• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.342-348
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• 2009

This paper introduces a novel denoising algorithm for the partial-discharge(PD) signals from power apparatuses. The developed algorithm includes three kinds of specific denoising sub-algorithms. The first sub-algorithm uses the fuzzy logic which classifies the noise types in the magnitude versus phase PD pattern. This sub-algorithm is especially effective in the rejection of the noise with high and constant magnitude. The second one is the method simply removing the pulses in the phase sections below the threshold count in the count versus phase pattern. This method is effective in removing the occasional high level noise pulses. The last denoising sub-algorithm uses the grouping characteristics of PD pulses in the 3D plot of the magnitude versus phase versus cycle. This special technique can remove the periodical noise pulses with varying magnitudes, which are very difficult to be removed by other denoising methods. Each of the sub-algorithm has different characteristic and shows different quality of the noise rejection. On that account, a parameter which numerically expresses the noise possessing degree of signal, is defined and evaluated. Using the parameter and above three sub-algorithms, an adaptive complex noise rejection algorithm for the on-line PD diagnosis system is developed. Proposed algorithm shows good performances in the various real PD signals measured from the power apparatuses in the Korean plants.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.834-844
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• 2012

Nonlinear electronic loads draw harmonic currents from the power grids that can cause energy loss, miss-operation of power equipment, and other serious problems in the power grids. This paper proposes a harmonic compensation method using multiple distributed resources (DRs) such as small distributed generators (DGs) and battery energy storage systems (BESSs) that are integrated to the power grids through power inverters. For harmonic compensation, DRs should inject additional apparent power to the grids so that certain DRs, especially operated in proximity to their rated power, may possibly reach their maximum current limits. Therefore, intelligent coordination methods of multiple DRs are required for efficient harmonic current compensation considering the power margins of DRs, energy cost, and the battery state-of-charge. The proposed method is based on fuzzy multi-objective optimization so that DRs can cooperate with other DRs to eliminate harmonic currents with optimizing mutually conflicting multi-objectives.

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

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1724-1731
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• 2018

The conventional polynomial neural network (PNN) is a classical flexible neural structure and self-organizing network, however it is not free from the limitation of overfitting problem. In this study, we propose a space search-optimized polynomial neural network (ssPNN) structure to alleviate this problem. Ranking selection is realized by means of ranking selection-based performance index (RS_PI) which is combined with conventional performance index (PI) and coefficients based performance index (CPI) (viz. the sum of squared coefficient). Unlike the conventional PNN, L2-norm regularization method for estimating the polynomial coefficients is also used when designing the ssPNN. Furthermore, space search optimization (SSO) is exploited here to optimize the parameters of ssPNN (viz. the number of input variables, which variables will be selected as input variables, and the type of polynomial). Experimental results show that the proposed ranking selection-based polynomial neural network gives rise to better performance in comparison with the neuron fuzzy models reported in the literatures.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.105-115
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• 2002

This paper proposed a recognition method of english calling card using both 4-directed contour tracking algorithm and enhanced ART1 algorithm. After we extract candidate character string region using horizontal smearing and 4-directed contour tracking method, we extract character string region through comparison of character region and non-character region using horizontal and vertical ratio and area in english calling card. In extracted character string region, we extract each character using horizontal smearing and contour tracking algorithm, and recognize each character by enhanced ART1 algorithm. The proposed ART1 algorithm is enhanced by dynamic control of similarity using fuzzy sum connective operator. The result indicate that the proposed method is superior in performance.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2567-2569
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• 2003

본 논문은 비선형 시스템의 퍼지모델을 위해 정보 Granules 기반 퍼지추론 시스템 모델의 최적화를 제시한다. 퍼지모델은 주로 경험적 방법에 의해 추출되기 때문에 보다 구체적이고 체계적인 방법에 의한 동정 및 최적화 될 필요성이 요구된다. 제안된 규칙베이스 퍼지모델은 HCM 클러스터링 방법, 컴플렉스 알고리즘 및 퍼지추론 방법을 이용하여 시스템 구조와 파라미터 동정을 수행한다. 두 가지 형태의 퍼지모델 추론 방법은 간략추론, 선형추론에 의해 시행된다. 본 논문에서는 퍼지모델의 입력변수와 퍼지 입력 공간 분할 및 입출력 데이타의 중심값을 구해서 후반부 다항식함수에 의한 정보 Granules 기반 구조 동정과 파라미터 동정을 통해 비선형 시스템을 표현한다. 전반부 파라미터의 동정에는 HCM 클러스터링 방법과 컴플렉스 알고리즘을 사용하고, 후반부는 표준 HCM 클러스터링과 표준 최소자승법을 사용하여 동정한다. 그리고 학습 및 테스트 데이타의 성능견과의 상호균형을 얻기 위한 하중값을 가진 성능지수를 제시함으로써 근사화와 예측성능의 향상을 꾀한다. 제안된 비선형 모델의 성능평가를 통해 그 우수성을 보인다.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.54-69
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• 2012

Chan and Qi (SCM 8/3 (2003) 209) developed an innovative measurement method that aggregates performance measures in a supply chain into an overall performance index. The method is useful and makes a significant contribution to supply chain management. Nevertheless, it can be cumbersome in computation due to its highly complex algorithmic fuzzy model. In aggregating the performance information, weights used by Chan and Qi-which aim to address the imprecision of human judgments-are incompatible with weights in additive models. Furthermore, the default assumption of linearity of its scoring procedure could lead to an inaccurate assessment of the overall performance. This paper addresses these limitations by developing an alternative measurement that takes care of the above. This research integrates three different approaches to multiple criteria decision analysis (MCDA)-the multiattribute value theory (MAVT), the swing weighting method and the eigenvector procedure-to develop a comprehensive assessment of supply chain performance. One case study is presented to demonstrate the measurement of the proposed method. The performance model used in the case study relies on the Supply Chain Operations Reference (SCOR) model level 1. With this measurement method, supply chain managers can easily benchmark the performance of the whole system, and then analyze the effectiveness and efficiency of the supply chain.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1970-1979
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• 1999

This paper proposes an AVI system which recognizes the license plate and the driver's face simultaneously using passive AVI techniques. For this, firstly, the pro-processing algorithm independent of the environment is proposed and region extraction of the car number plate and the driver's face is described. Secondly, characters are separated and recognition parameters are extracted from target regions. Thirdly, template matching of car number plate is performed and the fuzzy relation matrix of driver face is made for the final recognition processes. The merits of the proposed system are following : Pre-processing is accomplished regardless of the environment. The application areas of conventional AVI system can be expanded in the content that the driver's face is also recognized in the proposed system compared with only the number plast is recognized in the existing systems.

• Smart Structures and Systems
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• v.18 no.3
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• pp.525-540
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• 2016

The CFRP-confined circular concrete-filled steel tubular column is composed of concrete, steel, and CFRP. Its failure mechanics are complex. The most important difficulties are lack of an available method to establish a relationship between a specific damage mechanism and its acoustic emission (AE) characteristic parameter. In this study, AE technique was used to monitor the evolution of damage in CFRP-confined circular concrete-filled steel tubular columns. A fuzzy c-means method was developed to determine the relationship between the AE signal and failure mechanisms. Cluster analysis results indicate that the main AE sources include five types: matrix cracking, debonding, fiber fracture, steel buckling, and concrete crushing. This technology can not only totally separate five types of damage sources, but also make it easier to judge the damage evolution process. Furthermore, typical damage waveforms were analyzed through wavelet analysis based on the cluster results, and the damage modes were determined according to the frequency distribution of AE signals.