• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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• pp.59-64
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• 2001

The objectives of the thesis are to propose a pattern classification method for remote sensing data using artificial neural network. First, we apply the error back propagation algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. Using the training data set and the error back propagation algorithm, a layered neural network is trained such that the training pattern are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of Landsat TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1995년도 추계학술대회발표논문집; 서울대학교, 서울; 30 Sep. 1995
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• pp.325-333
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• 1995

Back-propagation is the most widely used algorithm for supervised learning in multi-layer feed-forward networks. However, back-propagation is very slow in convergence. In this paper, a new weight initialization method, called rough map initialization, in multi-layer perceptrons is proposed. To overcome the long convergence time, possibly due to the random initialization of the weights of the existing multi-layer perceptrons, the rough map initialization method initialize weights by utilizing relationship of input-output features with singular value decomposition technique. The results of this initialization procedure are compared to random initialization procedure in encoder problems and xor problems.

• International Journal of Contents
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• 제12권1호
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• pp.49-53
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• 2016

"Hahoe Village" in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since "Hahoe Village" is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near "Hahoe Village". To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.

• 한국통신학회논문지
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• 제22권11호
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• pp.2425-2431
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• 1997

자기 공명 영상의 사용이 빈번해 짐에 따라 환자의 해부학적인 정확한 정보와 이를 빠르고 효과적으로 진단하는데 유용한 자동 영상 세그멘테이션 방법이 요구되고 있다. 본 논문에서는 오류 역전파 알고리즘으로 학습한 신경망을 이용하여 뇌의 자기 공명 영상을 자동적으로 세그멘테이션하는 방법을 제안한다. 특정 환자의 자기 공명 영상을 분할하여 학습시킨 신경망은 다른 환자의 자기 공명 영상도 자동적으로 세그멘테이션하여 뇌의 윤곽을 뚜렷하게 나타내었다.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권7호
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• pp.327-333
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• 2002

In this study, we developed a snoring detection algorithm that detects snores automatically. It consists of preprocessing and snoring detection part. The preprocessing part is composed of a noise removal part using spectrum subtraction, and segmentation part, and computation part of temporal and spectral features. And the snoring detection part decides whether detected blocks are snores with BPNN(Back-Propagation Neural Network). BPNN with one hidden layer and one output layer, is trained with data of 7 subjects and tested with data of 11 subjects of total 18 subjects. The proposed algorithm showed a Sensitivity of 90.41% and a Predictive Positive Value of 84.95%.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.853-858
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• 1993

The error back-propagation(BP) algorithm is widely used for finding optimum weights of multi-layer neural networks. However, the critical drawback of the BP algorithm is its slow convergence of error. The major reason for this slow convergence is the premature saturation which is a phenomenon that the error of a neural network stays almost constant for some period time during learning. An inappropriate selections of initial weights cause each neuron to be trapped in the premature saturation state, which brings in slow convergence speed of the multi-layer neural network. In this paper, to overcome the above problem, Micro-Genetic algorithms(.mu.-GAs) which can allow to find the near-optimal values, are used to select the proper weights and slopes of activation function of neurons. The effectiveness of the proposed algorithms will be demonstrated by some computer simulations of two d.o.f planar robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.65-68
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• 1996

Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. The TMS32OC31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the, network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time, control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• 제어로봇시스템학회논문지
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• 제5권8호
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• pp.995-1000
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• 1999

This paper presents a optic system and a visual inspection algorithm looking for solder joint defects of J-lead chip which are more integrate and smaller than ones with Gull-wing on PCBs(Printed Circuit Boards). The visual inspection system is composed of three sections : host PC, imaging and driving parts. The host PC part controls the inspection devices and executes the inspection algorithm. The imaging part acquires and processes image data. And the driving part controls XY-table for automatic inspection. In this paper, the most important five features are extracted from input images to categorize four classes of solder joint defects in the case of J-lead chip and utilized to a back-propagation network for classification. Consequently, good accuracy of classification performance and effectiveness of chosen five features are examined by experiment using proposed inspection algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.468-471
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• 2005

The Air Quality Sensor(AQS), located near the fresh air inlet, serves to reduce the amount of pollution entering the vehicle cabin through the HVAC(heating, ventilating, and air conditioning) system by sending a signal to close the fresh air inlet door/ventilation flap when the vehicle enters a high pollution area. One chip sensor module which include above two sensing elements, humidity sensor and bad odor sensor was developed for AQS (air quality sensor) in automobile. With this sensor module, PIC microcontroller was designed with back propagation neural network to reduce detecting error when the motor vehicles pass through the dense fog area. The signal from neural network was modified to control the inlet of automobile and display the result or alarm the situation. One chip microcontroller, Atmega128L (ATmega Ltd., USA) was used. For the control and display. And our developed system can intelligently detect the bad odor when the motor vehicles pass through the polluted air zone such as cattle farm.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.777-782
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• 2011

Neural network technique is widely employed in the fields of signal processing, control systems, pattern recognition, etc. Learning of neural networks is an important procedure to accomplish dynamic system modeling. This paper presents a novel learning approach for differential neural network models based on the Kalman-Bucy filter theory. We construct an augmented state vector including original neural state and parameter vectors and derive a state estimation rule avoiding gradient function terms which involve to the conventional neural learning methods such as a back-propagation approach. We carry out numerical simulation to evaluate the proposed learning approach in nonlinear system modeling. By comparing to the well-known back-propagation approach and Kalman-Bucy filtering, its superiority is additionally proved under stochastic system environments.