• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.3
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• pp.113-127
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• 1993

In this study, a substitution model of the evolutionary generations of technological products is presented. The purpose of the model is to examine the demand side mechanisms which generate successive product life cycles along the path of technological improvements. In the model, the nature of substitution processes is summarized dto the demand function which is derived from the consumer's udtility maximization problem. To describe the nature of technological substitution processes, the concept of the vertical differentiation and the consumption externalities are considered in the utility function. The former is used to characterize the result of technological improvement and the latter is used in explaining the inertia of demand. To show the validity of the model, an empirical study is carried out using the data of the world DRAM market.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.297-300
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• 2005

In this study, we proposed genetically optimized self-organizing fuzzy polynomial neural network based on information granulation and evolutionary algorithm (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structural Iy and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.

• Journal of The Korean Astronomical Society
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• v.13 no.1
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• pp.9-14
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• 1980

The abundances of simple molecules are examined in terms of the time-dependent cloud evolution. The formation and destruction mechanisms of $H_2CO$ are reviewed. The average value of the fractional abundance of $H_2CO$ is derived to be in the range of $10^{-10}\;to\;5{\times}10^{-9}$. This is comparable to the observed values. The expected variations of the molecules formed from or destroyed by CO, CI, and $C^+$ whose abundances depend on the evolutionary state of the cloud are discussed.

• Genomics & Informatics
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• v.12 no.3
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• pp.98-104
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• 2014

Approximately 45% of the human genome is comprised of transposable elements (TEs). Results from the Human Genome Project have emphasized the biological importance of TEs. Many studies have revealed that TEs are not simply "junk" DNA, but rather, they play various roles in processes, including genome evolution, gene expression regulation, genetic instability, and cancer disposition. The effects of TE insertion in the genome varies from negligible to disease conditions. For the past two decades, many studies have shown that TEs are the causative factors of various genetic disorders and cancer. TEs are a subject of interest worldwide, not only in terms of their clinical aspects but also in basic research, such as evolutionary tracking. Although active TEs contribute to genetic instability and disease states, non-long terminal repeat transposons are well studied, and their roles in these processes have been confirmed. In this review, we will give an overview of the importance of TEs in studying genome evolution and genetic instability, and we suggest that further in-depth studies on the mechanisms related to these phenomena will be useful for both evolutionary tracking and clinical diagnostics.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2891-2893
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• 2005

In this paper, we introduce a new category of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. The conventional SOFPNN algorithm leads to a tendency to produce overly complex networks as well as a repetitive computation load by the trial and error method and/or the a repetitive parameter adjustment by designer. In order to generate a structurally and parametrically optimized network, such parameters need to be optimal. In this study, in solving the problems with the conventional SOFPNN, we introduce a new design approach of evolutionary optimized SOFPNN. Optimal parameters design available within FPN (viz. the no. of input variables, the order of the polynomial, input variables, and the no. of membership function) lead to structurally and parametrically optimized network which is more flexible as well as simpler architecture than the conventional SOFPNN. In addition, we determine the initial apexes of membership functions by genetic algorithm.

• Journal of Korea Game Society
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• v.12 no.4
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• pp.3-12
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• 2012

This study discusses how players make the mutually cooperative mechanism in Social Games. In SNG, many players exchange helps each other. That mechanism is not only one-off but also repetitive process. In the perspective of reciprocity in Evolutionary psychology and Mythology, this study analyzes mutual cooperation in several game texts most well known in the SNG field. According to the field study results, four cooperative mechanisms were extracted. These 4 principles apply to every digital game design for emerging of cooperative storytelling among players.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.809-817
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• 2008

Program plagiarism is widespread due to intelligent software and the global Internet environment. Consequently the detection of plagiarized source code and software is becoming important especially in academic field. Though numerous studies have been reported for detecting plagiarized pairs of codes, we cannot find any profound work on understanding the underlying mechanisms of plagiarism. In this paper, we study the evolutionary process of source codes regarding that the plagiarism procedure can be considered as evolutionary steps of source codes. The final goal of our paper is to reconstruct a tree depicting the evolution process in the source code. To this end, we extend the well-known bioinformatics approach, a local alignment approach, to detect a region of similar code with an adaptive scoring matrix. The asymmetric code similarity based on the local alignment can be considered as one of the main contribution of this paper. The phylogenetic tree or evolution tree of source codes can be reconstructed using this asymmetric measure. To show the effectiveness and efficiency of the phylogeny construction algorithm, we conducted experiments with more than 100 real source codes which were obtained from East-Asia ICPC(International Collegiate Programming Contest). Our experiments showed that the proposed algorithm is quite successful in reconstructing the evolutionary direction, which enables us to identify plagiarized codes more accurately and reliably. Also, the phylogeny construction algorithm is successfully implemented on top of the plagiarism detection system of an automatic program evaluation system.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.