. PC-SOM performs one-dimensional SOM during the first stage to decompose input units into node weights and residuals. At the second stage, another one-dimensional SOM is applied to the residuals of the first stage. Finally, by putting together two stages, one obtains two-dimensional SOM. Such procedure can be easily expanded to construct three or more dimensional maps. The number of grid lines along the second axis is determined automatically, once that of the first axis is given by the data analyst. Furthermore, PC-SOM provides easily interpretable map axes. Such merits of PC-SOM are demonstrated with well-known Fisher's iris data and a simulated data set.