• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2780-2785
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• 1997

Although KBANN(knowledge-based artificial neural network) has been shown to be more effective than other machine learning algorithms, KBANN doesn't have the theory refinement capability because the topology of the network can't be altered dynamically. Although TopGen algorithm was proposed to extend the ability of KABNN in this respect, it also had some defects due to the connection of hidden nodes from all input nodes and the use of beam search. An algorithm, which could solve this TopGen's defects by adding the hidden nodes connected from only related input nodes and using hill-climbing search with backtracking, is proposed.

• The Journal of Information Systems
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• v.9 no.1
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• pp.27-44
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• 2000

The structure of neural networks is represented by a weighted directed graph with nodes representing units and links representing connections. Each link is assigned a numerical value representing the weight of the connection. In learning process, the values of weights are adjusted by errors. Following experiment results, the interval of adjusting weights, that is, epoch size influenced neural networks＇ performance. As epoch size is larger than a certain size, neural networks＇performance decreased drastically. And the number of hidden layer＇s node also influenced neural networks＇performance. The networks＇performance decreased as hidden layers have more nodes and then increased at some number of hidden layer＇s node. So, in implementing of neural networks the epoch size and the number of hidden layer＇s node should be decided by systematic methods, not empirical or heuristic methods.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1773-1780
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• 1996

KBANN (knowledge-based artificial neural network) combining the symbolic approach and the numerical approach has been shown to be more effective than other machine learning models. However KBANN doesn't have the theory refinement ability because the topology of network can't be altered dynamically. Although TopGen was proposed to extend the ability of KABNN in this respect, it also had some defects due to the link-ing of hidden nodes to input nodes and the use of beam search. The algorithm which could solve this TopGen's defects, by adding the hidden nodes linked to next layer nodes and using hill-climbing search with backtracking, is designed.

• Journal of Multimedia Information System
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• v.3 no.4
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• pp.141-148
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• 2016

This paper proposes a medium access control (MAC) protocol for multiple-input multiple-output (MIMO) ad-hoc networks. Multiple-packet receptions in MIMO systems have attracted as a key technique to achieve a high transmission rate. In the conventional protocols for multiple-packet receptions, timing offsets among multiple-frame transmissions cause frame collisions induced by hidden nodes, which degrades network performance. In the proposed protocol, transmission synchronization among hidden nodes can be achieved by applying pulse/tone exchanges. By applying the pulse/tone exchanges, multiple-packet receptions among hidden nodes can be achieved, which enhances network throughputs compared with the conventional protocol. Simulation results show effectiveness of the proposed protocol.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.39-45
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• 2011

In this paper, we propose an efficient multichannel MAC protocol with idle nodes assistance to avoid the multi-channel hidden terminal problem in cognitive radio ad hoc network and further to improve the performance of the network. The proposed MAC protocol can be applied to the cognitive radio adhoc network where every node is equipped with the single transceiver and one common control channel exists for control message negotiation. In the proposed protocol, the idle nodes available in the neighbour of communication nodes are utilized because the idle nodes have the information about the channels being utilized in their transmission range. Whenever the nodes are negotiating for the channel, idle nodes can help the transmitting and receiving nodes to select the free data channel for data transfer. With the proposed scheme, we can minimize the hidden terminal problem and decrease the collision between the secondary users when selecting the channel for data transfer. As a result, the performance of the network is increased.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1312-1320
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• 2003

In this paper, we consider a multilayer neural network, with a single hidden layer. Error backpropagation learning method used widely in multilayer neural networks has a possibility of local minima due to the inadequate weights and the insufficient number of hidden nodes. So we propose a fuzzy supervised learning algorithm by using self-generation that self-generates hidden nodes by the compound fuzzy single layer perceptron and modified ART1. From the input layer to hidden layer, a modified ART1 is used to produce nodes. And winner take-all method is adopted to the connection weight adaptation, so that a stored pattern for some pattern gets updated. The proposed method has applied to the student identification card images. In simulation results, the proposed method reduces a possibility of local minima and improves learning speed and paralysis than the conventional error backpropagation learning algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.684-702
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• 2011

This paper proposes a TDMA-based multichannel medium access control (MAC) protocol for QoS provisioning in mobile ad hoc networks (MANETs) that enables nodes to transmit their packets in distributed channels. The IEEE 802.11 standard supports multichannel operation at the physical (PHY) layer but its MAC protocol is designed only for a single channel. The single channel MAC protocol does not work well in multichannel environment because of the multichannel hidden terminal problem. Our proposed protocol enables nodes to utilize multiple channels by switching channels dynamically, thus increasing network throughput. Although each node of this protocol is equipped with only a single transceiver but it solves the multichannel hidden terminal problem using temporal synchronization. The proposed energy efficient multichannel MAC (EM-MAC) protocol takes the advantage of both multiple channels and TDMA, and achieves aggressive power savings by allowing nodes that are not involved in communications to go into power saving "sleep mode". We consider the problem of providing QoS guarantee to nodes as well as to maintain the most efficient use of scarce bandwidth resources. Our scheme improves network throughput and lifetime significantly, especially when the network is highly congested. The simulation results show that our proposed scheme successfully exploits multiple channels and significantly improves network performance by providing QoS guarantee in MANETs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.516-518
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• 2022

In this paper, we introduce a dependence of number of nodes of hidden-layer in fall detection system using Long Short-Term Memory that can detect falls. Its training is carried out using the parameter theta(θ), which indicates the angle formed by the x, y, and z-axis data for the direction of gravity using a 3-axis acceleration sensor. In its learning, validation is performed and divided into training data and test data in a ratio of 8:2, and training is performed by changing the number of nodes in the hidden layer to increase efficiency. When the number of nodes is 128, the best accuracy is shown with Accuracy = 99.82%, Specificity = 99.58%, and Sensitivity = 100%.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.83-88
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• 2004

The Classification of defected oil-seals using a vision system with the artificial neural network is presented. The artificial neural network fur classification consists of 27 input nodes, 10 hidden nodes, and one output node. The selection of the number of the input nodes is based on an observation that the difference among the defected, non-defected, and smeared oil-seals is greatly pronounced in the 26 step gray-scale level thresholding. The number of the hidden nodes is chosen as a result of a trade-off between accuracy and computing time. The back-propagation algorithm is used for teaching the network. The proposed network is capable of successfully classifying the defected from the smeared oil-seals which tend to be classified as the defected ones using the binary thresholding. It is envisaged that the proposed method improves the reliability and productivity of the automotive vision inspection system.