• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1173-1176
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• 1996

Multilayer feedforward networks may be applied to identify the deterministic relationship between input and output data. When the results from the network require a high level of assurance, considering of the stochastic relationship between the data may be very important. The variance is one of the useful parameters to represent the stochastic relationship. This paper presents a new algorithm for a multilayer feedforward network to learn the variance of dispersed data without preliminary calculation of variance. In this paper, the network with this learning algorithm is named as a variance learning neural network(VALEAN). Computer simulation examples are utilized for the demonstration and the evaluation of VALEAN.

• 전자공학회논문지S
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• 제34S권1호
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• pp.1-8
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• 1997

A wide class of networking application services, such as video teleconferencing, VOD, LAN bridging, and distributed data processing require multipoint communications. The essential component inteh network to achieve this is a multicast packet switch which is capable of packet replication and switching. In this paper, we propose an efficient mukticast addressing scheme using the smallest number of routing bits which is deterministic lower bound. The new scheme performs all point-to-multipoint connection in radix-2 tree ATM switch like banyan network. Also, we provide a simple radix-2 switch block diagram for achieving our algorithm. And we investigate several addressing schemes for implementing multicasting in radix-r tree ATM switch and evaluate several performance factors, such as complexity of the additional header bits, requirement of the internal speedup and complexity of the major hardware.

• 한국통신학회논문지
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• 제22권7호
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• pp.1419-1426
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• 1997

A new SCEE(Syndrome Check Error Estimation) decoding method for convolutional code and concatenated SCEE/RS (Reed-Solomon) conding scheme are proposed. First, we describe the operation of the decoding steps in the proposed algorithm. Then deterministic values on the decoding operation are drived when some combination of predecoder-reencoder is used. Computer simulation results show that the compuatational complexity of the proposed SCEE decoder is significantly reduced compared to that of conventional Viterbi-decoder without degratation of the $P_{e}$ performance. Also, the concatenated SCEE/RS decoder has almost the same complexity of a RS decoder and its coding gain is higher than that of soft decision Viterbi or RS decoder respectively.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 하계학술대회 논문집 A
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• pp.159-161
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• 1994

This paper presents a combined algorithm of simulated annealing and determinstic method which overcomes the defects of two methods and guarantees the fast convergence to the global optimum. This optimization method is applied to optimal design of Interior Permanent Magnet Synchronous Motor. And the result of the proposed algorithm is compared with that of simulated annealing alone.

• 전산구조공학
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• 제5권2호
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• pp.93-104
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• 1992

최적화기법의 최종목표는 전체최적점(golbal optimum point)을 정확히 그리고 효율적으로 구하는 것이다. 이를 위해 확률론적인 탐색과정을 가지는 Simulated Annealing과 Genetic Algorithm에 의한 최적화과정을 살펴보고, 수학적함수와 트러스, 보 구조물에 대해 최적설계를 행하여 전체최적점에 도달한 신뢰도 및 계산시간을 기준으로 기존의 확정론적 최적화기법에 의한 결과와 그 유용성을 비교검토하였다.

• 대한전기학회논문지:전력기술부문A
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• 제51권3호
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• pp.109-117
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• 2002

High accuracy of the load forecasting for power systems improves the security of the power system and generation cost. However, the forecasting problem is difficult to handle due to the nonlinear and the random-like behavior of system loads as well as weather conditions and variation of economical environments. So far. many studies on the problem have been made to improve the prediction accuracy using deterministic, stochastic, knowledge based and artificial neural net(ANN) method. In the conventional load forecasting method, the load forecasting maximum error occurred for the holidays on Saturday and Monday. In order to reduce the load forecasting error of the daily peak load for the holidays on Saturday and Monday, fuzzy concept and linear regression theory have been adopted into the load forecasting problem. The proposed algorithm shows its good accuracy that the average percentage errors are 2.11% in 1996 and 2.84% in 1997.

• Wind and Structures
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• 제24권3호
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• pp.249-265
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• 2017

An efficient and accurate algorithm is proposed to estimate flutter safety factor of suspension bridges satisfying prescribed reliability levels. Uncertainties which arise from the basic wind speed at the bridge deck location, critical flutter velocity, the wind conversion factor from a scaled model to the prototype structure and the gust speed factor are incorporated. The proposed algorithm integrates the concepts of the inverse reliability method and the calculation method of the critical flutter velocity of suspension bridges. The unique feature of the proposed method is that it offers a tool for flutter safety assessment of suspension bridges, when the reliability level is specified as a target to be satisfied by the designer. Accuracy and efficiency of this method with reference to three example suspension bridges is studied and numerical results validate its superiority over conventional deterministic method. Finally, the effects of various parameters on the flutter safety factor of suspension bridges are also investigated.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.354-360
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• 1998

In general, it is very difficult to find optimal fuzzy rules by experience when a system is dynamical and/or complex. Futhermore proper fuzzy partitioning is not deterministic and there is no unique solution. Therefore we propose a new design method of an optimal fuzzy logic controller, that is a co-evolutionary genetic algorithm finding optimal fuzzy rule and proper membership functions at the same time. We formalize the relation between fuzzy rules and membership functions in terms of fitness. We review the typical approaching methods to co-evolutionary genetic algorithms , and then classify them by fitness relation matrix. Applications of the proposed method to a path planning problem of autonomous mobile robots when moving objects exist are presented to demonstrate the performance and effectiveness of the method.

• 산업공학
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• 제12권2호
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• pp.312-318
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• 1999

This paper considers the cell loss probability(CLP) in a multiplexer with the superposition of heterogeneous ON/OFF sources. The input traffic is composed of k classes. Traffic of class i is the superposition of M_(i) ON/OFF sources. Recently, the method based on the Markov modulated deterministic process(MMDP) is presented. Basically, it is the discretized model of stochastic fluid flow process(SFFP) and gives the CLP very fast, but under-estimates the CLP especially when the value of estimated CLP is very low. This paper develops the discretized model of Markov modulated Poisson process(MMPP). It is a special type of switched batch Bernoulli process(SBBP). Combining the transition probability matrix of MMDP and SBBP according to the state which is characterized by the arrival rate, this paper presents hybrid algorithm. The hybrid algorithm gives better estimate of CLP than that of MMDP and faster than SBBP.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1993년도 추계학술대회
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• pp.151-155
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• 1993

Evoked potentials(EP) measured with scalp electrodes are often described as a deterministic process corrupted by uncorrelated electrical activities occuring in the brain and These electrical activities(ongoing EEG) refer to noise in EP recording. The Conventional method to determine the EP waveform requires long recording time. Unfortunately most of algorithm developed are too complicated for implementation in real time. Thus, conner EP recording devices use Ensemble average for real time processing. In this paper introduce EP recording hardware for processing advanced algorithm in real tlne. This hardware is composed of DSP chip(TMS320c25) and microcomputer.