• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1135-1139
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• 1989

Three layered neural network was applied for the pattern recognition problem of human spermatozoa in clinical test. The goodness of recognition rate was studied in relation to the number of hidden layer cells and of output layer cells. The proposed method provided better results than conventional template matching technique. Parallel processing of the back propagation learning algorithm was also studied using transputers and its performance was evaluated.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.144-148
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• 1989

We solve the inverse kinematics problems in robotics by employing a neural network. In the practical situation. it is not easy to obtain the exact inverse kinematics solution, since there are many unforeseen errors such as the shift of a robot base the link's bending, et c. Hence difficulties follow in the trajectory planning. With the neural network, it is possible to train the robot motion so that the robot follows the desired trajectory without errors even under the situation where the unexpected errors are involved. In this work, Back-Propagation rule is used as a learning method.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.131-134
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• 1990

A back-propagation neural network based system for a fault diagnosis of a chemical process is developed. Training data are acquired from FCD(Fault-Consequence Digraph) model. To improve the resolution of a diagnosis, the system is decomposed into 6 subsystems and the training data are composed of 0, 1 and intermediate values. The feasibility of this approach is tested through case studies in a real plant, a naphtha furnace, which has been used to develop a knowledge based expert system, OASYS (Operation Aiding expert SYStem).

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.87-90
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• 2002

This paper has been studied a new approach using neural network to detect false laser marks. In the proposed approach, input images are segmented into R, G and B colors and implements mask areas respectively. And then average and variation values of the each mask area are extracted for the learning process to minimize input nodes. Using this technique, the new input data is obtained and implemented to the back-propagation algorithm using multi layer perception. This paper reduces the computational complexity necessary and shows better effectiveness to inspect false laser marks.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.206-211
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• 1996

A major problem in automation of turning operation is the difficulty in obtaining a sufficient and reliable chip control. Therefore it becomes desirable to find a method which can detect the chip form. In this paper, a method of the identification of chip form using output of pyrometer and neural network technique is developed. An efficiency of developed method is examined by experiments in turning and the validity of it is confirmed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.69-73
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• 1996

Tool wear monitoring is very important aspect in metal cutting because tool wear effects quarity and precision of workpiece, tool life etc. In this study we detected force signal through tool dynamometer in turning and using it we conducted 6th AR modeling and fractal analysis. Finally the back-propagation model of the neural network is utilized to monitor tool wear and features are extracted through AR model and fractal analysis.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.109-112
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• 2001

This paper proposes a method to classify different video contents using features of digital video. Classified video types are the news, drama, show, sports, and talk program. Features, such as intra-coded macroblock number St motion vector in P-picture in MPEG domain are used. The frame difference of YCbCr is also employed as a measure of classification. We detect the occurrences of cuts in a video for a measure of classification. Finally, back-propagation neural-network of 3 layers is used to classify video contents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.149-152
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• 2009

본 논문에서는 오차역전파 알고리즘을 사용한 3층 구조의 퍼셉트론형 신경회로망으로 네트워크의 학습을 실시하여, N비트의 패리티판별에 필요한 최소의 중간유닛수의 해석에 관한 연구이다. 따라서 본 논문은 제안한 퍼셉트론형 신경회로망의 중간 유닛의 수를 변화시켜 N 비트의 패리티 판별 실험을 실시하였다. 본 시스템은 패리티 판별의 실험을 통하여 N 비트 패리티 판별이 가능하다는 것을 실험으로 확인한다.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.3
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• pp.51-57
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• 2004

An intelligent performance diagnostic computer program of a gas turbine using the NN(Neural Network) was developed. Recently on-condition performance monitoring of major gas path components using the GPA(Gas Path Analysis) method has been performed in analyzing of engine faults. However because the types and severities of engine faults are various and complex, it is not easy that all fault conditions of the engine would be monitored only by the GPA approach Therefore in order to solve this problem, application of using the NNs for learning and diagnosis would be required. Among then, a BPN (Back Propagation Neural Network) with one hidden layer, which can use an updating learning rate, was proposed for diagnostics of PT6A-62 turboprop engine in this work.