• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.3
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• pp.75-84
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• 2005

Since an unexpected fault of induction motor drive systems can cause serious troubles in many industrial applications, which the technique is required to diagnose faults of a voltage-fed PWM inverter for induction motor drives. The considered fault types are rectifier diodes, switching devices and input terminals with open-circuit faults, and the signal for diagnosis is derived from motor currents. The magnitude of dq-current trajectory is used for the feature extraction of a fault and PCA LDA are applied to diagnose. Also, we show results with respect to the execution time because of the possibility to use that a diagnosis software is embedded in the controllers of medium and small size induction motors drive for real-time diagnosis. After we performed various simulations for the fault diagnosis of the inverter, the usefulness of proposed algerian was verified.

• The Journal of the Korea Contents Association
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• v.11 no.8
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• pp.14-22
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• 2011

It is important for the strategy of service to provide the health information in the environment that the healthcare has been changed focusing on the preventive medicine. Recently, the various applications of u-healthcare have been presented by researchers. In this paper, we proposed the health information monitoring system using the context sensors based band. By wearing the proposed hand, the health status is gathered and vital signals are transmitted to the connected UMPC. It can be easily monitored according to the user locations in real time. To provide the health index according to the temperature, the air conditioning, the illumination, the humidity, and the ultraviolet rays, we use the various XML links extracted from RSS of the Korea Meteorological Administration. The health information is analyzed in terms of factors, such as, the asthma index, the stroke index, the skin disease index, the pulmonary disease index, the pollen concentration index, and the city high temperature index. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the proposed system. Accordingly, the satisfaction and the quality of services will be improved the healthcare.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.33-40
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• 2006

This paper presents an efficient face recognition method using both first moment of image and eigenvector. First moment is a method for finding centroid of image, which is applied to exclude the needless backgrounds in the face recognitions by shitting to the centroid of face image. Eigenvector which are the basis images as face features, is extracted by principal component analysis(PCA). This is to improve the recognition performance by excluding the redundancy considering to second-order statistics of face image. The proposed methods has been applied to the problem for recognizing the 60 face images(15 persons *4 scenes) of 320*243 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. In case of the 45 face images, the experimental results show that the recognition rate of the proposed methods is about 1.6 times and its the classification is about 5.6 times higher than conventional PCA without preprocessing. The city-block has been relatively achieved more an accurate classification than Euclidean or negative angle.

• The Journal of the Acoustical Society of Korea
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• v.30 no.5
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• pp.255-264
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• 2011

The Head Related Transfer Function (HRTF) means a process related to acoustic transmission from 3d space to the listener's ear. In other words, it contains the information that human can perceive locations of sound sources. So, we make virtual 3d sound using HRTF, despite it doesn't actually exist. But, it can deteriorate some three-dimensional effect by the confusion between front and back directions due to the non-individual HRTF depending on each listener. In this paper, we proposed the new algorithm to reduce the confusion of sound image localization using human's acoustic characteristics. The frequency spectrum and global masking threshold of 3d sounds using HRTF are used to calculate the psychoacoustical differences among each directions. And perceptible cues in each critical band are boosted to create effective 3d sound. As a result, we can make the improved 3d sound, and the performances are much better than conventional methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.382-382
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• 2011

최근의 극심한 기상이변으로 인하여 발생되는 유출량의 예측에 관한 사항은 치수 이수는 물론 방재의 측면에서도 역시 매우 중요한 관심사로 부각되고 있다. 강우-유출 관계는 유역의 수많은 시 공간적 변수들에 의해 영향을 받기 때문에 매우 복잡하여 예측하기 힘든 요소이며, 과거에는 추계학적 예측모형이나 확정론적 예측모형 혹은 경험적 모형 등을 사용하여 유출량을 예측하였으나 최근에는 인공신경망과 퍼지모형 그리고 유전자 알고리즘과 같은 인공지능기반의 모형들이 많이 사용되고 있다. 하지만 유출량을 예측하고자 할 때 학습자료 및 검정자료로써 사용되는 유출량은 수위-유량 관계곡선식으로부터 구하는 경우가 대부분으로 이는 이렇게 유도된 유출량의 경우 오차가 크기 때문에 그 신뢰성에 문제가 있을 것으로 판단된다. 따라서 본 논문에서는 수위를 직접 예측함으로써 이러한 오차의 문제점을 극복 하고자 한다. Neuro-Fuzzy 모형은 과거자료의 입 출력 패턴에서 정보를 추출하여 지식으로 보유하고, 이를 근거로 새로운 상황에 대한 해답을 제시하도록 하는 인공지능분야의 학습기법으로 인간이 과거의 경험과 훈련으로 지식을 축적하듯이 시스템의 입 출력에 의하여 소속함수를 최적화함으로서 모형의 구조를 스스로 조직화한다. 따라서 수학적 알고리즘의 적용이 어려운 강우와 유출관계를 하천유역이라는 시스템에서 발생된 신호체계의 입 출력패턴으로 간주하고 인간의 사고과정을 근거로 추론과정을 거쳐 수문계의 예측에 적용할 수 있을 것이다. 유전자 알고리즘은 적자생존의 생물학 원리에 바탕을 둔 최적화 기법중의 하나로 자연계의 생명체 중 환경에 잘 적응한 개체가 좀 더 많은 자손을 남길 수 있다는 자연선택 과정과 유전자의 변화를 통해서 좋은 방향으로 발전해 나간다는 자연 진화의 과정인 자연계의 유전자 메커니즘에 바탕을 둔 탐색 알고리즘이다. 즉, 자연계의 유전과 진화 메커니즘을 공학적으로 모델화함으로써 잠재적인 해의 후보들을 모아 군집을 형성한 뒤 서로간의 교배 혹은 변이를 통해서 최적 해를 찾는 계산 모델이다. 이러한 유전자 알고리즘은 전역 샘플링을 중심으로 한 수법으로 해 공간상에서 유전자의 개수만큼 복수의 탐색점을 설정할 뿐만 아니라 교배와 돌연변이 등으로 좁아지는 탐색점 바깥의 영역으로 탐색을 확장할 수 있기 때문에 지역해에 빠질 위험성이 크게 줄어든다. 따라서 예측과 패턴인식에 강한 뉴로퍼지 모형의 해 탐색방법을 유전자 알고리즘을 사용한다면 보다 정확한 해를 찾는 것이 가능할 것으로 판단된다. 따라서 본 논문에서는 선행우량 및 상류의 수위자료로부터 하류의 단시간 수위예측에 관해 연구하였으며, 이를 위해 유전자 알고리즘을 이용항여 소속함수를 최적화 시키는 형태의 Neuro-Fuzzy모형에 대하여 연구하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12A
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• pp.1876-1882
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• 1999

