• Journal of Energy Engineering
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• v.6 no.1
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• pp.68-76
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• 1997

This paper presents a new neural network training algorithm which reduces the required training time considerably and overcomes many of the shortcomings presented by the conventional back-propagation algorithm. The algorithm uses a modified form of the back-propagation algorithm to minimize the mean squared error between the desired and actual outputs with respect to the inputs to the nonlinearities. Artificial Neural Network (ANN) model using the new algorithm is applied to forecast the short-term electric load. Inputs to the ANN are past loads and the output of the ANN is the hourly load forecast for a given day.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.113-117
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• 1995

Grinding operations is accomplished by rotating a gfinding wheel with lots of random abrasive at high speed, and its object is generally obtained the fanal workpiece surface of high quality as well as the maximization of workpiece removal rate. But, especiallysince grinding operations is related with a large amount of functional parameter, it is actually difficult to therapy that the grinding trouble occurs during the grinding process. Therefore, we trytodesign grinding trouble-shooting system utilizing the back-propagation model of neural network. The conceptual method is produced byidentifying the four parameters derived from the grinding power, and we are design te to the grinding trouble-shooting system on the basis of their data. In this paper, cognition and therapy method tothe grinding trouble which utilizes neural network based four identified models are suggested, and implementation results of computer simulation with respect to the grinding burn and chatter vibration is presented.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.131-137
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• 1994

Detection of grinding trouble occuring during the grinding process is classified into two types, i.e, based on the quantitative and qualitative knowledge. But, since the grinding operation is especially related with a large amount of functional parameters, it is actually defficult to cope with the grinding troubles occuring during process. Therefore, grinding trouble-shooting has difficulty in satisfying the requirement from the user. To cope with the grinding troubles occuring during the process, the application of neural network is on effective way. In this study, we identify the four parameters derived from the AE(Acoustic Emission) signals and present the grinding trouble-shooting system utilizing a back-propagation model of the neural network.

• Journal of Bio-Environment Control
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• v.1 no.2
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• pp.162-168
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• 1992

As the automation of nutrient solution management proceeds in the field of hydroponics, effective supporting systems to manage the nutrient solution by computer become needed. This study was attempt to predict the EC of nutrient solution using the neural networks. The multilayer perceptron consisting of 3 layers with the back propagation learning algorithm was selected for EC prediction, of which nine variables in the input layer were the concentrations of each ion and one variable in the output layer the EC of nutrient solution. The meq unit in ion concentration was selected fir input variable in the input layer. After the 10,000 learning sweeps with 108 sample data, the comparison of predicted and measured ECs for 72 test data showed good agreements with the correlation coefficient of 0.998. In addition, the predicted ECs by neural network showed relatively equal or closer to the measured ones than those by current complicated models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11C
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• pp.75-84
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• 2001

In this paper, we propose an on-line FDD(Fault Detection and Diagnosis) system based on the three layer feed-forward neural network which is trained by the back-propagation teaming algorithm. We implement the on-line fault detection and diagnosis system by Visual C++ and Visual Basic. The proposed FDD system is applied to an air handling unit in operation. Experimental results show the high performance of our system in the task of fault detection and diagnosis.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.107-113
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• 2001

This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.119-128
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• 1994

In this paper, we propose a method for facial feature extraction and recognition algorithm using neural networks. First we extract a face part from the background image based on the knowledge that it is located in the center of an input image and that the background is homogeneous. Then using vertical and horizontal projections. We extract features from the separated face image using knowledge base of human faces. In the recognition step we use the back propagation algorithm of the neural networks and in the learning step to reduce the computation time we vary learning and momentum rates. Our technique recognizes 6 women and 14 men correctly.

• Transactions of the Society of Information Storage Systems
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• v.6 no.1
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• pp.18-24
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• 2010

The performance of the speech recognition mainly depends on uncertain factors such as speaker's conditions and environmental effects. The present study deals with the speech recognition of a number of multi-syllable isolated Korean words using soft computing techniques such as back-propagation neural network, fuzzy inference system, and fuzzy neural network. Feature patterns for the speech recognition are analyzed with 12th order thirty frames that are normalized by the linear predictive coding and Cepstrums. Using four models of speech recognizer, actual experiments for both single-speakers and multiple-speakers are conducted. Through this study, the recognizers of combined fuzzy logic and back-propagation neural network and fuzzy neural network show the better performance in identifying the speech recognition.

• Laser Solutions
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• v.3 no.3
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• pp.13-19
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• 2000

The stencil is a thin stainless sheet in which a pattern is formed, which is placed on a surface of plate to reproduce the pattern of electric circuit. Conventionally the stencil has been produced by etching process. This process has many anti-environmental factors. In this study, Nd : YAG laser cutting process has been applied for stencil manufacturing. The study is focused on estimating kerf width of laser cut stencil by E.B.P.(Error Back-Propagation). This algorithm is good for estimating target value from input value. In this paper, target value was kerf width, and input values were frequency, pulse width, cutting speed and laser power. E.B.P. after teaming input and target could estimate kerf width from some variables precisely.

• International Journal of Reliability and Applications
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• v.10 no.1
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• pp.1-15
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• 2009

Even though the impact of manufacturing quality to reliability is not considered much as well as that of design area, a major cause of an early failure of the product is known as manufacturing problem. This research applies two different types of neural network algorithms, the Back propagation (BP) algorithm and Learning Vector Quantization (LVQ) algorithm, to identify and classify the nonrandom variation pattern on the control chart based on knowledge-based diagnosis of dimensional variation. The performance and efficiency of both algorithms are evaluated to choose the better pattern recognition system for auto body assembly process. To analyze hundred percent of the data obtained by Optical Coordinate Measurement Machine (OCMM), this research considers an application in which individual observations rather than subsample means are used. A case study for analysis of OCMM data in underbody assembly process is presented to demonstrate the proposed knowledge-based pattern recognition system.