• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(2)
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• pp.36-39
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• 2000

The adaptive learning circuit is designed the basis of modeling of MFSFET (Metal-Ferroelectric-Semiconductor FET) and the numerical results is analyzed. The output frequency of the adaptive learning circuit is inversely proportioned to the source-drain resistance of MFSFET and the capacitance of the circuit. The output frequency modulation of the adaptive learning circuit is investigated by analyzing the source-drain resistance of MFSFET as functions of imput pulse numbers in the adaptive learning circuit and the dimensionality factor of the ferroelectric thin film. From the results, the frequency modulation characteristics of the adaptive learning circuit, that is, adaptive learning characteristics which means a gradual frequency change of output pulse with the progress of input pulse are confirmed.

• Computers and Concrete
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• 제22권4호
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• pp.355-363
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• 2018

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.

• 전력전자학회논문지
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• 제4권2호
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• pp.192-200
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• 1999

위성의 전지는 위성의 수명과 직접적인 영향을 갖고 있으며 이것의 정상동작여부에 따라 위성의 안정적인 임무수행여부가 결정된다. 상대적으로 일반화된 셀 모델링의 최근 개발은 NiH2셀의 동특성을 시뮬레이션 하기 위한 기본적인 원리에 기반을 둔 접근방식이다. 그러나 이러한 일반적인 방정식을 통해 비선형성과 전력상태를 포함하는 전지 특성을 예측하는 것은 사실상 불가능하다. 본 연구에서는 다층신경회로망을 이용하여 비선형 특성를 갖는 니켈-하이드로진 전지 특성을 모델링 하였으며, 모델링된 상수값은 위성의 식시간 동안의 전지 전력상태 분석을 위해 사용되었다. 모델링 결과의 정확성을 확인하기 위해 니켈-하이드로진 전지시험결과 분석자료와 비교 검토 되었다. 전지 동작모드는 정상동작모드와 실패모드로 나누어 분석되었다. 정상동작모드는 위성의 식시간 동안 아크젯 동작 여부에 의해 각각 분석되었으며, 또한 태양전지와 배터리 셀 일부의 고장으로 인한 실패모드에서의 전지전력상태가 분석되었다.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.529-538
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a riven large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.

• 한국멀티미디어학회논문지
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• 제15권4호
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• pp.417-424
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• 2012

In this paper, we propose an automatic system that recognizes dynamic human gestures activity, including Arabic numbers from 0 to 9. We assume the gesture trajectory is almost in a plane that called principal gesture plane, then the Least Squares Method is used to estimate the plane and project the 3-D trajectory model onto the principal. An improved FNN model combined with HMM is proposed for dynamic gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network. The proposed algorithm shows that satisfactory performance and high recognition rate.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.527-529
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• 2006

In this paper, we find a robot's path using a Virtual Reality Modeling Language and overlay vision. For correct robot's path we describe a method for localizing a mobile robot in its working environment using a vision system and VRML. The robt identifies landmarks in the environment, using image processing and neural network pattern matching techniques, and then its performs self-positioning with a vision system based on a well-known localization algorithm. After the self-positioning procedure, the 2-D scene of the vision is overlaid with the VRML scene. This paper describes how to realize the self-positioning, and shows the overlap between the 2-D and VRML scenes. The method successfully defines a robot's path.

• 한국정보시스템학회지:정보시스템연구
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• 제20권3호
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• pp.1-24
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• 2011

Recently, six sigma has been widely adopted in a variety of industries as a disciplined, data-driven problem solving approach or methodology supported by a handful of powerful statistical tools in order to reduce variation through continuous process improvement. Also, data mining has been widely used to discover unknown knowledge from a large volume of data using various modeling techniques such as neural network, decision tree, regression analysis, etc. This paper proposes a six sigma methodology based on data mining for effectively and efficiently processing massive data in driving six sigma projects. The proposed methodology is applied in the hot stove system which is a major energy-consuming process in a "P" steel company for improvement of heat efficiency through reduction of energy consumption. The results show optimal operation conditions and reduction of the hot stove energy cost by 15%.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1998년도 추계종합학술대회
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• pp.202-207
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• 1998

본 논문에서는 유전자 알고리즘을 이용하여 불확실한 비선형 시스템의 퍼지-신경 회로망 모델링을 제안하였다. 제안한 퍼지-신경 회로망 모델링을 위한 학습 알고리즘은 다음과 같은 세 단계로 나누어 진행한다. 첫 번째 단계에서는 퍼지 모델의 소속 함수의 중심간과 표준편차를 구하여 초기 퍼지소속 함수를 결정한다. 두 번째 단계에서는 새로운 알고리즘을 통하여 언어적 퍼지 규칙을 만든다. 마지막 세 번째 단계에서는 유전자 알고리즘을 이용하여 중심값과 표준편차를 최적화함으로써 퍼지 모델의 소속 함수를 조절한다. 제안된 유전자 알고리즘의 장점은 흔히 신경 회로망에서 널리 쓰이는 역전파 알고리즘이 갖는 지역 최소점에 빠지는 현상이 없다는 것이다. 제안한 알고리즘의 유용성을 확인하기 위하여 일반적으로 가장 많이 쓰이는 비선형 시스템에 대하여 시뮬레이션 하여 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.513-515
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• 1999

In this paper, Neuro-Fuzzy controller which has the characteristic of Fuzzy control and artificial Neural Network is designed A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which are created by the expert. In order to adaptivity, the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in Neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of Dual mode Method. To test the effectiveness of the algorithm designed above the experiment for DC servo motor with variable load as variable load plant is implementation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.484-487
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• 1992

To resolve the knowledge acquisition bottleneck in the expert systems, the connectionist expert systems have been proposed, which facilitate learning capability of neural networks. This paper is to modify Gallant's connectionist expert network so that it can be applied to more general problems : 1) The hidden nodes are added between the input nodes and an output node, so that the back propagation learning algorithm is used instead of perception based Pocket algorithm. 2) Inference engine is thus modified by modeling that a node may have uncertainties due to unknown inputs.