• 기술혁신학회지
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• 제2권2호
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• pp.275-289
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• 1999

This study aims at investigating the stylized facts of the complex system products innovation in Korea. It reviewed the preceeding studies on the innovation patterns of complex system products and selected the issues to be examined in investigating Korean case. In addition, the stylized facts of innovation patterns of Korean complex system products were suggested. The peculiar characteristics of Korean cases are as follows: Korea has succeeded in integrating foreign technologies and components into commercial complex system products and in substituting the foreign system products in the domestic market. However, it faces a great difficulty in upgrading the products to the next generation. It lacks capabilities which facilitate the evolution of system products competing in the world market.

• 디자인학연구
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• 제14권4호
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• pp.137-146
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• 2001

본 연구의 목적은 진화이론을 이용하여 제품의 진화 유형을 밝히고 이를 통해 제품의 발전과정을 이해하는데 있다. 연구의 목적을 위해 진화 메커니즘을 설정하여 제품들을 이에 적용하였고, 그 결과는 '기능의 진화', '형태의 진화', '상징의 진화' 등 세 가지 진화 유형으로 나타났다. 연구 문제인 <제품은 진화하는가>, <왜 진화하는가>, <어떻게 진화하는가>에 대한 답을 찾기 위해, 본 연구는 세 단계로 진행되었다: 첫째, 선학들의 연구 결과를 통하여 제품 진화론을 정의하였다: 둘째, 이를 바탕으로 진화 메커니즘을 설정하였다: 셋째, 제품을 진화 메커니즘에 적용하여 제품의 진화 유형을 구분하였다. 연구의 결과는 정리하면 다음과 같다. ${\bullet}$본 연구에서 제시한 진화 메커니즘은 '진화의 원인'인 '필요와 즐거움', '진화의 시점'인 '결함', '진화의 원동력'인 '이익'등으로 구성된 유기적 장치이다. ${\bullet}$제품을 진화 메커니즘에 적용한 결과, 진화 유형은 '기능의 진화', '형태의 진화', '상징의 진화'등으로 나타났다.

• Industrial Engineering and Management Systems
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• 제15권2호
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• pp.181-195
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• 2016

In a product environment where various product-service systems (PSSs) are already being provided, the provision of a different type of PSS is difficult for second movers but necessary for their sustainability and differentiation. Despite the importance of providing distinguishing PSSs to market, prior PSS studies have not effectively considered the influence of existing PSSs in their methods. In response, we suggest an approach to generate new PSS concepts by employing general needs (GNs) and business system evolution patterns (BSEPs). Our approach 1) identifies customer GNs fulfilled by existing PSSs, 2) generates advanced PSS ideas from an evolutionary perspective by mapping the existing PSSs onto BSEPs, and 3) selects PSS ideas that can meet the unfulfilled or insufficiently considered GNs using a GN-PSS linking matrix, thereby generating new PSS concepts based on the selected ideas. The workings and practicability of this approach are illustrated using a PSS case study of furniture industry. This approach would provide PSS planners with an ability to generate the differentiated PSS concepts that handle the customer needs that have been untapped throughout a product's lifecycle. In addition, this approach as a module will have a synergetic effect when incorporated with other PSS methodologies.

• 전자공학회논문지S
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• 제36S권1호
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• pp.29-37
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• 1999

일반적으로 신경망의 정보처리 능력은 신경망의 구조와 효율적인 학습패턴에 의해 결정된다. 그러나 아직까지 체계적으로 신경망의 구조를 설계하거나 효율적인 학습패턴을 선택하는 방법은 없다. 한편 진화 알고리즘은 개체군을 이용한 탐색법으로 전역적 최적해를 구하는 데 많이 사용되고 있으며, 특히 최적의 시스템을 설계하고자 할 때 매우 유용한 방법이다. 본 논문에서는 유전자 알고리즘으로 구성된 두 개의 개체군이 서로 경쟁적으로 진화하는 공진화 방법에 의해 최적의 신경망구조를 찾는 방법을 제안한다. 이 방법은 신경망구조를 나타내는 주개체군과 학습패턴을 나타내는 부개체군으로 되어 있으며, 이 두 개체군(신경망과 학습패턴)은 서로 경쟁적으로 진화한다. 즉, 학습패턴은 신경망이 학습하기 힘든 패턴으로 진화하고 신경망은 그 패넌들을 학습할 수 있도록 진화하단. 이 방법은 부적절한 학습패턴의 선택과 임의적인 신경망의 설계로 인한 시스템의 성능이 저하되는 것을 해결한다. 또한 공진화 방법에서 각 개체군의 적합도는 동적으로 변화하기 때문에 그 진행과정을 쉽게 알 수 없다. 따라서 본 논문에서는 그 진행과정을 관찰할 수 있는 방법도 소개한다. 마지막으로 제안한 방법을 로봇 매니플레이터의 비주얼 서보임 문제에 적용하여 그 유효성을 검증한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.111-114
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• 1997

Usually, Evolutionary Algorithms are considered more efficient for optimal system design, However, the performance of the system is determined by fitness function and system environment. In this paper, in order to overcome the limitation of the performance by this factor, we propose a co-evolutionary method that two populations constantly interact and coevolve. In this paper, we apply coevolution to neural network's evolving. So, one population is composed of the structure of neural networks and other population is composed of training patterns. The structure of neural networks evolve to optimal structure and, at the same time, training patterns coevolve to feature patterns. This method prevent the system from the limitation of the performance by random design of neural network structure and inadequate selection of training patterns. In this time neural networks are trained by evolution strategies that are able to apply to the unsupervised learning. And in the coding of neural networks, we propose the method to maintain nonredundancy and character preservingness that are essential factor of genetic coding. We show the validity and the effectiveness of the proposed scheme by applying it to the visual servoing of RV-M2 robot manipulators.

