• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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• pp.45-56
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• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.391-394
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• 2009

현재 산업사회의 발달과 더불어 신뢰성 높은 양질의 전기에너지와 운전 및 보수의 간편화, 계통운용의 신뢰성의 확보가 요구되고 있다. 또한 $SF_6$을 대체할 친환경적인 절연매체의 개발이 절실히 요구되고 있다. 이에 본 연구는 모의 GIS내 제조공기의 압력 및 캡 변화에 따른 전원별 절연특성을 구명하였다. 또한 배전급 전력설비에 적용을 위하여 제조공기와 $SF_6$의 절연특성을 비교 평가하였다. 제조공기의 절연특성을 구명함으로서 각종 전력응용 설비의 절연설계에 $SF_6$ 대신 친환경적인 모의공기를 사용 가능함을 본 연구에서 구명하였다. 현재 송배전 설비의 절연매체로 사용하고 있는 $SF_6$ 3[atm] 에 상응하는 제조공기의 압력을 인공신경망을 이용하여 추정하여 제조공기를 이용한 친환경적인 절연재 사용으로 지구의 온난화현상 감소에 크게 기여할 것으로 기대된다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.527-536
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• 2020

It has been found that an attacker can extract the secret key embedded in a security device and recover the operation instruction using power consumption traces which are some kind of side channel information. Many profiling-based side channel attacks based on a deep learning model such as MLP(Multi-Layer Perceptron) method are recently researched. In this paper, we implemented a disassembler for operation instruction set used in the micro-controller AVR XMEGA128-D4. After measuring the template traces on each instruction, we automatically made the pre-processing process and classified the operation instruction set using a deep learning model CNN. As an experimental result, we showed that all instructions are classified with 87.5% accuracy and some core instructions used frequently in device operation are with 99.6% respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1779-1785
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• 2006

This paper presents a method of virtual robot arm control by EMG pattern recognition using the proposed hybrid system. The proposed hybrid system is composed of the LVQ and the SOFM, and the SOFM is used for the preprocessing of the LVQ. The SOFM converts the high dimensional EMG signals to 2-dimensional data. The EMG measurement system uses three surface electrodes to acquire the EMG signal from operator. Six hand gestures can be classified sufficiently by the proposed hybrid system. Experimental results are presented that show the effectiveness of the virtual robot arm control by the proposed hybrid system based classifier for the recognition of hand gestures from EMG signal patterns.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.7
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• pp.1064-1068
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• 2002

In this paper, we introduce a hyper-chaos synchronization method using State-Controlled Cellular Neural Network(SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scrolls with weak coupled method, to each cell. Hyper-chaos synchronization was achieved using drive response synchronization between the transmitter and receiver about each state in the SC-CNN. From the result of the recovery signal through the demodulation method in the receiver, We shown that recovery quality of state variable $$\chi$_3$ is superior to that of ${$\chi$_2}, {$\chi$_1}$ in secure communication.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2365-2370
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• 2010

The method of cancer classification based on microarray could contribute to being accurate cancer classification by finding differently expressing gene pattern statistically according to a cancer type. Therefore, the process to select a closely related informative gene with a particular cancer classification to classify cancer using present microarray technology with effect is essential. In this paper, the system can detect marker genes to likely express the most differentially explaining the effects of cancer using ovarian cancer microarray data. And it compare and analyze a performance of classification of the proposed system with it of established microarray system using multi-perceptron neural network layer. Microarray data set including marker gene that are selected using ANOVA method represent the highest classification accuracy of 98.61%, which show that it improve classification performance than established microarray system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1647-1652
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.387-392
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• 2001

This Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling for grouped failure data that is able to predict cumulative failures in the variable future time. The two variant models of cascade-correlation learning (CasCor) algorithm are presented. Suggested models are compared with other well-known NN models and statistical software reliability growth models (SRGMs). Experimental results show that the suggested models show better predictability.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.395-402
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• 2018

Human palms contain discriminative features for proving the identity of each person. In this paper, we present a novel method for user verification based on palmprints and palm veins. Specifically, the region of interest (ROI) is first determined to be forced to include the maximum amount of information with respect to underlying structures of a given palm image. The extracted ROI is subsequently enhanced by directional patterns and statistical characteristics of intensities. For multispectral palm images, each of convolutional neural networks (CNNs) is independently trained. In a spirit of ensemble, we finally combine network outputs to compute the probability of a given ROI image for determining the identity. Based on various experiments, we confirm that the proposed ensemble method is effective for user verification with palmprints and palm veins.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.