• 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.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.131-147
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• 2024

A routing strategy based on traffic prediction and dynamic cache allocation for satellite nodes is proposed to address the issues of high propagation delay and overall delay of inter-satellite and satellite-to-ground links in low Earth orbit (LEO) satellite systems. The spatial and temporal correlations of satellite network traffic were analyzed, and the relevant traffic through the target satellite was extracted as raw input for traffic prediction. An improved gradient boosting regression tree algorithm was used for traffic prediction. Based on the traffic prediction results, a dynamic cache allocation routing strategy is proposed. The satellite nodes periodically monitor the traffic load on inter-satellite links (ISLs) and dynamically allocate cache resources for each ISL with neighboring nodes. Simulation results demonstrate that the proposed routing strategy effectively reduces packet loss rate and average end-to-end delay and improves the distribution of services across the entire network.

• Journal of Korean Academy of Nursing
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• v.26 no.2
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• pp.281-289
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• 1996

Neural networks have recently attracted considerable attention in the field of classification and other areas. The purpose of this study was to demonstrate an experiment using back-propagation neural network model applied to nursing diagnosis. The network's structure has three layers ; one input layer for representing signs and symptoms and one output layer for nursing diagnosis as well as one hidden layer. The first prototype of a nursing diagnosis system for patients with stomach cancer was developed with 254 nodes for the input layer and 20 nodes for the output layer of 20 nursing diagnoses, by utilizing learning data set collected from 118 patients with stomach cancer. It showed a hitting ratio of .93 when the model was developed with 20,000 times of learning, 6 nodes of hidden layer, 0.5 of momentum and 0.5 of learning coefficient. The system was primarily designed to be an aid in the clinical reasoning process. It was intended to simplify the use of nursing diagnoses for clinical practitioners. In order to validate the developed model, a set of test data from 20 patients with stomach cancer was applied to the diagnosis system. The data for 17 patients were concurrent with the result produced from the nursing diagnosis system which shows the hitting ratio of 85％. Future research is needed to develop a system with more nursing diagnoses and an evaluation process, and to expand the system to be applicable to other groups of patients.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.8
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• pp.1239-1249
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• 1990

In this paper, using the neural network design techniques, an adaptive Mahalanobis distance classifier(AMDC) is designed. This classifier has three layers: input layer, internal layer and output layer. The connection from input layer to internal layer is fully connected, and that from internal to output layer has partial connection that might be thought as an Oring. If two ormore clusters of patterns of one class are laid apart in the feature space, the network adaptively generate the internal nodes, whhch are corresponding to the subclusters of that class. The number of the output nodes in just same as the number of the classes to classify, on the other hand, the number of the internal nodes is defined by the number of the subclusters, and can be optimized by itself. Using the method of making the subclasses, the different patterns that are of the same class can easily be distinguished from other classes. If additional training is needed after the completion of the traning, the AMDC does not have to repeat the trainging that has already done. To test the performance of the AMDC, the experiments of classifying 500 Hangeuls were done. In experiment, 20 print font sets of Hangeul characters(10,000 cahracters) were used for training, and with 3 sets(1,500 characters), the AMDC was tested for various initial variance \ulcornerand threshold \ulcorner and compared with other statistical or neural classifiers.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.511-515
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• 2009

For detecting of the faulty insulator, Correlation Propagation Neural Networks(CPNN) has been proposed. Faulty insulator is reduced the rate of insulation extremely, and taken the results dirty and injured. It is necessary to detect the faulty insulator and exchange the new one. And thus, we have designed the CPNN to be detected that insulators by the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. 1-D CPNN hardware has been implemented with general purpose. Experiments with static and dynamic signals have been done upon the CPNN hardware. Through the results of simulation experiments, we define the ability of real-time detecting the faulty insulators.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.83-87
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• 2007

Correlation Propagation Neural Networks is proposed for On-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D CPNN hardware has been implemented with general purpose analog ICs to test the interpolation capability of the proposed neural networks. Experiments with static and dynamic signals have been done upon the CPNN hardware.

• Journal of Communications and Networks
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• v.7 no.3
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• pp.284-293
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• 2005

A critical issue in applications involving networks of wireless sensors is their ability to synchronize, and mitigate the fading propagation channel effects. Especially when distributed 'slave' sensors (nodes) reach-back to communicate with the 'master' sensor (gateway), low power cooperative schemes are well motivated. Viewing each node as an antenna element in a multi-input multi-output (MIMO) multi-antenna system, we design pilot patterns to estimate the multiple carrier frequency offsets (CFO), and the multiple channels corresponding to each node-gateway link. Our novel pilot scheme consists of non-zero pilot symbols along with zeros, which separate nodes in a time division multiple access (TDMA) fashion, and lead to low complexity schemes because CFO and channel estimators per node are decoupled. The resulting training algorithm is not only suitable for wireless sensor networks, but also for synchronization and channel estimation of single- and multi-carrier MIMO systems. We investigate the performance of our estimators analytically, and with simulations.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2546-2549
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• 2004

For Reduction of Power-Loss, a new Lateral Information Propagation Networks(LIPN) has been proposed. Damaged insulator is reduced the rate of insulation extremely, and taken the results dirty and injured. It is necessary to be actions that detect the damaged insulator and exchange the new one. And thus, we have designed the LIPN to be detected that insulators by the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interrelation is achieved. Through the results of simulation experiments, we difine the ability of real-time detecting the damaged insulators.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2276-2279
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• 2002

For detecting of Energized Insulator, a new Lateral Information Propagation Networks(LIPN) has been proposed. Faulty insulator is reduced the rate of insolation extremely, and taken the results dirty and injured. It is necessary to be actions that detect the faulty insulator and exchange the new one. And thus, we have designed the LIPN to be detected that insulators by the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. Through the results of simulation experiments, we define the ability of real-time detecting the faulty insulators.