Identification of fuzzy systems by means of space search evolutionary algorithm (SSEA) and Information granulation

Wei Huang,오성권(Sung-Kwun Oh) 대한전기학회 2009 정보 및 제어 심포지엄 논문집 Vol.2009 No.10

In this paper, we introduce a hybrid optimization of fuzzy inference systems based on space search evolutionary algorithm (SSEA) and information granulation (IG). SSEA is exploited here to carry out the parameter estimation of the fuzzy models as well as to realize structure optimization which is described as a function optimization problem with inequality constraints by using a new chromosome for structure identification. Compared with the chromosome commonly used in fuzzy modeling, the new chromosome records the same information with a simpler structure. The whole hybrid optimization mechanisms Structural optimization and parametric optimization. The structural optimization is developed by SSEA and HCM while the parametric optimization is realized via SSEA and a standard least square method. As two representative numerical examples, gas furnace and Mackey-Glass time series are considered to evaluate the performance of the proposed model. Experimental results show that the proposed model leads to superior performance in comparison with some other fuzzy models reported in the literature.

개선된 공간 탐색 알고리즘을 이용한 정보입자 기반 퍼지모델 설계

Wei Huang,오성권(Sung-Kwun Oh) 한국지능시스템학회 2011 한국지능시스템학회 학술발표 논문집 Vol.21 No.2

Optimization analysis of factors affecting hydrocarbon gas drive based on orthogonal experimental design

Wei Huang,Shenglai Yang,Zhilin Wang,Xiaowei Lv,Hao Lei,Li Chen 한국자원공학회 2014 Geosystem engineering Vol.17 No.6

To improve the ultimate recovery of reservoirs and to increase the economic benefit of oilfields, we feel obliged to study on various factors affecting the development effect of hydrocarbon gas injection and its influence in terms of ultimate recovery. Thus, we can formulate a reasonable and feasible scheme on effective implementation of hydrocarbon gas drive. Take Q28 fault-block as an example; five main factors affecting the development effect of hydrocarbon gas injection have been screened out, four different levels of each influence factor has been set up; the orthogonal experiment method has been adopted to design 16 different combination schemes of 5 factors. Numerical reservoir simulation predicted the final recovery under the different combination schemes. Following are the range analysis and variance analysis of entire results so that the major and minor factors affecting the final recovery and the best range of each factor can be confirmed. The order sequence from major to minor factors: gas injection slug size, gas injection time, gas–water ratio, pressure level, and injection rate. This method is significant on its guidance for hydrocarbon gas injection in this area.

Identification of Fuzzy Inference Systems Using a Multi-objective Space Search Algorithm and Information Granulation

Wei Huang,Sung-Kwun Oh,Lixin Ding,Hyun-Ki Kim,Su-Chong Joo 대한전기학회 2011 Journal of Electrical Engineering & Technology Vol.6 No.6

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

Optimized Polynomial Neural Network Classifier Designed with the Aid of Space Search Simultaneous Tuning Strategy and Data Preprocessing Techniques

Wei Huang,Sung-Kwun Oh 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.2

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

Study on Thermodynamics of Three Kinds of Benzindocarbocyanine Dyes in Aqueous Methanol Solution

Wei Huang,Lan-Ying Wang,Yi-Le Fu,Ji-Quan Liu,You-Ni Tao,Fang-Li Fan,Gao-Hong Zhai,Zhen-Yi Wen 대한화학회 2009 Bulletin of the Korean Chemical Society Vol.30 No.3

Aggregation behavior of three kinds of benzindocarbocyanine dyes in aqueous methanol solution was studied by UV-Vis absorption spectrum. The results indicated that the three dyes all existed monomer-dimer equilibrium in aqueous methanol solution (concentration range 10−5 to 10−6 M) at 25.0~41.0 °C for Dye 1, 28.0~49.0 oC for Dye 2 and 26.0~47.0 °C for Dye 3. The fundamental property of the three dyes as the dimeric association constant KD, the dimeric free energy ΔGD, the dimeric entropy ΔSD, and the dimeric enthalpy ΔHD were determined. The ΔHD of three dyes: Dye 1, Dye 2 and Dye 3 was -42.5, -15.1 and -18.9 kJ/mol, respectively. The experimental observations were the subject of a theoretical study including the ground-state geometries which were fully optimized using DFT at B3LYP/6-31G level. The effect of dye molecule structure on ΔHD was discussed by theoretical calculations.

