• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.10
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• pp.2563-2578
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• 1996

In this paper, channel error characteristics are investigated to alleviate the channel error propagation problem of the digital TV transmission systems. First, error propagation problems, which are mainly caused by the inter-frame dependancy and variable length coding of the MPEG-2 baseline encoder, are intensively analyzed. Next, existing channel resilient schemes are systematically classified into two kinds of schemes; one for the encoder and the other for the decoder. By comparing the performance and implementation cost, the encoder side schemes, such as error localization, layered coding, error resilience bit stream generation techniques, are described in this paper. Also, in an effort to consider the parcticality of the real transmission situation, an efficient error detection scheme for a decoder system is proposed by employing a priori information of the bit stream syntas, checking the encoding conditions at the encoder stage, and exploiting the statistics of the image itself. Finally, subsequent error concealment technique based on the DCT coefficient recovery algorithm is adopted to evaluate the performance of the proposed error resilience technique. The computer simulation results show that the quality of the received image is significantly improved when the bit error rate is as high as 10$^{-5}$ .

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.1-6
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• 2014

In many medical image devices, dc noise often prevents normal diagnosis. In wireless capsule endoscopy systems, multipath fading through indoor wireless links induces inter-symbol interference (ISI) and indoor electric devices generate impulsive noise in the received signal. Moreover, dc noise, ISI, and impulsive noise are also found in optical fiber communication that can be used in remote medical diagnosis. In this paper, a blind signal processing method based on the biased probability density functions of constant modulus error that is robust to those problems that can cause error propagation in decision feedback (DF) methods is presented. Based on this property of robustness to error propagation, a DF version of the method is proposed. In the simulation for the impulse response of optical fiber channels having slowly varying dc noise and impulsive noise, the proposed DF method yields a performance enhancement of approximately 10 dB in mean squared error over its linear counterpart.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.485-500
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• 2000

In this paper the adaptive crack propagation analysis based on the estimated local and global error in the element-free Galerkin (EFG) method is presented. It is possible to keep consistency and accuracy of analysis in each propagation step by adaptive analysis. The adaptivity analysis in crack propagation is achieved by adding and removing the node along the background integration cell that are refined or recovered as estimated error. These errors are obtained by calculating the difference between the values of the projected stresses and original EFG stresses. To evaluate the performance of proposed adaptive procedure, the convergence behavior is investigated lot several examples. The results of these examples show the efficiency of proposed scheme in crack propagation analysis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.10
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• pp.571-576
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• 2003

XML Web Service based on internet can cause problems on transmission speed and data error. Also system analysis results simulated by several different research groups can hardly have reliability because of error data that come from improperly managed files. In order to solve this problems, algorithm sever using XML Web Service is shared on the internet so widely that various application programs based on basic analysis module with a united IO can be developed. And also XML Dataset DB is interacted with XML Web Service, which prevents propagation of error data. It causes to improve reliabilityon the load flow analysis result and solve the problems on data error or transmission speed that can possibly come from internet.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.1
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• pp.66-70
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• 1997

In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.83-91
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• 1995

A neural network with one or more layers of hidden units can be trained using the well-known error back propagation algorithm. According to this algorithm, the synaptic weights of the network are updated during the training by propagating back the error between the expected output and the output provided by the network. However, the error back propagation algorithm is characterized by slow convergence and the time required for training and, in some situation, can be trapped in local minima. A theoretical formulation of a new fast learning method based on tabu search method is presented in this paper. In contrast to the conventional back propagation algorithm which is based solely on the modification of connecting weights of the network by trial and error, the present method involves the calculation of the optimum weights of neural network. The effectiveness and versatility of the present method are verified by the XOR problem. The present method excels in accuracy compared to that of the conventional method of fixed values.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.75-81
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• 2001

In this paper we analyzed the localization error of near-field detection algorithm in the sea. The near-field detection algorithms using triangulation and wavefront curvature basically assume a signal in two dimension of bearing and range. But the assumption causes localization error because there is three dimension of bearing, range, and depth in the sea. Even through three dimensional effect is considered, the localization error is occurred if multipath propagation in the sea is ignored. To analyze the localization error in the sea, we simulate the near-field localization using acoustic propagation model and focused beamforming considering wavefront curvature. The simulation results indicate that localization error always occurs in the sea and the error varied with sound velocity profile, water depth, bottom slope, source range, etc.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.896-899
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• 1991

This paper presents the neural network method to predict the Korea composition stock price index. The error back-propagation method is used to train the multi-layer perceptron network. Ten of the various economic indices of the past 7 Nears are used as train data and the monthly transition of the composition stock price index is represented by five output neurons. Test results of this method using the data of the last 18 months are very encouraging.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.4
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• pp.58-65
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• 1992

In this paper new back propagation algorithm which adds neurons to hidden layer is proposed. this proposed algorithm is applied to the pattern recognition of written number coupled with back propagation algorithm through omitting redundant learning. Learning rate and recognition rate of the proposed algorithm are compared with those of the conventional back propagation algorithm and the back propagation through omitting redundant learning. The learning rate of proposed algorithm is 4 times as fast as the conventional back propagation algorithm and 2 times as fast as the back propagation through omitting redundant learning. The recognition rate is 96.2% in case of the conventional back propagation algorithm, 96.5% in case of the back propagation through omitting redundant learning and 97.4% in the proposed algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.327-329
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• 1998

Up-Propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden variables using top-down connections. The inversion process is iterative, utilizing a negative feedback loop that depends on an error signal propagated by bottom-up connections. The error signal is also used to learn the generative model from examples. the algorithm is benchmarked against principal component analysis in experiments on images of handwritten digits.