• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.95-115
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• 2001

We propose classifying the venture firms into four types of knowledge management. For this purpose, we collected questionnaire data from 101 venture firms listed in KOSDAQ, and applied a unsupervised neural network algorithm SOM to obtain four clusters representing knowledge management types-High Tech Type, Organizational Knowledge Type, Information Technology Type, and Beginner Type. Based on the results, we conclude that the venture firms listed in KOSDAQ should first know its own knowledge management type, and then apply appropriate strategies to take advantage of the knowledge management impacts on the competitiveness.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.713-718
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• 2003

The CONDENSATION (Conditional Density Propagation) algorithm has a robust tracking performance and suitability for real-time implementation. However, the CONDENSATION tracker has some difficulties with real-time implementation for multiple people tracking since it requires very complicated shape modeling and a large number of samples for precise tracking performance. Further, it shows a poor tracking performance in the case of close or partially occluded people. To overcome these difficulties, we present three improvements: First, we construct effective templates of people´s shapes using the SOM (Self-Organizing Map). Second, we take the discrete HMM (Hidden Markov Modeling) for an accurate dynamical model of the people´s shape transition. Third, we use the competition rule to separate close or partially occluded people effectively. Simulation results shows that the proposed CONDENSATION algorithm can achieve robust and real-time tracking in the image sequences of a crowd of people.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.499-507
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• 2006

Recently human gait has been considered as a useful biometric supporting high performance human identification systems. This paper proposes a view-based pedestrian identification method using the dynamic silhouettes of a human body modeled with the Hidden Markov Model(HMM). Two types of gait models have been developed both with an endless cycle architecture: one is a discrete HMM method using a self-organizing map-based VQ codebook and the other is a continuous HMM method using feature vectors transformed into a PCA space. Experimental results showed a consistent performance trend over a range of model parameters and the recognition rate up to 88.1%. Compared with other methods, the proposed models and techniques are believed to have a sufficient potential for a successful application to gait recognition.

• Knowledge Management Research
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• v.13 no.5
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• pp.43-64
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• 2012

Emerging technologies have significant implications in establishing competitive advantages and are characterized by continuous rapid development. Efficient benchmarking is more and more important in the development of emerging technologies. Similar input level and importance are two necessary criteria need to be considered for emerging technology's benchmarking. In this study, we proposed a sequential use of self-organizing map(SOM), data envelopment analysis(DEA) and analytical hierarchy process(AHP) method for the stepwise benchmarking of emerging technology. The proposed method uses two-level SOM to cluster the emerging technologies with similar required input levels together, then, in each cluster, uses DEA-BCC model to evaluate the efficiencies of the emerging technologies and do tier analysis to form tiers. On each tier, AHP rating method is used to calculate each emerging technology's importance priority. The optimal benchmarking path of each cluster is established by connecting the emerging technologies with the highest importance priority. In order to validate the proposed method, we apply it to a case of biotechnology. The result shows the proposed method can overcome difficulties in benchmarking, select suitable benchmarking targets and make the benchmarking process more efficient and reasonable.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.162-164
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• 2005

This paper presents an hour-ahead System Marginal Price (SMP) forecasting framework based on a neural network. Recently, the deregulation in power industries has impacted on the power system operational problems. The bidding strategy of market participants in energy market is highly dependent on the short-term price levels. Therefore, short-term SMP forecasting is a very important issue to market participants to maximize their profits. and to market operator who may wish to operate the electricity market in a stable sense. The proposed hybrid neural network is composed of tow parts. First part of this scheme is pattern classification to input data using Kohonen Self-Organizing Map (SOM) and the second part is SMP forecasting using back-propagation neural network that has three layers. This paper compares the forecasting results using classified input data and unclassified input data. The proposed technique is trained, validated and tested with historical date of Korea Power Exchange (KPX) in 2002.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.2
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• pp.9-16
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• 2003

We proposed a method of a virtual robot arm controlled by the EMG pattern recognition using an improved SOFM method. The proposed method is simple in that the EMG signals are used as SOFM's input directly without preprocessing but nevertheless input patterns are reliably classified and then used for fuzzy logic systems to automatically tune the neighborhood and the learning rate. In order to verify the effectiveness of the proposed method, we experimented on EMG pattern recognition of 6 movements from the shoulder, wrist, and elbow. Experimental results show that the proposed SOFM method has 21.7% higher recognition rate than the general SOFM method, the average number of learning iterations has been decreased, and then the virtual robot arm is controlled by EMG pattern recognition.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.2
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• pp.119-127
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• 2004