A half subcarrier guard band spectrum assignment scheme for multi-user FBMC systems

( Wei Huang ),( Hongbo Xu ),( Zhongnian Li ) 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.1

Traditionally, in multi-user multi-carrier systems, the neighboring subband will be gapped by one subcarrier, which is set as guard band to reduce multiple access interference (MAI) between neighboring subbands. The empty subcarrier for guard band will degrade the spectral efficiency of the whole system. In order to enhance the spectral efficiency of multi-user filter bank multiple carrier (FBMC) systems, a new subband allocation method is introduced, in which the neighboring subband is gapped by half subcarrier instead of one subcarrier. Meanwhile, in order to implement the proposed resource allocation scheme, an optimized FBMC prototype filter is designed to decrease the inter-subband interference to the neighboring subband. The detailed simulations about the comparison between the proposed spectrum assignment and traditional FBMC are given, as well as the performance in the different interference scenarios. The simulation results show that the combination of the proposed spectrum assignment scheme and the optimized filter has better performance compared to the traditional scheme. The proposed scheme can be used in the system which serves massive users to get higher spectrum efficiency.

Analysis and Optimization of Wireless Power Transfer Efficiency Considering the Tilt Angle of a Coil

Wei Huang,Hyunchul Ku 한국전자파학회JEES 2018 Journal of Electromagnetic Engineering and Science Vol.18 No.1

Wireless power transfer (WPT) based on magnetic resonant coupling is a promising technology in many industrial applications. Efficiency of the WPT system usually depends on the tilt angle of the transmitter or the receiver coil. This work analyzes the effect of the tilt angle on the efficiency of the WPT system with horizontal misalignment. The mutual inductance between two coils located at arbitrary positions with tilt angles is calculated using a numerical analysis based on the Neumann formula. The efficiency of the WPT system with a tilted coil is extracted using an equivalent circuit model with extracted mutual inductance. By analyzing the results, we propose an optimal tilt angle to maximize the efficiency of the WPT system. The best angle to maximize the efficiency depends on the radii of the two coils and their relative position. The calculated efficiencies versus the tilt angle for various WPT cases, which change the radius of RX (r2 = 0.075 m, 0.1 m, 0.15 m) and the horizontal distance (y = 0 m, 0.05 m, 0.1 m), are compared with the experimental results. The analytically extracted efficiencies and the extracted optimal tilt angles agree well with those of the experimental results.

Multiobjective Space Search Optimization and Information Granulation in the Design of Fuzzy Radial Basis Function Neural Networks

Wei Huang,Sung-Kwun Oh,Honghao Zhang 대한전기학회 2012 Journal of Electrical Engineering & Technology Vol.7 No.4

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

Applying AR Technology Integrating Unity3D with the Vuforia SDK for Oral English Teaching

Wei Huang,Haiyan Zhang 대한전자공학회 2023 IEIE Transactions on Smart Processing & Computing Vol.12 No.6

With the rapid progress of information technology, its application to teaching has gradually become a hot topic in the education field. Augmented reality (AR) combines virtual and real characteristics that can improve comprehension in a virtual environment, bringing new development opportunities to oral English teaching. Based on integration of the Vuforia SDK in the U-nity3D augmented reality engine, this research applies AR technology to spoken English teaching, improves a convolutional neural network (CNN), and proposes an English speech recognition system based on a connectionist temporal classification (CTC)-CNN (maxout). The results from experiments varying the number of iterations and the loss value, the proposed model converges after 80 iterations with strong performance. In recognition of spoken English with or without noise, the accuracy of this method was highest at 0.957 and 0.894, respectively, which is better than the CTC-CNN (sigmoid) model. In recognizing six kinds of spoken English, the accuracy of the CTC-CNN (maxout) model stabilizes at about 95%, with the highest accuracy at 97%. The accuracy rate shows that the method can be effectively applied to oral English teaching, and can provide a new reference method for innovations in oral English teaching and the improvement of teaching efficiency.