In this paper, we have proposed a FCB detection algorithm for the GSM system which is european cellular standard. The detection algorithm can be implemented using received signal sampler, correlator, and post detection combiner. GSM mobile phone can use the proposed algorithm for detection of the Broadcasting Channel, and to obtain the initial timing estimate. The proposed algorithm has a architecture which is suitable for DSP or ASIC implementation, and required memory size is small. The performance of the algorithm is a function of the processing data window size and the threshold values. Proper window size and the threshold values can be determined by analyzing the correlator and combiner. The proposed algorithm has been implemented using DSP, and the performance was verified using baseband simulation. The simulation assumed frequency offset values of 0ppm and 15ppm with the receiver filter bandwidth set at both minimum and maximum. It is shown that the algorithm is robust under various assumptions, and suitable for real implementations.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1143-1155
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• 1999

셀룰러 방식의 이동통신 시스템에서 전파의 유효신호 도달범위를 예측하기 위해서는 전파전파 모델을 이용한 예측기법이 주로 사용된다. 그러나, 전파과정에서 주변 지형지물에 의해 발생하는 전파손실은 매우 복잡한 비선형적인 특성을 가지며 수식으로는 정확한 표현이 불가능하다. 본 논문에서는 신경회로망의 함수 근사화 능력을 이용하여 전파손실 예측모델을 생성하는 방법을 제안한다. 즉, 전파손실을 송수신 안테나간의 거리, 송신안테나의 특성, 장애물 투과영향, 회절특성, 도로, 수면에 의한 영향 등과 같은 전파환경 변수들의 함수로 가정하고, 신경회로망 학습을 통하여 함수를 근사화한다. 전파환경 변수들이 신경회로망 입력으로 사용되기 위해서는 3차원 지형도와 벡터지도를 이용하여 전파의 반사, 회절, 산란 등의 물리적인 특성이 고려된 특징 추출을 통해 정량적인 수치들을 계산한다. 이와 같이 얻어진 훈련데이타를 이용한 신경회로망 학습을 통해 전파손실 모델을 완성한다. 이 모델을 이용하여 서울 도심 지역의 실제 서비스 환경에 대한 타 모델과의 비교실험결과를 통해 제안하는 모델의 우수성을 보인다.Abstract In cellular mobile communication systems, wave propagation models are used in most cases to predict cell coverage. The amount of propagation loss induced by the obstacles in the propagation path, however, is a highly non-linear function, which cannot be easily represented mathematically. In this paper, we introduce the method of producing propagation loss prediction models by function approximation using neural networks. In this method, we assume the propagation loss is a function of the relevant parameters such as the distance from the base station antenna, the specification of the transmitter antenna, obstacle profile, diffraction effect, road, and water effect. The values of these parameters are produced from the field measurement data, 3D digital terrain maps, and vector maps as its inputs by a feature extraction process, which takes into account the physical characteristics of electromagnetic waves such as reflection, diffraction and scattering. The values produced are used as the input to the neural network, which are then trained to become the propagation loss prediction model. In the experimental study, we obtain a considerable amount of improvement over COST-231 model in the prediction accuracy using this model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10A
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• pp.1065-1071
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• 2007

In this paper, in order to research spectrum usage efficiency in urban canyon environment at the microwave band, measurement and channel capacity analysis of multi-antenna technology is described. The measurement data obtained from 3 - 4 stories building area used and the propagation characteristics at the 3.7 and 8GHz band are analysed and compared. In case of $2{\times}2$ MIMO, channel capacities of 3.7 and 8 GHz band are calculated to 9.1 bps/Hz and S bps/Hz and in case of $4{\times}4$ MIMO, 21 bps/Hz and 12.5 bps/Hz respectively. Considering the coverage, SNR and channel capacity in urban environment, MIMO propagation characteristics of 3.7 GHz are more predominate than those of 8 GHz.

• Analytical Science and Technology
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• v.25 no.1
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• pp.60-68
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• 2012

A gas chromatography-mass spectrometric (GC-MS) method was developed for determining 25 semivolatile organic compounds in water. A 1.0 L water sample was placed in a separatory funnel and saturated with NaCl, and the solution was extracted two times with 40 mL of methylene chloride. Under the established condition, the linear quantification range was 0.02-800 ng/L and the relative standard deviation was less than 15%. The method was used to analyze 16 surface water samples collected from various regions in Gum-River. The samples revealed SVOC concentrations in the range of 0.02-96.8 ng/L. Maximum concentrations of VOCs detected were not exceeded the EPA or Germany guidelines in any of the samples. The developed method may be valuable for monitoring SVOCs in water.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.