• 한국음향학회지
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• 제37권4호
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• pp.196-205
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• 2018

본 논문은 선박소음 데이터에 음선 역전파 기법을 적용하여 수동 선박 위치 추정 알고리듬을 제시한다. 기존의 방법 [S. H. Abadi, D. Rouseff and D. R. Dowling, J. Acoust. Soc. Am. 131, 2599-2610 (2012)]은 음선 기반 블라인드 디컨벌루션 및 음선 역전파 기법을 활용하여 배열의 기울기가 없는 근거리 환경에서 음원의 위치를 추정하였다. 하지만 위 방법은 배열의 기울기에 따른 위치 추정 오차가 크게 발생한다는 단점이 존재한다. 이를 극복하기 위해 본 논문에서는 음선 기반 블라인드 디컨벌루션 및 음선 역전파 기법을 사용하되, 배열의 기울기를 보정하여 음원의 위치를 추정할 수 있는 알고리듬을 제안한다. 제안된 알고리듬의 성능은 SAVEX15(Shallow-water Acoustic Variability EXperiment in 2015)해상 실험의 선박소음 데이터를 이용하여 검증하였다.

• 한국인터넷방송통신학회논문지
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• 제11권4호
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• pp.91-97
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• 2011

본 논문에서는 Mackey-Glass시계열의 예측에서 유전자알고리즘을 이용하는 구조적인 동정과 뉴로퍼지에 의한 파라미터 동정의 학습방법과 하이브리드 시스템을 제안하였다. 본 방법은 두 가지로 구성되었다. 하나는 입력공간에 대한 분할을 통하여 유전 알고리즘을 이용하여 퍼지 규칙베이스를 구축하고 다른 하나는 이 규칙베이스를 토대로 기울기 최하강법을 이용하여 제어규칙의 변수에 대한 파라미터 동정이다. 제안된 방법을 성능을 검증하기 위하여 입력의 패턴을 시간간격에 따라서 x(t-3), x(t-6)과 x(t-9)의 세 가지로 구성하였다. 많은 시뮬레이션을 통하여 유전알고리즘에 의한 구조적인 동정으로 인하여 학습초기에 오차가 작은 것을 알 수 있었다. 표2에서와 같이 성능을 확인 할 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제23권5호
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• pp.1027-1035
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• 2012

This paper proposes a new Levenberg-Marquardt algorithm that is accelerated by adjusting a Jacobian matrix and a quasi-Hessian matrix. The proposed method partitions the Jacobian matrix into block matrices and employs the inverse of a partitioned matrix to find the inverse of the quasi-Hessian matrix. Our method can avoid expensive operations and save memory in calculating the inverse of the quasi-Hessian matrix. It can shorten the training time for fast convergence. In our results tested in a large application, we were able to save about 20% of the training time than other algorithms.

• 전자공학회논문지B
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• 제31B권7호
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• pp.211-218
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• 1994

Stochastic computation employs random pulse streams to represent numbers. In this paper, we study a new method to implement the number system which uses the ratio of the numbers of ones and zeros in the pulse streams. In this number system. if P is the probability that a pulse is one in a pulse stream then the number X represented by the pulse stream is defined as P/(1-P). We propose circuits to implement the basic operations such as addition multiplication and sigmoid function with this number system and examine the error characteristics of such operations in stochastic computation. We also propose a neuron model and derive a learning algorithm based on backpropagation for the 3-layered feedforward neural networks. We apply this learning algorithm to a digit recognition problem. To analyze the results, we discuss the errors due to the variance of the random pulse streams and the quantization noise of finite length register.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.69-71
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• 2004

DC servo motors have a defect that they need a periodical maintenance because of a brush commutation and also they have a difficulty at high speed operation. In this reason, the use of AC Servo motors are increasing these days. In this paper, a proposed neuro observer is applied to speed control of AC servo motor. The proposed observer complement a problem that occur from increase of gain of High-gain observer in proportion to the square number of observable state variables. And also, the proposed observer can tune the gain obtained by differentiating observational error automatically by using the backpropagation training method to stabilize the observational speed. The excellence and feasibility of the proposed observer is proved by making a comparison test between the proposed observer and the others applied to the same AC servo motor.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.390-393
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• 2007

In this paper, applications of self recurrent neuro-fuzzy controller to stabilization of nonlinear system are considered. The architecture of self recurrent neuro-fuzzy controller is fix layer, and the hidden layer is comprised of self recurrent architecture. Also, generalized dynamic error-backpropagation algorithm is used for the learning of the self recurrent neuro-fuzzy controller. To demonstrate the efficiency of the self recurrent neuro-fuzzy control algorithm presented in this study, a self recurrent neuro-fuzzy controller was designed and then a comparative analysis was made with LQR controller through an simulation.

• Korean Journal of Hydrosciences
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• 제4권
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• pp.1-9
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• 1993

It is a major objective for the management and operation of water resources system to forecast streamflows. The applicability of artificial neural network model to hydrologic system is analyzed and the performance is compared by statistical method with observed. Multi-layered perception was used to model rainfall-runoff process at Pyung Chang River Basin in Korea. The neural network model has the function of learning the process which can be trained with the error backpropagation (EBP) algorithm in two phases; (1) learning phase permits to find the best parameters(weight matrix) between input and output. (2) adaptive phase use the EBP algorithm in order to learn from the provided data. The generalization results have been obtained on forecasting the daily and hourly streamflows by assuming them with the structure of ARMA model. The results show validities in applying to hydrologic forecasting system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.69-71
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• 2005

DC servo motors have a defect that they need a periodical maintenance because of a brush commutation and also they have a difficulty at high speed operation. In this reason, the use of AC Servo motors are increasing these days. In this paper, a proposed neuro observer is applied to speed control of AC servo motor. The proposed observer complement a problem that occur from increase of gain of High-gain observer in proportion to the square number of observable state variables. And also, the proposed observer can tune the gain obtained by differentiating observational error automatically by using the backpropagation training method to stabilize the observational speed. The excellence and feasibility of the proposed observer is proved by making a comparison test between the proposed observer and the others applied to the same AC servo motor.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.385-389
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• 1994

So far many researches have studied to control a cart system with a pole on the top of itself (forwards we call it simply a cart system) which is movable only to the directions to which a cart moves, using neural networks and genetic algorithms. Especially which it wag solved by genetic algorithms, it was possible to control a cart system more robustly than ordinary methods using neural networks but it had problems too, i.e., the control time to be achieved was short and the processing time for it was long. However we could control a cart system using standard genetic algorithm longer than ordinary neural network methods (for example error backpropagation) and could see that robust control was possible. Computer simulation was performed through the personal computer and the results showed the possibility of real time control because the cpu time which was occupied by processes was relatively short.