• 한국정밀공학회지
• /
• 제19권11호
• /
• pp.83-88
• /
• 2002

In order to predict the moldability of a injection molded part, a simulation of filling is needed. Short shot is one of the most frequent troubles encountered during injection molding process. The adjustment of process conditions is the most economic way to troubleshoot the problematic short shot in cost and time since the mold doesn't need to be modified at all. But it is difficult to adjust the process conditions appropriately in no times since it requires an empirical knowledge of injection molding. In this paper, the intelligent CAE system synergistically combines fuzzy-neural network (FNN) for heuristic knowledge with CAE programs for analytical knowledge. To evaluate the intelligent algorithms, a cellular phone flip has been chosen as a finite element model and filling analyses have been performed with a commercial CAE software. As the results, the intelligent CAE system drastically reduces the troubleshooting time of short shot in comparison with the experts' conventional methodology which is similar to the golden section search algorithm.

• 대한안전경영과학회:학술대회논문집
• /
• 대한안전경영과학회 2000년도 춘계학술대회
• /
• pp.111-123
• /
• 2000

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences. Recycling cells are formed considering design, process and usage attributes. In this paper, a novel approach to the design of cellular recycling system is proposed, which deals with the recycling cell formation and assignment of identical products concurrently. Fuzzy clustering algorithm and Fuzzy-ART neural network are applied to describe the states of disposal product with the membership functions and to make recycling cell formation. This approach leads to recycling and reuse of the materials, components, and subassemblies and can evaluate the value at each cell of disposal products. Application examples are illustrated by disposal refrigerators, compared fuzzy clustering with Fuzzy-ART neural network performance in cell formation.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 하계학술대회 논문집 G
• /
• pp.2183-2185
• /
• 1998

In this paper, we propose a method of designing neural networks using biological inspired developmental and evolutionary concept. The living things are best information processing system in themselves. One individual is developed from a generative cell. And a species of this individual have adapted itself to the environment by evolution. Ontogeny of organism is embodied in cellular automata and phylogeny of species is realized by evolutionary algorithms. The connection among cells is determined by a rule of cellular automata. In order to obtain the best neural networks in the environment, we evolve the arrangement of initial cells. The cell, that is neuron of neural networks, is modeled on chaotic neuron with firing or rest state like biological neuron. A final output of network is measured by frequency of firing state. The effectiveness of the proposed scheme is verified by applying it to navigation problem of robot.

• ETRI Journal
• /
• 제27권3호
• /
• pp.294-303
• /
• 2005

In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. ML-CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas-Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.

• 한국항행학회논문지
• /
• 제21권4호
• /
• pp.347-352
• /
• 2017

본 논문에서는 셀룰러 기반 무선 인지망에서 스펙트럼 인지(CR)기술을 이용하여 모바일 사용자의 핸드오버 호의 손실확률을 줄이는 방법을 제안한다. 제안한 방법에서는 모바일이 방문할 셀을 Ziv-Lempel 알고리듬을 이용하여 예측하고, 방문할 셀에 할당된 채널이 부족할 때는 CR기술에 기초한 스펙트럼 홀 자원을 예측하여 모바일 사용자를 지원한다. 스펙트럼 홀 자원의 크기는 신경망기법으로 예측하며, 예측된 스펙트럼 홀 자원은 핸드오버 호가 초기 발생 호 보다 우선하여 사용할 수 있게 한다. 시뮬레이션을 통하여 셀룰러 이동 통신망에 CR기술을 사용함으로써 모바일 사용자의 핸드오버 호 손실확률을 줄일 수 있음을 보인다.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제52권9호
• /
• pp.540-546
• /
• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• 전자공학회논문지S
• /
• 제35S권11호
• /
• pp.88-95
• /
• 1998

