• Journal of the Korea Institute of Military Science and Technology
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• v.1 no.1
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• pp.82-91
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• 1998

This paper presents a neural networks based approach for the problem of sensor failure detection and accommodation for ship without physical redundancy in the sensors. The designed model consists of two neural networks. The first neural network is responsible for the failure detection and the second neural network is responsible for the failure identification and accommodation. On the yaw rate sensor of ship, simulation results indicates that the proposed method can be useful as failure detector and sensor estimator.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.293-297
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• 1998

This paper presents a newly developed speed sensorless drive using RLS based on Neural Network Training Algorithm. The proposed algorithm has just the time-varying learning rate, while the wellknown back-propagation algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The theoretical analysis and experimental results to verify the effectiveness of the proposed control strategy are described.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.552-557
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• 2000

In the paper, we proposed a face recognition method that uses GA-BP(Genetic Algorithm-Back propagation Network) that optimizes initial parameters such as bias values or weights. Each pixel in the picture is used for input of the neuralnetwork. The initial weights of neural network is consist of fixed-point real values and converted to bit string on purpose of using the individuals that arte expressed in the Genetic Algorithm. For the fitness value, we defined the value that shows the lowest error of neural network, which is evaluated using newly defined adaptive re-learning operator and built the optimized and most advanced neural network. Then we made experiments on the face recognition. In comparison with learning convergence speed, the proposed algorithm shows faster convergence speed than solo executed back propagation algorithm and provides better performance, about 2.9% in proposed method than solo executed back propagation algorithm.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.49-57
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• 2010

This paper proposes a Korean document. categorization algorithm using Back Propagation Neural Network(BPNN) with Singular Value Decomposition(SVD). BPNN makes a network through its learning process and classifies documents using the network. The main difficulty in the application of BPNN to document categorization is high dimensionality of the feature space of the input documents. SVD projects the original high dimensional vector into low dimensional vector, makes the important associative relationship between terms and constructs the semantic vector space. The categorization algorithm is tested and compared on HKIB-20000/HKIB-40075 Korean Text Categorization Test Collections. Experimental results show that BPNN algorithm with SVD achieves high effectiveness for Korean document categorization.

• Water Engineering Research
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• v.1 no.2
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• pp.129-136
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• 2000

Predicting water levels is a difficult task because a lot of uncertainties are included. Therefore the neural network which is appropriate to such a problem, is introduced. One day ahead forecasting of river stage in the Bocheong River is carried out by using the neural network model. Historical water levels at Snagye gauging point which is located at the downstream of the Bocheong River and average rainfall of the Bocheong River basin are selected as training data sets. With these data sets, the training process has been done by using back propagation algorithm. Then waters levels in 1997 and 1998 are predicted with the trained algorithm. To improve the accuracy, a filtering method is introduced as predicting scheme. It is shown that predicted results are in a good agreement with observed water levels and that a filtering method can overcome the lack of training patterns.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.3
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• pp.565-571
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• 2010

This paper proposes a parity discrimination algorithm which discriminates N bit parity using a perceptron neural network and back propagation algorithm. This algorithm decides minimum hidden unit numbers when discriminates N bit parity. Therefore, this paper implements parity discrimination experiments for N bit by changing hidden unit numbers of the proposed perceptron neural network. Experiments confirm that the proposed algorithm is possible to discriminates N bit parity.

• Journal of Mechanical Science and Technology
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• v.18 no.8
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• pp.1327-1337
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• 2004

An efficient multi-objective optimization method is presented making use of neural network and a systematic satisficing trade-off method (STOM), in order to simultaneously improve both maneuverability and durability of tire. Objective functions are defined as follows: the sidewall-carcass tension distribution for the former performance while the belt-edge strain energy density for the latter. A back-propagation neural network model approximates the objective functions to reduce the total CPU time required for the sensitivity analysis using finite difference scheme. The satisficing trade-off process between the objective functions showing the remarkably conflicting trends each other is systematically carried out according to our aspiration-level adjustment procedure. The optimization procedure presented is illustrated through the optimum design simulation of a representative automobile tire. The assessment of its numerical merit as well as the optimization results is also presented.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.3
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• pp.64-74
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• 2007

The continuous chip in turning operation deteriorates precision of workpiece and causes a hazardous condition to operator. Thus the chip form control becomes a very important task for reliable machining process. So, grooved chip breaker is widely used to obtain reliable discontinuous chip. However, developing new cutting insert having chip breaker takes long time and needs lots of research expense due to a couple of processes such as forming, sintering, grinding and coating of product and many different evaluation tests. In this paper, performance of commercial chip breaker is evaluated with neural network which is learned with a back propagation algorithm. For the evaluation, several important elements(depth of cut, land, breadth, radius) which directly influence the chip formation were chosen among commercial chip breakers and were used as input values of neural network. With the results of these input values, the performance evaluation method was developed and applied that method to the commercial tools.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.47-50
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• 1992

This paper describes pattern classification algorithm of ECG using back-propagation neural network. We presents new feature extractor using second order approximating function as the input signals of neural network. We use 9 significant parameters which were extracted by feature extractor. 5 most characterized ECG signal pattern is classified accurately by neural network. We use AHA database to evaluate the performance ol the proposed pattern classification algorithm.

• IE interfaces
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• v.8 no.1
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• pp.5-13
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• 1995

Automated vision inspection has become a vital part of computer related industries. Most of the existing inspection systems mainly utilize black and white images. In this paper, we consider an application of automated vision inspection in which cable color has to be recognized in order to detect the quality status of assembled wire harness. A back-propagation neural network is proposed to classify seven different cable colors. To represent a single point in image space, we use the ($L^*,\;a^*,\;b^*$) model which is one of commonly used color-coordinate systems in image processing. After training the neural network with ($L^*,\;a^*,\;b^*$) data obtained from color image, we tested its performance. The results show that the neural network is able to classify cable colors with high performance.