• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권1호
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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.

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권9호
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• pp.547-553
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• 2008

본 논문에서는 2 단계 서류 레이아웃 분할 방법을 제안한다. 서류 분할의 1 차 단계는 top-down 계열의 영역 추출로서 모폴로지 기반의 거리 함수를 사용하여 주어진 영상 데이타를 사각형 영역들로 분할한다. 거리 사상 함수를 통한 예비 결과는 성능 개선을 위한 2 차 단계의 입력 변수로 작용한다. 서류 분할의 2차 단계로서 기계 학습 이론을 적용한다. 통계 모델을 따르는 RBF 신경망을 선택하였고, 은닉 층의 설계를 위해 코호넨 네트워크의 자기 조직화 성격을 활용한 데이타 군집화 기법을 기반으로 하였다. 본 논문에서는 300개의 영상에서 추출된 영역 데이타를 통해 학습된 신경망이 1차 단계에서 도출된 예비 결과를 개선함을 연구 결과로 제시하였다.

• 대한토목학회논문집
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• 제28권3D호
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• pp.307-315
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• 2008

본 연구의 목적은 수도권 남부 국도 ITS 시범구간인 국도 3호선의 장지IC~곤지암IC구간에서 수집되는 교통자료를 기반으로 자기조직형 신경망 이론을 도입하여 국도구간의 통행시간 추정모형을 개발하는 방안을 제시하는 것이다. 지점 검지기 적정 설치위치와 구간의 연장 및 연도의 토지이용특성이 단속류의 구간통행시간에 영향을 미침을 확인하였으며, 구간 통행시간 추정을 위해 기존의 인공신경망 모형이 가지는 추가학습이 불가능하다는 단점과 신경망 구조의 최적구성이 어려운 점 등을 고려하여 자기조직형 인공신경망 구조방법을 도입하였다. 통행시간 추정결과 기존 검지기에서 수집된 자료와 최적위치에서 수집된 자료를 이용하여 모형을 검증한 결과 통행특성을 가장 잘 반영하는 지점자료를 활용한 모형의 추정력이 우수한 것으로 나타났다. 이러한 시도는 향후 국도 ITS 사업의 설계에서 검지기의 설치 위치 선정에 응용할 수 있을 것으로 기대된다.

• 로봇학회논문지
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• 제1권2호
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• pp.163-171
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• 2006

This paper introduces the research progress on the artificial brain in the Telerobotics and Control Laboratory at KAIST. This series of studies is based on the assumption that it will be possible to develop an artificial intelligence by copying the mechanisms of the animal brain. Two important brain mechanisms are considered: spike-timing dependent plasticity and dopaminergic plasticity. Each mechanism is implemented in two coding paradigms: spike-codes and rate-codes. Spike-timing dependent plasticity is essential for self-organization in the brain. Dopamine neurons deliver reward signals and modify the synaptic efficacies in order to maximize the predicted reward. This paper addresses how artificial intelligence can emerge by the synergy between self-organization and reinforcement learning. For implementation issues, the rate codes of the brain mechanisms are developed to calculate the neuron dynamics efficiently.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.19-22
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• 2000

In this paper, we present forecasting ozone concentration with decision support system. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• 제어로봇시스템학회논문지
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• 제5권5호
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• pp.600-613
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• 1999

This paper focuses on development of a performance manager for IEEE 802.4 token bus networks to serve large-scale integrated systems. In order to construct the management algorithm, the principles of fuzzy logic, genetic algorithm, and neural network have been combined to represent human knowledge and to imitate of human inference mechanism. Through the simulation experiments, it is shown that the proposed performance manager is capable of improving the network performance without a priori knowledge.

• 정보처리학회논문지B
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• 제9B권3호
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• pp.327-336
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• 2002

텍스쳐 분석은 표면, 물체, 모양, 깊이 인식 등의 많은 영상 이해 분야에서 활용되는 가장 중요한 인식 기술 중의 하나이다. 그러나 기존의 방법들은 다중 텍스쳐 영상에 내재된 텍스쳐 성분의 인식 정보를 활용할 수 없는 분할만을 목적으로 하고 있으며, 내재된 텍스쳐 인식을 기반으로 하는 비교사적인 방법에 관한 연구는 거의 이루어지고 있지 않은 실정이다. 따라서 본 논문에서는 텍스쳐 성분을 방향장(orientation-field) 특징 정보인 방향각과 방향강도로 정의하고 블록-기반 자기조직화 신경회로망에 의해서 비교사적으로 영상 내에 존재하는 텍스쳐 영역을 군화(clustering) 및 통합(merging) 처리에 의해서 식별한다. 또한 제안된 알고리즘의 성능 평가를 위해서는 다양한 형태의 다중 텍스쳐 영상을 생성하여 블록 기반의 불림(dilation) 및 윤곽 검출 과정을 통해서 영상에 내재하는 텍스쳐 영역을 분할함으로써 그 유효성을 보인다.

• 한국CDE학회논문집
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• 제14권6호
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• pp.382-389
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• 2009

Recently, knowledge management has been required in companies as a tool of competitiveness. Companies have constructed Enterprise Resource Planning(ERP) system in order to manage huge knowledge. But, it is not easy to formalize knowledge in organization. We focused on data mining system by genetic programming(GP). Data mining system by genetic programming can be useful tools to derive and extract the necessary information and knowledge from the huge accumulated data. However when we don't have enough amounts of data to perform the learning process of genetic programming, we have to reduce input parameter(s) or increase number of learning or training data. In this study, an enhanced data mining method combining Genetic Programming with Self organizing map, that reduces the number of input parameters, is suggested. Experiment results through a prototype implementation are also discussed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.887-890
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• 1993

By introducing the notion of constraint-oriented fuzzy inference, we will show that it provides us ways of fuzzy control methods that has abilities of adaptation, learning and self-organization. The basic supporting techniques behind these abilities are“hard”processing by Artificial Intelligence or traditional computational framework and“soft”processing by Neural Network or Genetic Algorithm techniques. The reason that these techniques can be incorporated to fuzzy control systems is that the notion of“constraint”itself has two fundamental properties, that is, the“modularity”property due to its declarativeness and the“logicality”property due to its two-valuedness. From the former property, the modularity property, decomposing and integrating constraints can be done easily and efficiently, which enables us to carry out the above“soft”processing. From the latter property, the logicality property, Qualitative Reasoning and Instance Generalization by Symbolic Reasoning an be carried out, thus enabling the“hard”processing.