• Journal of Electrical Engineering and Technology
• /
• v.12 no.1
• /
• pp.207-216
• /
• 2017

Wind energy conversion systems (WECS) which consist of wind turbines with permanent magnet synchronous generator (PMSG) and full-power converters have become widespread in the field of renewable power systems. Generally, conventional diode bridge rectifiers have used to obtain a constant DC bus voltage from output of PMSG based wind generator. In recent years, together advanced power electronics technology, Pulse Width Modulation (PWM) rectifiers have used in WECS. PWM rectifiers are used in many applications thanks to their characteristics such as high power factor and low harmonic distortion. In general, L, LC and LCL-type filter configurations are used in these rectifiers. These filter configurations are not exactly compensate current and voltage harmonics. This study proposes a hybrid passive filter configuration for PWM rectifiers instead of existing filters. The performance of hybrid passive filter was tested via MATLAB/Simulink environment under various operational conditions and was compared with LCL filter structure. In addition, neuro-fuzzy controller (NFC) was preferred to increase the performance of PWM rectifier in DC bus voltage control against disturbances because of its robust and nonlinear structure. The study demonstrates that the hybrid passive filter configuration proposed in this study successfully compensates current and voltage harmonics, and improves total harmonic distortion and true power factor.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.231-235
• /
• 2001

Insulation aging diagnosis system provides early warning regarding electrical equipment defect. Early warning is very important in that it can avoid great losses resulting from unexpected shutdown of the production line. Since relations of insulation aging and partial discharge dynamics are non-linear, it is very difficult to provide early warning in an electrical equipment. In this paper, we propose the design method of insulation aging diagnosis system that use a magnetic wave and acoustic signal to diagnoses an electrical equipment. Proposed system measures the partial discharge on-line from DAS(Data Acquisition System) and acquires 2D Patterns from analyzing it. For fettering the noise contained in sensor signals we used ICA algorithms. Using this data design of the neuro-fuzzy model that diagnoses an electrical equipment is investigated. Validity of the new method is asserted by numerical simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.2
• /
• pp.647-669
• /
• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2846-2849
• /
• 2002

본 논문은 뉴로-퍼지 시스템에서의 규칙 선택 및 모델 학술에 대하여 EM 알고리즘을 기반으로 하는 구조 동정을 제안한다. 뉴로-퍼지 모델링에서의 초기 파라미터가 학습과정에서의 모델 성능에 큰 영향을 주고 있다. 주어진 데이터에 근거한 파라미터 추정에는 다양한 방법들이 소개되고 응용되어져 왔는데 이전 연구들에서 볼 수 있는 HCM, FCM 등은 데이터와의 유클리디언 거리를 최소화하는 중심점을 파라미터로 선택하는 등의 방법과 퍼지 균등화 등은 데이터의 확률 밀도함수를 이용하여 파라미터를 추정하였다. 제안된 방법에서는 데이터에서의 Maximum Likelihood Estimator를 기반으로 하는 방법으로 EM 알고리즘을 이용하였다. 초기 파라미터의 결정에서 EM 알고리즘을 이용하여 뉴로-퍼지 모델의 전제부 소속함수 파라미터 추정을 실시한다. EM 알고리즘을 이용한 퍼지 모델의 특징으로는 전제부가 클러스터링에 의하여 생성되므로 입력의 차원이나 소속함수의 수가 증가하여도 규칙의 수는 증가하지 않는다. 이를 자동차 MPG 예제를 통하여 제안된 방법의 유용성을 보이고자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.04a
• /
• pp.289-292
• /
• 2005

본 논문에서는 클러스터링과 뉴로-퍼지 모델링을 동시에 실시하는 학습 기법을 제안하였다. 클러스터링을 이용하여 뉴로-퍼지 모델링을 실시하는 일반적인 경우, 클러스터링 학습을 실시한 후 학습된 파라미터를 뉴로-퍼지 모델의 초기 파라미터로 설정하고 모델을 다시 학습하는 방법을 취한다. 즉 클러스터링에서 클러스터의 수를 구하고 파라미터를 최적화함으로써 초기 구조동정과 파라미터 동정을 실시하며 이를 다시 뉴로-퍼지 모델에서 세부적인 파라미터 동정을 실시하는 것이다. 또한 모델에서의 학습은 출력데이터의 오차를 이용한 오차미분기반 학습으로 전제부 소속함수 파라미터를 수정하는 방법을 이용한다. 이 경우 클러스터링의 영향과 모델의 영향이 각각 별개로 고려될 수 있다. 따라서 본 논문에서는 클러스터링을 전제부 소속함수로 부여하고 클러스터링의 학습에 뉴로-퍼지 모델을 이용하면서 또한 모델의 학습에 클러스터링을 직접 적용하는 클러스터링 기반 뉴로-퍼지 모델링을 제안하였으며 이 경우 클러스터링의 학습과 모델의 학습이 동시에 이루어지며 뉴로-퍼지 모델에서 클러스터링의 효과를 직접적으로 확인할 수 있다. 제안된 방법의 유용성을 시뮬레이션을 통하여 보이고자 한다.

