• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.293-300
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• 2014

This paper proposes a novel pattern recognition approach based on the radial basis function (RBF) neural network for identifying insulation defects of high-voltage electrical apparatus arising from partial discharge (PD). Pattern recognition of PD is used for identifying defects causing the PD, such as internal discharge, external discharge, corona, etc. This information is vital for estimating the harmfulness of the discharge in the insulation. Since an insulation defect, such as one resulting from PD, would have a corresponding particular pattern, pattern recognition of PD is significant means to discriminate insulation conditions of high-voltage electrical apparatus. To verify the proposed approach, experiments were conducted to demonstrate the field-test PD pattern recognition of cast resin current transformer (CRCT) models. These tests used artificial defects created in order to produce the common PD activities of CRCTs by using feature vectors of field-test PD patterns. The significant features are extracted by using nonlinear principal component analysis (NLPCA) method. The experimental data are found to be in close agreement with the recognized data. The test results show that the proposed approach is efficient and reliable.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.219-222
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• 1997

Due to the unpredictability and irregularity, the behaviors of chaotic systems are considered as undesirable phenomena to be avoided or controlled. Thus in this paper, to control systems showing chaotic behaviors, a direct adaptive control method using a radial basis function network (RBFN) as an excellent alternative of multi-layered feed-forward networks is presented. Compared with an indirect scheme, a direct one does not need the estimation of the controlled process and gives fast control effects. Through simulations on the two representative continuous-time chaotic systems, Duffing and Lorenz systems, validity of the proposed control scheme is shown.

• Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.215-225
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• 2016

In recent years, the automotive industry has known a remarkable development in order to satisfy the customer requirements. In this paper, we will study one of the components of the automotive which is the twist beam. The study is focused on the multicriteria design of the automotive twist beam undergoing linear elastic deformation (Hooke's law). Indeed, for the design of this automotive part, there are some criteria to be considered as the rigidity (stiffness) and the resistance to fatigue. Those two criteria are known to be conflicting, therefore, our aim is to identify the Pareto front of this problem. To do this, we used a Normal Boundary Intersection (NBI) algorithm coupling with a radial basis function (RBF) metamodel in order to reduce the high calculation time needed for solving the multicriteria design problem. Otherwise, we used the free form deformation (FFD) technique for the generation of the 3D shapes of the automotive part studied during the optimization process.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.241-242
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• 2008

본 논문에서는 fuzzy granular computing 방법 중의 하나인 context-based FCM을 이용하여 granular-based radial basis function neural network에 대한 기본적인 개면과 그들의 포괄적인 설계 구조에 대해서 자세히 기술한다. 제안된 모델에 기본이 되는 설계 도구는 context-based fuzzy c-means (C-FCM)로 알려진 fuzzy clustering에 초점이 맞춰져 있으며, 이는 주어진 데이터의 특징에 맞게 공간을 분할함으로써 효율적으로 모델을 구축할 수가 있다. 제안된 모델의 설계 공정은 1) Context fuzzy set에 대한 정의와 설계, 2) Context-based fuzzy clustering에 대한 모델의 적용과 이에 따른 모델 구축의 효율성, 3) 입력과 출력공간에서의 연결된 information granule에 대한 parameter(다항식의 계수들)에 대한 최적화와 같은 단계로 구성되어 있다. Information granule에 대한 parameter들은 성능지수를 최소화하기 위해 Least square method에 의해서 보정된다. 본 논문에서는 모델을 설계함에 있어서 체계적인 설계 알고리즘을 포괄적으로 설명하고 있으며 더 나아가 제안된 모델의 성능을 다른 표준적인 모델들과 대조함으로써 제안된 모델의 우수성을 나타내고자 한다.

• The KSFM Journal of Fluid Machinery
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• v.3 no.3 s.8
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• pp.19-24
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• 2000

It is well known that the finite element analysis often has an inaccuracy when it is in conflict with test results. Model updating is concerned with the correction of analytical model by processing records of response from test results. The famous one of the model updating methods is FRF sensitivity method. However, it has demerit that the solution is not unique. So, the neural network is recommended when an unique and exact solution is desired. The generalization ability of radial basis function neural network is used in model updating. As an application model, a cantilever and a rotor system are used. Specially the machined clearance($C_p$) of a journal bearing is updated.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1715-1723
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• 2018

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.473-481
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• 2008

This paper presents an approach to Back-propagation and Radial Basis Function neural network method with various training set for automatic vehicle detection from aerial images. The initial extraction of candidate object is based on Mean-shift algorithm with symmetric property of a vehicle structure. By fusing the density and the symmetry, the method can remove the ambiguous objects and reduce the cost of processing in the next stage. To extract features from the detected object, we describe the object as a log-polar shape histogram using edge strengths of object and represent the orientation and distance from its center. The spatial histogram is used for calculating the momentum of object and compensating the direction of object. BPNN and RBFNN are applied to verify the object as a vehicle using a variety of non-car training sets. The proposed algorithm shows the results which are according to the training data. By comparing the training sets, advantages and disadvantages of them have been discussed.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.547-553
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• 2008

We propose a two-stage document layout segmentation method. At the first stage, as top-down segmentation, morphological distance map algorithm extracts a collection of rectangular regions from a given input image. This preliminary result from the first stage is employed as input parameters for the process of next stage. At the second stage, a machine-learning algorithm is adopted RBF network, one of neural networks based on statistical model, is selected. In order for constructing the hidden layer of RBF network, a data clustering technique bared on the self-organizing property of Kohonen network is utilized. We present a result showing that the supervised neural network, trained by 300 number of sample data, improves the preliminary results of the first stage.

• Advances in environmental research
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• v.4 no.4
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• pp.219-231
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• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.8
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• pp.780-788
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• 2005

Two different types of algorithms were developed and applied to detect the partial faults of a multi-type air conditioning system. Partial faults include the compressor valve leakage, the refrigerant pipe partial blockage, the condenser fouling, and the evaporator fouling. The first algorithm was developed by using mathematical models and parity relations, and the second algorithm was developed by using mathematical models and a RBF neural network. Test results showed that the second algorithm was better than the first algorithm in detecting various partial faults of the system. Therefore, the algorithm developed by using mathematical models and a RBF neural network may be used for the detection of partial faults of an air-conditioning system.