• 한국통신학회논문지
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• 제34권5A호
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• pp.421-428
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• 2009

Inter-cell coordination has been an emerging issue for mitigating inter-cell interference in broadband wireless access networks such as IEEE802.16 and 3GPP LTE (Long Term Evolution). This paper proposes uplink/downlink hybrid inter-cell coordination patterns for a TDD (Time Division Duplex)/MC-CDMA (Multi-Carrier Code Division Multiple Access) system. For the performance analysis, closed forms of inter-cell interferences are derived when uplink and downlink transmissions coexist over a multi-cell environment. In the analysis, we find an optimal ratio of downlink transmit powers of BSs (Base Stations) based on the target outage probability and the performance according to ratios of uplink/downlink transmit powers of MSs (Mobile Stations)/BSs is explored. Our numerical results show that interference mitigation utilizing the characteristics of the uplink and downlink power ratio is very effective in improving system performance in terms of QoS.

• 한국실내디자인학회논문집
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• 제19권6호
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• pp.100-111
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• 2010

'Pattern' is the term that is frequently used in the aspects of history, society, and science. It always appears in the remains or relics of the age of civilization when recording was started, and its evaluation and value differ by time. Patterns in the ancient civilization were symbolic, social, and spatially crucial. However, after the modernization, they were considered to be immoral and unnecessary, so the range of their significance came to reduce. Due to the development of science, ornament patterns lost the limitation of its range of use along with new interpretation of them. Especially with the advent of new scientific theories such as the evolution theory from the biological aspect, quantum mechanics, and super string theory, morphological possibilities more than the human scale perceived by men came to be discovered. Living organisms maintain their lives through patterns, structures, and processes in order to produce a system alive. Among them, patterns are the organization of relations determining the characteristics of the system. The present patterns may correspond to this meaning. The pattern in a space is the matter of how to relate the components after all. In a space, however, there are numerous components mingled with one another. If these tasks are conducted as analogue work, it will take a lot of time and effort. However, if digital media are utilized to perform the tasks like analysis, generation, or fabrication, it will produce a result with higher precision and efficiency. In this sense, parametric modeling is quite useful media. Opening morphological variation, it realizes more possibilities, connects conveniently the relations between complex components composing a space, and helps produce creative patterns.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2193-2196
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• 1998

Cellular automata are dynamical systems in which space and time are discrete, where each cell has a finite number of states and updates its states by interactive rules among the cell-neighborhood. From the characteristics of self-reproduction and self- organization, it is possible to create a neural network which has the specific patterns or structures dynamically. CAM-Brain is a kind of such neural network system which evolves its structure by adopting evolutionary computations like genetic algorithms (GA). In this paper, we suggest the evolution strategies for the structure of neural networks based on cellular automata.

• 한국실내디자인학회논문집
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• 제21권2호
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• pp.35-45
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• 2012

The development of digital media made the change of architectural paradigm from tectonic to the surface and pattern. This means the transition to the new kind of materiality and the resurrection of ornament. This study started as an aim to apply biological pattern to architectural design from the new perception of pattern. Architectural patterns in the early era appeared as ladders, steps, chains, trees, vortices. But since 21st century, we can find patterns in nature like atoms and molecular structures, fluid forms of dynamics and new geometrical pattern like fractal and first of all biological patterns like viruses and micro-organisms, Voronoi cells, DNA structure, rhizomes and various hybrids and permutations of these. Pattern became one of the most important elements and themes of contemporary architecture through the change of materiality and resurrection of ornament with the new perception of surface in architecture. One of the patterns that give new creative availability to the architectural design is biological pattern which is self-organized as an optimum form through interaction with environment. Biological patterns emerge mostly as self-replicating patterns through morphogenesis, certain geometrical patterns(in particular triangles, pentagons, hexagons and spirals). The architectural application methods of biological patterns are direct figural pattern of organism, circle pattern, polygon pattern, energy-material control pattern, differentiation pattern, parametric pattern, growth principle pattern, evolutionary ecologic pattern. These patterns can be utilized as practical architectural patterns through the use of computer programs as morphogenetic programs like L-system, MoSS program and genetic algorithm programs like Grasshoper, Generative Components with the help of computing technology like mapping and scripting.

• 대한교통학회지
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• 제32권5호
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• pp.522-530
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• 2014

교통관리센터에 축적되어 있는 속도 이력데이터에는 반복 비반복 정체 시공간 전개에 대한 상세한 정보가 모두 들어있으나, 도해법에 의해 다루어져 왔기 때문에 많은 양의 이력데이터를 처리하여 교통상황예측이나 정보제공에 활용할 수 없는 한계가 존재하였다. 본 논문에서는, 기존의 Classification과 Density-Based Clustering 알고리즘을 속도 시공간 데이터 특성에 맞게 조합하고 변형하여 정체 시공간 영역을 자동 인식하는 알고리즘과, 정체파급길이, 파급속도, 해소속도 등 정체 시공간 전개 패턴의 특성치를 산정하는 알고리즘을 개발하였다, 본 알고리즘은, 교통관리센터에 축적되어 있는 방대한 양의 이력데이터를 자동으로 분석하여 자세한 정체 관련 정보를 추출할 수 있고, 산정된 특성치를 가지고 각 센터의 필요에 따라 다양한 정보를 2차 생성하고 활용할 수 있는 장점이 있다. 본 연구결과는 향후 반복 비반복 정체에 대한 예측과 대응이 획기적으로 개선되는데 초석이 될 것으로 기대된다.