• Design & Manufacturing
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• v.14 no.3
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2315-2326
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• 2016

This paper discusses the elimination of DC voltage deviation and the enhancement of load current sharing accuracy in multi-terminal high voltage direct current (MT-HVDC) systems. In order to minimize the power line losses in different parallel network topologies and to insure the stable operation of systems, a decentralized control method based on a modified droop control is presented in this paper. Averaging the DC output voltage and averaging the output current of two neighboring converters are employed to reduce the congestion of the communication network in a control system, and the decentralized control method is implemented. By minimizing the power loss of the cable, the optimal load current sharing proportion is derived in order to achieve rational current sharing among different converters. The validity of the proposed method using a low bandwidth communication (LBC) network for different topologies is verified. The influence of the parameters of the power cable on the control system stability is analyzed in detail. Finally, transient response simulations and experiments are performed to demonstrate the feasibility of the proposed control strategy for a MT-HVDC system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7B
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• pp.550-556
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• 2008

Swam intelligence refers that a large group of simple and unsophisticated entities work together to achieve complex and significant tasks. Researches using such swarm intelligence has been performed in the network routing area. Expecially, it has been well known that routing in mobile ad-hoc networks whose features are dynamic topology and routing based on the local information is one of the applications of swarm intelligence. In this paper, we propose an ant-based routing method for MANET. The proposed method sets its goals to reduce overheads by managing ants efficiently, and to reduce route set up time. The results obtained from simulations proved that the proposed method provides shorter path set-up time and end-to-end delay and less overhead while providing comparable packet delivery ratio.

• ETRI Journal
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• v.38 no.1
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• pp.41-51
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• 2016

Smart metering systems have become widespread around the world. RF mesh communication systems have contributed to the creation of smarter and more reliable power systems. This paper presents an algorithm for positioning GPRS concentrators to attain delay constraints for a ZigBee-based mesh network. The proposed algorithm determines the number and placement of concentrators using integer linear programming and a queueing model for the given mesh network. The solutions given by the proposed algorithm are validated by verifying the communication network performance through simulations.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.9
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• pp.37-44
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• 2002

This paper proposes efficient digital hardware design method by using fixed weight of pre-trained neural network. For this, arithmetic operations of PEs(Processing Elements) are represented with matrix-vector multiplication. The relationship of fixed weight and input data present bit-level array structure architecture which is consisted operation node. To minimize the operation node, this paper proposes node elimination method and setting common node depend on bit pattern of weight. The result of FPGA simulation shows the efficiency on hardware cost and operation speed with full precision. And proposed design method makes possibility that many PEs are implemented to on-chip.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2627-2629
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• 2003

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized networks, and to be much more flexible and preferable neural network than the conventional SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented with using nonlinear system data.

• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.431-434
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• 2016

Currently, the radial basis function network (RBFN) and various other neural networks are employed to classify gases using chemical sensors arrays, and their performance is steadily improving. In particular, the identification performance of the RBFN algorithm is being improved by optimizing parameters such as the center, width, and weight, and improved algorithms such as the radial basis function network-stochastic gradient (RBFN-SG) and radial basis function network-normalized stochastic gradient (RBFN-NSG) have been announced. In this study, we optimized the number of centers, which is one of the parameters of the RBFN-NSG algorithm, and observed the change in the identification performance. For the experiment, repeated measurement data of 8 samples were used, and the elbow method was applied to determine the optimal number of centers for each sample of input data. The experiment was carried out in two cases(the only one center per sample and the optimal number of centers obtained by elbow method), and the experimental results were compared using the mean square error (MSE). From the results of the experiments, we observed that the case having an optimal number of centers, obtained using the elbow method, showed a better identification performance than that without any optimization.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.10
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• pp.1079-1086
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• 2013

Optimal mount position of a front pillar trim considering heat resistant characteristics can be determined by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1311-1324
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• 2019

In the traditional cellular network communication, the cellular user and the base station exchange information through the uplink channel and downlink channel. Meanwhile, device-to-device (D2D) users access the cellular network by reusing the channel resources of the cellular users. However, when cellular user channel conditions are poor, not only D2D user cannot reuse its channel resources to access the network, but also cellular user's communication needs cannot be met. To solve this problem, we introduced a novelty D2D communication mechanism in the downlink, which D2D transmitter users as half-duplex (HD) relays to assist the downlink transmission of cellular users with reusing corresponding spectrum. The optimization goal of the system is to make the cellular users in the bad channel state meet the minimum transmission rate requirement and at the same time maximize the throughput of the D2D users. In addition, i for the purpose of improving the efficiency of relay transmission, we use two-antenna architecture of D2D relay to enable receive and transmit signals at the same time. Then we optimized power of base station and D2D relay separately with consideration of backhaul interference caused by two-antenna architectures. The simulation results show that the proposed HD relay strategyis superior to existing HD and full-duplex (FD) models in the aspects of system throughput and power efficiency.