• Journal of the Korean Geotechnical Society
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• v.17 no.2
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• pp.73-83
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• 2001

본 연구에서는 전국 4개 지구의 화강암 풍화토를 연구대상으로 현장 및 실내시험을 수행하고 토량 변화율을 노상과 노체에 대하여 결정하였다. 그리고, 본 연구에서는 인공 신경망 중 오류 역전파 학습 알고리즘을 도입하여 토량 변화율 C 값을 추정하고 신경망의 적용성에 대한 검증을 수행하였다. 화강암 풍화토에 대한 실내 및 현장시험 결과에서 얻어진 토량 변화율 C 값은 노상과 노체 구분 없이 최소 0.7에서 최대 1.2정도의 넓은 범위로 나타났다. 토지공사에서 제안하는 C값의 산정식과 본 연구 결과를 비교한 결과, 토지공사의 산정식에 의한 결과가 과대 평가될 가능성이 큰 것으로 나타났다. 비중, 자연 함수비, 자연상태의 습윤단위중량, #200 통과율 그리고 균등계수의 입력변수를 갖는 $I_{5-1}$$H_{30-30}$$O_1$의 신경망에서 다른 신경망 구조들보다 잦은 지역 최소점에 수렴하는 결과를 보였다. 본 연구에서 사용한 모든 신경망 구조에서 시험결과와 신경망 결과의 상관계수는 0.9이상으로 나타나 높은 상관성을 나타내었다. 특히, 인공 신경망에 의한 예측결과는 다양한 영향인자들 중에서 비중, 자연 함수비, 자연상태의 습윤단위중량 그리고 #200 통과율의 4개 변수만으로도 C값을 예측할 수 있었으며, 상관계수는 0.96으로 나타났다.다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2C
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• pp.31-40
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• 2005

we propose an enhanced self-generation supervised algorithm that by combining an ART algorithm and the delta-bar-delta method. Form the input layer to the hidden layer, ART-1 and ART-2 are used to produce nodes, respectively. A winner-take-all method is adopted to the connection weight adaption so that a stored pattern for some pattern is updated. we test the recognition of student identification, a certificate of residence, and an identifier from container that require nodes of hidden layers in neural network. In simulation results, the proposed self-generation supervised learning algorithm reduces the possibility of local minima and improves learning speed and paralysis than conventional neural networks.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.26-31
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• 2014

In this paper, we present performance analysis of video smoke detection based on BPN-Network that is using multi-smoke feature, and Neural Network. Conventional smoke detection method consist of simple or mixed functions using color, temporal, spatial characteristics. However, most of all, they don't consider the early fire conditions. In this paper, we analysis the smoke color and motion characteristics, and revised distinguish the candidate smoke region. Smoke diffusion, transparency and shape features are used for detection stage. Then it apply the BPN-Network (Back-Propagation Neural Network). The simulation results showed 91.31% accuracy and 2.62% of false detection rate.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2003.02a
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• pp.400-405
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• 2003

오이 수확작업의 자동화를 이루기 위한 기술적 방법은 다른 작물보다 실험하기가 어렵다. 왜냐하면 첫째 오이는 한번 심은 줄기로부터 계속해서 오이가 열리기 때문에 연속적으로 관찰하여 수확하여야 하고, 둘째 넝쿨식물로써 줄기는 가늘고 잎은 크기 때문이다. 즉 수확 작업 시 줄기의 손상은 바로 수확량의 감소로 이어지고, 수확하려는 오이를 구별하기가 어렵다. 이렇게 어려운 수확작업을 자동화하기 위하여 영상처리 장치가 필요한 실정이다. (중략)

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1159-1161
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• 2000

This paper presents a speed control system of induction motor using neural network The speed control of induction motor was designed to NNC(Neural Network Controller) and NNE(Neural Network Estimator) used backpropagation, the NNE was constituted to be get an error value of output of an induction motor and conspire an input/output. NNC is controled to be made the error of reference speed and actual speed decrease, and in order to determine the weighting of NNC can be back propagated through the NNE, and it is adapted to the outside circumstances and system characters with learning ability.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.842-848
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• 2008

This paper deal with uncertainty problem by using Type-2 fuzzy logic set for nonlinear system modeling. We design Type-2 fuzzy logic system in which the antecedent and the consequent part of rules are given as Type-2 fuzzy set and also analyze the performance of the ensuing nonlinear model with uncertainty. Here, the apexes of the antecedent membership functions of rules are decided by C-means clustering algorithm and the apexes of the consequent membership functions of rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The proposed model is demonstrated with the aid of two representative numerical examples, such as mathematical synthetic data set and Mackey-Glass time series data set and also we discuss the approximation as well as generalization abilities for the model.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2188-2195
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• 1999

This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neuralnet, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Means) arithmetics, to decide whether a requested call not to be trained in learning phase to be connected or not. The system generates the estimated traffic pattern for the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmetics. The input to the NN is the vector consisted of traffic parameters which are the means and variances of the number of cells arriving in decision as to whether to accept or reject a new call depends on whether the NN is used for decision threshold(+0.5). This method is a new technique for call admission control using the membership values as traffic parameter which declared to CAC at the call set up stage, and this is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simulations, it is founded the performance of the suggested method outperforms compared to the conventional NN method.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1171-1179
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• 2004

We describe word boundary detection that extracts the boundary between speech and non-speech. The proposed method uses two features. One is the normalized root mean square of speech signal, which is insensitive to white noises and represents temporal information. The other is the normalized met-frequency band energy of voice signal, which is frequency information of the signal. Our method detects word boundaries using a recurrent fuzzy associative memory(RFAM) that extends FAM by adding recurrent nodes. Hebbian learning method is employed to establish the degree of association between an input and output. An error back-propagation algorithm is used for teaming the weights between the consequent layer and the recurrent layer. To confirm the effectiveness, we applied the suggested system to voice data obtained from KAIST.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.181-189
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• 2013

In this paper, we introduce the fuzzy neural network based on the individual input space to design the pattern recognizer. The proposed networks configure the network by individually dividing each input space. The premise part of the networks is independently composed of the fuzzy partition of individual input spaces and the consequence part of the networks is represented by polynomial functions. The learning of fuzzy neural networks is realized by adjusting connection weights of the neurons in the consequent part of the fuzzy rules and it follows a back-propagation algorithm. In addition, in order to optimize the parameters of the proposed network, we use real-coded genetic algorithms. Finally, we design the optimized pattern recognizer using the experimental data for pattern recognition.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.1
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• pp.40-49
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• 2008

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications and so of due to their excellent power to weight ratio. To obtain maximum efficiency in these applications, this paper proposes the neural network control method. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the error back propagation algorithm(EBPA) of neural network. The minimization of loss is possible to realize eHciency optimization control for the IPMSM drive. This paper proposes high performance and robust control through a real time calculation of parameter variation such as variation of back emf constant, armature resistance and d-axis inductance about the motor operation. Proposed algorithm is applied IPMSM drive system, prove validity through analysis operating characteristics con011ed by efficiency optimization control.