• Journal of IKEEE
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• v.22 no.3
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• pp.870-873
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• 2018

This paper presents the high frequency resonant inverter using ZVS(Zero Voltage Switching) known as the soft switching technology, which can reduce the turn-on and turn-off switching losses. Also, the analysis of the proposed resonant inverter is described by adopting normalized parameters, and its operating characteristics are evaluated according to the switching frequency and parameters. An example of 1.3[kW] IH-Jar design technique is presented based on the characteristic values obtained from the theoretical analysis. To prove the validity of the theoretical analysis, the experimental results using IGBT as the switching devices are additionally presented. In the future, it can be practically used in various power systems such as induction heating cooking, IH-Jar etc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.626-628
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• 2018

In this paper, we compare the performances of SVM (Support Vector Machine) and ANN (Artificial Neural Network) machine learning models for predicting solar energy by using meteorological data. Two machine learning models were built by using fifteen kinds of weather data such as long and short wave radiation average, precipitation and temperature. Then the RBF (Radial Basis Function) parameters in the SVM model and the number of hidden layers/nodes and the regularization parameter in the ANN model were found by experimental studies. MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) were considered as metrics for evaluating the performances of the SVM and ANN models. Sjoem Simulation results showed that the SVM model achieved the performances of MAPE=21.11 and MAE=2281417.65, and the ANN model did the performances of MAPE=19.54 and MAE=2155345.10776.

• Journal of Digital Contents Society
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• v.1 no.1
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• pp.53-64
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• 2000

This paper is related to color image segmentation scheme which makes it possible to achieve the excellent segmented results by block-based segmentation using Y/C bit-plane summation image. First, normalized chrominance summation image is obtained by normalizing the image which is summed up the absolutes of color-differential values between R, G, B images. Secondly, upper 2 bits of the luminance image and upper 6bits of and the normalized chrominance summation image are bitwise operated by the pixel to generate the Y/C bit-plane summation image. Next, the Y/C bit-plane summation image divided into predetermined block size, is classified into monotone blocks, texture blocks and edge blocks, and then each classified block is merged to the regions including one more blocks in the individual block type, and each region is selectively allocated to unique marker according to predetermined marker allocation rules. Finally, fine segmented results are obtained by applying the watershed algorithm to each pixel in the unmarked blocks. As shown in computer simulation, the main advantage of the proposed method is that it suppresses the over-segmentation in the texture regions and reduces computational load. Furthermore, it is able to apply global parameters to various images with different pixel distribution properties because they are nonsensitive for pixel distribution. Especially, the proposed method offers reasonable segmentation results in edge areas with lower contrast owing to the regional characteristics of the color components reflected in the Y/C bit-plane summation image.

• Journal of the Korean Society of Radiology
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• v.3 no.1
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• pp.29-39
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• 2009

In this paper has proposed to the recognition of the disease on medical images using neural network. The neural network is constructed as three-layers of the input-layer, the hidden-layer and the output-layer. The training method applied for the recognition of disease region is adaptive error back-propagation. The low-frequency region analyzed by DWT are expressed by matrix. The coefficient-values of the characteristic polynomial applied are n+1. The normalized maximum value +1 and minimum value -1 in the range of tangent-sigmoid transfer function are applied to be use as the input vector of the neural network. To prove the validity of the proposed methods used in the experiment with a simulation experiment, the input medical image recognition rate the evaluation of areas of disease. As a result of the experiment, the characteristic polynomial coefficient of low-frequency area matrix, conversed to 4 level DWT, was proved to be optimum to be applied to the feature parameter. As for the number of training, it was marked fewest in 0.01 of learning coefficient and 0.95 of momentum, when the adaptive error back-propagation was learned by inputting standardized feature parameter into organized neural network. As to the training result when the learning coefficient was 0.01, and momentum was 0.95, it was 100% recognized in fifty-five times of the stomach image, fifty-five times of the chest image, forty-six times of the CT image, fifty-five times of ultrasonogram, and one hundred fifty-seven times of angiogram.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.8-14
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• 2002