Butterfly valves are popularly used in service in the industrial and water works pipeline systems with large diameter because of its lightweight, simple structure and the rapidity of its manipulation. Sometimes cavitation can occur. resulting in noise, vibration and rapid deterioration of the valve trim, and do not allow further operation. Thus, the monitoring of cavitation is of economic interest and is very importance in industry. This paper proposes a condition monitoring scheme using statistical feature evaluation and support vector machine (SVM) to detect the cavitation conditions of butterfly valve which used as a flow control valve at the pumping stations. The stationary features of vibration signals are extracted from statistical moments. The SVMs are trained, and then classify normal and cavitation conditions of control valves. The SVMs with the reorganized feature vectors can distinguish the class of the untrained and untested data. The classification validity of this method is examined by various signals that are acquired from butterfly valves in the pumping stations and compared the classification success rate with those of self-organizing feature map neural network.

• Proceedings of the KIEE Conference
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• 2005.11a
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• pp.9-11
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• 2005

본 연구는 GIS 진단신뢰성 향상기술 개발을 목적으로, 16개의 인위적 결함을 이용하여 부분방전 신호를 발생시키고 검출하여 그 패턴인식 확률을 높이기 위하여 신경망에 Genetic Algorithm (GA) 을 적용하였다. 이를 위하여 다음과 같은 5가지 서로 다른 신경망 모델을 선택하였다: Back Propagation (BP), Jordan-Elman Network (JEN), Principal Component Analysis (PCA), Self-Organizing Feature Map (SOFM) 및 Support Vector Machine (SVM). 이와 같이 선택된 모델에 동일한 데이터를 학습 시키고 패턴인식 확률을 비교 및 분석하였다. 실험 결과에 의하면, BP의 인식률이 가장 높고 다음으로 JEN의 인식률이 높이 나타났으며, 후자의 경우 모든 결함에 대하여 정확한 패턴분류를 한 반면에 전자의 경우 1.8% 의 분류 오차가 발생하였다. 따라서 인식률이 높은 신경망이 더 정확한 패턴분류를 보장하지 못한다는 실험적 결과를 고려 할 때, 인식률이 높은 두 개의 모델을 선정하여 각각의 출력에 일정한 가중치를 주고 합산하여 새로운 출력을 얻는 방법을 제안한다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.586-587
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• 2008

지난 논문 "Critical Peak Pricing 요금제를 이용한 일반 수용가 대상 수요관리의 방법" 및 그 후속 연구에서는 일반 수용가를 대상으로 한 효율적인 수요관리의 한 방법으로써 Critical Peak Pricing 요금제를 제안하였다[1]. 또한 이 요금제에서 핵심이 되는 최적 critical peak 시점을 푸는 하위 문제들 및 방법론을 제시하였는데, 이 논문에서는 그 하위 문제들 중 수용가의 부하를 예측하는 문제를 다룬다. 우리는 energy service provider(ESP)가 관리해야 할 수용가의 수가 매우 많다는 점에 주목하여, 각 수용가의 1일 부하 사용량 패턴을 몇 개의 그룹으로 나누어 각 그룹에 대해 critical peak 최적 시점을 결정하는 연구를 수행하였다. 이러한 수용가 부하량 패턴 그룹화를 위해 인공 지능의 여러 기법 중 하나인 self-organizing map(SOM)을 사용하였다.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.222-224
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• 2007

Social participation of the elderly and the disabled continuously becomes more active due to the improvement of social systems and technological development. Various systems such as intelligence robots and intelligence home systems have been developed to support their social participation, and those systems obviously contribute to the independent lives of the elderly and the disabled. Those systems, however, usually require special hardware, which make them very expensive. Considering the economic difficulties of the users, the problem should be tackled with software-oriented approaches using existing hardware such as laptops. The software should be adapted to users with limited capabilities by enabling them to utilize the system without much knowledge related to computers and also without keyboards and mice. This paper suggests software-oriented approaches to solve those problems with the description of the unified user interface for a special work chair, and introduces an actual development procedure of the program together with real application of related theories.