• Journal of Korea Technology Innovation Society
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• 제7권3호
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• pp.607-640
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• 2004

This article analyzes the venture firm based upon the new framework of Dynamic Firm Capability (DFC) to identify the process mechanism. Research methodology includes the case study involving structured interview and data collection from two leading Korean ICT(Information Communication Technology) firms in the same sector (DVR). IDIS, spun off from the university, has accumulated the innovative capability based on the R&D department. On the other hand, KODICOM has retained the technological trajectory in terms of marketing competence. Underlying hypothesis is that a firm should show a idiosyncratic evolutionary pattern by acquiring different complimentary assets(CA). In addition, effective internal process should be matched with the essential characteristics not only at the firm level but also at the sectoral level. By analyzing those two different firms, we will find the strategic successful factors based upon the evolutionary point of view. It is a key contribution of this paper to study on the process mechanism of ventures, and to explain detailed process mechanism by viewing two different characteristics of the firm at the functional level.

• Korean Journal of Psychosomatic Medicine
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• 제27권1호
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• pp.1-12
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• 2019

The behavioural patterns of individuals in the same species are very diverse. The phenomenon in which different behavioural phenotypes are maintained in the same species for long time can be explained by niche specialization or frequency dependent selection, but it has not been proven yet. Especially, the high prevalence of mental illnesses as extreme behaviour patterns is one of the challenges of evolutionary psychology. From an evolutionary point of view, several frameworks for studying various patterns of behaviours or psychopathologies may be proposed. In this paper, I briefly explain animal models, personality factor models, DSM-IV multiaxial models, FSD models, and RDoC models, and discuss their advantages and disadvantages, focusing on the evolutionary approach to behavioural variation among individuals.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (C)
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• pp.399-402
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• 2007

뇌 속 뉴런들의 네트워크는 근본적으로 recurrent neural networks(RNNs)의 형태를 지닌다. 이 논문에서는 반복되는 뉴런 반응 패턴들 사이의 관계를 네트워크에 저장함으로써 생물의 기억이 생성된다는 가정하에, 이를 표현할 수 있는 RNN 모델을 제안하였고, evolutionary algorithm을 통해 이러한 여러 쌍의 기억들이 저장된 네트워크가 존재할 수 있음을 보였다.

• Journal of the Korean Society for Railway
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• 제9권1호
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• pp.76-80
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• 2006

The Optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model have been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme are utilized, respectively. As a result, two meta-models for trip time and energy consumption were constructed. The optimization to search an economical running pattern was achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• Proceedings of the KIEE Conference
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• 대한전기학회 2005년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.238-240
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• 2005

The Optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model have been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme are utilized, respectively. As a result, two meta-models for trip time and energy consumption were constructed. The optimization to search an economical running pattern was achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• Journal of the Korean Institute of Intelligent Systems
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• 제16권5호
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• pp.594-600
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• 2006

The optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model has been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme ate utilized, respectively. As a result, two meta-models for trip time and energy consumption are constructed. The optimization to search an economical running pattern is achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• Proceedings of the Zoological Society Korea Conference
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• 한국동물학회 2000년도 한국생물과학협회 학술발표대회
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• pp.67.2-68
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• 2000

No Abstract, See Full Text

• Proceedings of the Microbiological Society of Korea Conference
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• 한국미생물학회 2000년도 추계학술발표대회
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• pp.146-153
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• 2000

• Journal of the Korean Institute of Intelligent Systems
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• 제11권5호
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• pp.400-405
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• 2001

This paper addresses an effective approach of training neural networks classifiers for credit card fraud detection. The proposed approach uses evolutionary programming to trails the neural networks classifiers based on maximization of the detection rate of fraudulent usages on some ranges of the rejection rate, loot minimization of mean square error(MSE) that Is a common criterion for neural networks learning. This approach enables us to get classifier of satisfactory performance and to offer a directive method of handling various conditions and performance measures that are required for real fraud detection applications in the classifier training step. The experimental results on "real"credit card transaction data indicate that the proposed classifiers produces classifiers of high quality in terms of a relative profit as well as detection rate and efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• 제24권2호
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.