Home Energy Management System for Interconnecting and Sensing of Electric Appliances

( Wei-ting Cho ),( Chin-feng Lai ),( Yueh-min Huang ),( Wei-tsong Lee ),( Sing-wei Huang ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.7

Due to the variety of household electric devices and different power consumption habits of consumers at present, general home energy management (HEM) systems suffer from the lack of dynamic identification of various household appliances and a unidirectional information display. This study presented a set of intelligent interconnection network systems for electric appliances, which can measure the power consumption of household appliances through a current sensing device based on OSGi platform. The system establishes the characteristics and categories of related electric appliances, and searches the corresponding cluster data and eliminates noise for recognition functionality and error detection mechanism of electric appliances by applying the clustering algorithm. The system also integrates household appliance control network services so as to control them according to users` power consumption plans or through mobile devices, thus realizing a bidirectional monitoring service. When the system detects an abnormal operating state, it can automatically shut off electric appliances to avoid accidents. In practical tests, the system reached a recognition rate of 95%, and could successfully control general household appliances through the ZigBee network.

Morphology-controlled SWCNT/polymeric microsphere arrays by a wet chemical self-assembly technique and their application for sensors

Huang, Xing-Jiu,Li, Yue,Im, Hyung-Soon,Yarimaga, Oktay,Kim, Ju-Hyun,Jang, Doon-Yoon,Cho, Sung-Oh,Cai, Wei-Ping,Choi, Yang-Kyu IOP Pub 2006 Nanotechnology Vol.17 No.12

<P>Large-scale morphology-controlled SWCNT/polymeric microsphere arrays can be obtained by a wet chemical self-assembly technique. The loading of SWCNTs, the length of SWCNTs, and the size and nature of polymeric microspheres can easily be controlled. Similar results can also be reached using this method for MWCNTs. In both types of CNTs, they form an interesting interactive ‘net’ structure on spheres and sphere joints. The SWCNT/PS-modified Au electrode was used for detection of uric acid by cyclic voltammetry and single-potential time-based techniques. The preliminary results show that the modified electrode presents good sensitivity and stability to uric acid. </P>

New weapons to fight malaria transmission: A historical view

Huang Wei,Cha Sung‐Jae,Jacobs‐Lorena Marcelo 한국곤충학회 2022 Entomological Research Vol.52 No.5

The stagnation of our fight against malaria in recent years, mainly due to the development of mosquito insecticide resistance, argues for the urgent development of new weapons. The dramatic evolution of molecular tools in the last few decades led to a better understanding of parasite–mosquito interactions and coalesced in the development of novel tools namely, mosquito transgenesis and paratransgenesis. Here we provide a historical view of the development of these new tools and point to some remaining challenges for their implementation in the field.

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

Identification of Fuzzy Inference System Based on Information Granulation

( Wei Huang ),( Lixin Ding ),( Sung-kwun Oh ),( Chang-won Jeong ),( Su-chong Joo ) 한국인터넷정보학회 2010 KSII Transactions on Internet and Information Syst Vol.4 No.4

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with “conventional” evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization 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 polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some “conventional” fuzzy models already encountered in the literature.

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.

3D SIMULATION OF FLAPPING FLAGS IN A UNIFORM FLOW BY THE IMMERSED BOUNDARY METHOD

Wei-Xi Huang,Hyung Jin Sung(성형진) 한국전산유체공학회 2007 한국전산유체공학회 학술대회논문집 Vol.2007 No.-

We present an immersed boundary (IB) method for 3D simulation of flappingflags in a uniform flow. The proposed formulation is manipulated on the basis of an efficient Navier-Stokes solver adopting the fractional step method and a staggered Cartesian grid system. A direct numerical method is developed to calculate the flag motion, with the elastic force treated implicitly. The fluid motion defined on an Eulerian grid and the flag motion defined on a Lagrangian grid are independently solved and the mass of flag is handled in a natural way. An additional momentum forcing is formulated from the flag motion equation in a way similar with the direct-forcing IB formulation and acts as the interaction force between the flag and ambient fluid. A series of numerical tests are performed and the present results are compared qualitatively and quantitatively with previous studies. The instantaneous flag motion is analyzed under different conditions and surrounding vortical structures are identified. The effects of physical parameters on the flapping frequency are studied.

Improvement of mass source/sink for an immersed boundary method

Huang, Wei-Xi,Sung, Hyung Jin John Wiley Sons, Ltd. 2007 International journal for numerical methods in flu Vol.53 No.11

<P>An improved immersed boundary method using a mass source/sink as well as momentum forcing is developed for simulating flows over or inside complex geometries. The present method is based on the Navier–Stokes solver adopting the fractional step method and a staggered Cartesian grid system. A more accurate formulation of the mass source/sink is derived by considering mass conservation of the virtual cells in the fluid crossed by the immersed boundary. Two flow problems (the decaying vortex problem and uniform flow past a circular cylinder) are used to validate the proposed formulation. The results indicate that the accuracy near the immersed boundary is improved by introducing the accurate mass source/sink. Copyright © 2006 John Wiley & Sons, Ltd.</P>

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

Huang, Wei,Oh, Sung-Kwun,Ding, Lixin,Kim, Hyun-Ki,Joo, Su-Chong The Korean Institute of Electrical Engineers 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.