본 논문은 인공생명 기법을 이용하여 생물의 정보처리 시스템을 구현하고자 하는 것이다. 자연계의 생물은 그 자체로 훌륭한 정보처리 시스템이다. 생물체는 하나의 생식 세포로부터 발생된다. 또한 이 개체의 종은 진화의 과정을 통해 환경에 적응한다. 본 논문에서는 이와 같은 생물학적인 발생과 진화의 개념을 이용하여 신경망을 설계하는 방법을 제안한다. 생물체의 개체발생은 발생모델의 하나인 셀룰라 오토마다(CA)를 통하여 구현하였고 진화과정은 진화 알고리즘(EAs)을 사용하였다. 우리는 이와 같이 구현한 '진화하는 셀룰라 오토마타 신경망'을 줄여서 ECANS1이라 명명하였다. 셀 사이의 연결은 CA 법칙에 의하여 결정되며, 셀의 초기 패턴이 진화함으로써 유용한 신경망을 찾아낸다. 신경망의 각 셀 즉 뉴런은 생물의 발화 ${\cdot}$ 비발화의 특성을 갖는 카오스 뉴런 모델을 사용하였다. 그리고 신경마의 최종 출력값은 뉴런의 발화 빈도로서 나타내었다. 제안한 방법은 Exclusive-OR 문제 및 패리티 문제에 적용함으로써 그 유효성을 검증하였다.

• 한국지능시스템학회논문지
• /
• 제13권2호
• /
• pp.154-162
• /
• 2003

셀룰라 신경회로망의 연상 메모리를 이용하여 시각적인 입력 데이터의 연산을 통하여 영상 패턴의 분류와 인식을 수행한다. 셀룰라 신경회로망은 일반적인 신경회로망과 같이 비선형 데이터의 실시간 처리가 가능하고, 세포자동자와 같이 이 격자구조의 셀로 이루어져 인접한 셀과 직접 정보를 주고받는다. 응용 분야로는 최적화, 선형/비선형화, 연상 메모리, 패턴인식, 컴퓨터 비전 등에 적용할 수 있다. 영상의 이미지 픽셀을 셀룰라 신경회로망의 셀에 대응하여 전체 이미지 영상을 모든 셀룰라 신경회로망의 셀에서 동시에 병렬로 처리할 수 있어 2-D 이미지 처리에 적합하다. 본 논문은 셀룰라 신경회로망에 의한 연상 메모리 구조를 설계하고, 학습된 하중값 메모리에서 가장 적당한 하중값을 선택하여 학습된 영상과 정확히 일치하는 출력을 얻는 방법을 제시한다. 학습을 통한 연상 메모리 구현에는 각각의 뉴런에서 일정하지 않은 다른 템플릿을 사용한다. 각각의 템플릿은 뉴런들 간의 연결 하중값을 나타내고 학습에 따라 갱신된다. 학습방법으로는 템플릿 하중값 학습에 뉴런들 간의 연결 하중값을 조정하는 가장 단순한 규칙인 Hebb의 학습방법이 사용되었고 분류값 학습에 LMS 알고리즘이 사용되었다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제4권1호
• /
• pp.84-89
• /
• 2004

The machine cell formation problem is the problem to group machines into machine families and parts into part families so as to minimize bottleneck machines, exceptional parts, and inter-cell part movements in cellular manufacturing systems and flexible manufacturing systems. This paper proposes a new machine cell formation method based on the adaptive Hamming net which is a kind of neural network model. To show the applicability of the proposed method, it presents some experiment results and compares the method with other cell formation methods. From the experiments, we observed that the proposed method could produce good cells for the machine cell formation problem.

• 대한안전경영과학회지
• /
• 제6권2호
• /
• pp.187-197
• /
• 2004

The recycling cell formation problem means that disposal products are classified into recycling product families using group technology in their end-of-life phase. Disposal products have the uncertainties of product condition usage influences. Recycling cells are formed considering design, process and usage attributes. In this paper, a new approach for the design of cellular recycling system is proposed, which deals with the recycling cell formation and assignment of identical products concurrently. Fuzzy ART neural networks are applied to describe the condition of disposal product with the membership functions and to make recycling cell formation. The approach leads to cluster materials, components, and subassemblies for reuse or recycling and can evaluate the value at each cell of disposal products. Disposal refrigerators are shown as an example.