• Computers and Concrete
• /
• v.16 no.3
• /
• pp.399-414
• /
• 2015

Nowadays, fiber reinforced polymer (FRP) composites are widely used for rehabilitation, repair and strengthening of reinforced concrete (RC) structures. Also, recent advances in concrete technology have led to the production of high strength concrete, HSC. Such concrete due to its very high compression strength is less ductile; so in seismic areas, ductility is an important factor in design of HSC members (especially FRP strengthened members) under flexure. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and multiple regression analysis are used to predict the curvature ductility factor of FRP strengthened reinforced HSC (RHSC) beams. Also, the effects of concrete strength, steel reinforcement ratio and externally reinforcement (FRP) stiffness on the complete moment-curvature behavior and the curvature ductility factor of the FRP strengthened RHSC beams are evaluated using the analytical approach. Results indicate that the predictions of ANFIS and multiple regression models for the curvature ductility factor are accurate to within -0.22% and 1.87% error for practical applications respectively. Finally, the effects of height to wide ratio (h/b) of the cross section on the proposed models are investigated.

• Journal of Electrical Engineering and Technology
• /
• v.5 no.2
• /
• pp.209-219
• /
• 2010

In this paper, a novel dynamic available transfer capability (DATC) has been computed for real time applications using three different intelligent techniques viz. i) back propagation algorithm (BPA), ii) radial basis function (RBF), and iii) adaptive neuro fuzzy inference system (ANFIS) for the first time. The conventional method of DATC is tedious and time consuming. DATC is concerned with calculating the maximum increase in point to point transfer such that the transient response remains stable and viable. The ATC information is to be continuously updated in real time and made available to market participants through an internet based Open Access Same time Information System (OASIS). The independent system operator (ISO) evaluates the transaction in real time on the basis of DATC information. The dynamic contingency screening method [1] has been utilized and critical contingencies are selected for the computation of DATC using the energy function based potential energy boundary surface (PEBS) method. The PEBS based DATC has been utilized to generate patterns for the intelligent techniques. The three different intelligent methods are tested on New England 68-bus 16 machine and 39-bus 10 machine systems and results are compared with the conventional PEBS method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.7
• /
• pp.1117-1122
• /
• 2017

Pedestrian detection is a very important and valuable part of artificial intelligence and computer vision. It can be used in various areas for example automatic drive, video analysis and others. Many works have been done for the pedestrian detection. The accuracy of pedestrian detection on multiple pedestrian image has reached high level. It is not easily get more progress now. This paper proposes a new structure based on the idea of HOG and convolutional filters to do the pedestrian detection in single pedestrian image. It can be a method to increase the accuracy depend on the high accuracy in single pedestrian detection. In this paper, we use Multiblock HOG and magnitude of the pixel as the feature and use convolutional filter to do the to extract the feature. And then use NEWFM to be the classifier for training and testing. We use single pedestrian image of the INRIA data set as the data set. The result shows that the Convolutional Multiblock HOG we proposed get better performance which is 0.015 miss rate at 10-4 false positive than the other detection methods for example HOGLBP which is 0.03 miss rate and ChnFtrs which is 0.075 miss rate.

• 한국방재학회:학술대회논문집
• /
• 2008.02a
• /
• pp.405-408
• /
• 2008

Up to now, a lot of houses, roads and other urban facilities have been damaged by natural disasters such as flash floods and landslides. It is reported that the size and frequency of disasters are growing greatly due to global warming. In order to mitigate such disaster, flood forecasting and alerting systems have been developed for the Han river, Geum river, Nak-dong river and Young-san river. These systems, however, do not help small municipal departments cope with the threat of flood. In this study, a real-time urban flood forecasting service (U-FFS) is developed for ubiquitous computing city which includes small river basins. A test bed is deployed at Tan-cheon in Gyeonggido to verify U-FFS. It is found that U-FFS can forecast the water level of outlet of river basin and provide real-time data through internet during heavy rain. Furthermore, it is expected that U-FFS presented in this study can be applied to ubiquitous computing city (u-City) and/or other cities which have suffered from flood damage for a long time.

• Proceedings of the KIEE Conference
• /
• 2007.11a
• /
• pp.242-243
• /
• 2007

본 논문은 1310nm 단일모드 광섬유를 이용하여 온도센서로 활용하기 위한 연구이다. 기존광섬유센서의 연구는 복잡한 여러 가지 기기를 이용하여 구성된 시스템이었다. 그리고 산란 변화를 주기 위하여 Bragg 격자나 Pulse generator를 이용하여 광주파수의 변화를 측정하거나, YAG 레이저를 이용 벌크형 시스템을 택하여 구성하였는데 실험 환경을 구성하는 어려움과 측정된 데이터의 정확도에 대한 문제점이 있었다. 본 연구에서 제안한 유도 브릴루앙 산란(sBs: stimulated Brillouin scattering)광을 이용한 온도센서 시스템은 기존의 측정방식 보다 간소화된 직렬방식의 시스템이다. 광주파수에서 발생하는 노이즈와 애매한 결과에 대해서 신뢰성과 정확도를 확보하기 위하여 지능형인 뉴로-퍼지 알고리즘을 이용하여 분석함으로써 기존 시스템 보다 정확한 데이터를 얻고자 하였다. 본 연구에서 sBs는 빛의 산란 특성 중 광주파수가 온도에 변화에 대해 각각의 온도 변화당 천이가 이루어졌음을 측정하였다. 시스템에서 출력된 데이터를 뉴로-퍼지로 분석한 변화율은 1.1MHz/의 결과를 얻었다.