In this paper, we realized a real-time text-independent speaker recognition system using gaussian mixture model, and applied frame level likelihood normalization method which shows its effects in verification system. The system has three parts as front-end, training, recognition. In front-end part, cepstral mean normalization and silence removal method were applied to consider speaker's speaking variations. In training, gaussian mixture model was used for speaker's acoustic feature modeling, and maximum likelihood estimation was used for GMM parameter optimization. In recognition, likelihood score was calculated with speaker models and test data at frame level. As test sentences, we used text-independent sentences. ETRI 445 and KLE 452 database were used for training and test, and cepstrum coefficient and regressive coefficient were used as feature parameters. The experiment results show that the frame-level likelihood method's recognition result is higher than conventional method's, independently the number of registered speakers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.80-87
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• 2007

In this paper, we investigate an enhancement method for Western epigraphic images, which is based on local statistics. Image data is partitioned into two regions, background and information. Statistical and functional analyses are proceeded for image modeling. The Western epigraphic images, for the most part, have shown the Gaussian distribution. It is clarified that each region can be differentiated statistically. The local normalization process algorithm is designed on this model. The parameter is extracted and it‘s properties are verified with the size of moving window. The spatial gray-level distribution is modified and regions are differentiated by adjusting parameter and the size of moving window. Local statistics are utilized for realization of the enhancement, so that difference between regions can be enhanced and noise or speckles of region can be smoothed. Experimental results are presented to show the superiority of the proposed algorithm over the conventional methods.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.4
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• pp.134-138
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• 2015

Heart sound is used for a basic clinical examination to check for abnormalities in the lungs and heart that can be heard with a stethoscope or phonocardiography. In this paper, we try to find an easier and non-invasive method to diagnose heart diseases using neural network classifier. The classifier has been developed for one normal heart sound and five murmurs by using Shannon entropy and conjugate scaled back propagation algorithm. The experimental results showed that the classification is possible with 1.63185e-6 of classification error.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.112-118
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• 2010

This paper describes a noise suppression system based on a normalization method using a time-delay neural network and line spectrum pair having a parameter of frequency domain. First, a time-delay neural network is trained using line spectrum pair values of noisy speech signals obtained by linear prediction analysis. After trained the time-delay neural network, the proposed system enhances speech signals that are degraded by a background noise. Accordingly, the proposed time-delay neural network restores from the line spectrum pair values of noisy speech signals to the line spectrum pair values of clean speech signals. It is confirmed that this system is effective for speech signals degraded by a background noise, judging from spectral distortion measurement.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1954-1955
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• 2011

본 연구에서는 교육용으로 제작된 NXT 장비에 설치된 Light 센서, Ultrasonic센서, Sound센서를 이용하여 각 거리(10~60cm)에서 5cm 간격으로 각 센서 데이터를 취득하였다. 데이터 취득은 NI(National Instrument)에서 제공하는 LabVIEW Software를 사용하여 각 거리마다 100개의 셈플 데이터를 취득하였다. 취득한 데이터는 제안한 모델의 입력 데이터로 사용하여 실제거리와 모델 출력과의 정확도를 평가 하였다. 본 연구에서 제안한 모델은 지능형 모델 중 퍼지추론 기반의 최적 다항식 RBF 뉴럴네트워크(Radial Basis Function Neural Network; RBFNN)를 설계한다. 제안된 RBFNN은 기존 RBF 뉴럴네트워크를 기반으로 한 구조로, 퍼지추론 메커니즘의 기능적 모듈 동작 특성을 갖도록 정규화 부분을 추가하고, 은닉층과 출력층 사이의 연결가중치를 기존 상수항에서 선형식(first order)으로 확장한 형태이다. 또한 최적의 알고리즘인 PSO(Paticle Swarm Optimization)와 DE(Differential Evolution)을 이용하여 제안된 모델의 파라미터들을 동정하여 성능을 비교, 분석 하였다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.4
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• pp.9-16
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• 2005

In this paper, electronic ballast using double resonant inverter for 250[W] MHD lamp is designed and implemental Electronic ballast reduce a turn-onion loss by using ZVS(Zero Voltage Switching) technic in the double resonant inverter, and when circuit have an analysis, the characteristic evaluations is described generally by using the normalized i:ぉ used numerical analysis. We conform a rightfulness of theoretical analysis by experimental waveforms, rouble resonant inverter for 250[W]) MHD lamp is operated safely.