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Yang J.L.,Lee Ming Institute of Technology | Tzeng G.-H.,Kainan University | Tzeng G.-H.,National Chiao Tung University
Expert Systems with Applications | Year: 2011

Traditionally, most importance-assessing methods used to demonstrate the importance among criteria by preference weightings are based on the assumptions of additivity and independence. In fact, people have found that using such an additive model is not always feasible because of the dependence and feedback among the criteria to somewhat different degrees. To solve the issue the analytic network process (ANP) method is proposed by Saaty. The general method is easy and useful for solving the above-mentioned problem. However in ANP procedures, using average method (equal cluster-weighted) to obtain the weighted supermatrix seems to be irrational because there are different degrees of influence among the criteria. Therefore, we intended to propose an integrated multiple criteria decision making (MCDM) techniques which combined with the decision making trial and evaluation laboratory (DEMATEL) and a novel cluster-weighted with ANP method in this paper, in which the DEMATEL method is used to visualize the structure of complicated causal relationships between criteria of a system and obtain the influence level of these criteria. And, then adopt these influence level values as the base of normalization supermatrix for calculating ANP weights to obtain the relative importance. Additionally, an empirical study is illustrated to demonstrate that the proposed method is more suitable and reasonable. By the concept of ideal point, some important conclusions drawn from a practical application can be referred by practitioners. © 2010 Elsevier Ltd. All rights reserved.

Li I.-H.,Lee Ming Institute of Technology | Lee L.-W.,Lunghwa University of Science and Technology
Fuzzy Sets and Systems | Year: 2011

An observer-based adaptive controller developed from a hierarchical fuzzy-neural network (HFNN) is employed to solve the controller time-delay problem for a class of multi-input multi-output (MIMO) non-affine nonlinear systems under the constraint that only system outputs are available for measurement. By using the implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the HFNN adaptive controller are derived. According to the design of the HFNN hierarchical fuzzy-neural network, the observer-based adaptive controller can alleviate the online computation burden. Moreover, the common adaptive controller is utilized to control all the MIMO subsystems. Hence, the number of adjusted parameters of the HFNN can be further reduced. In this paper, we prove that the proposed observer-based adaptive controller can guarantee that all signals involved are bounded and that the outputs of the closed-loop system track asymptotically the desired output trajectories. © 2011 Elsevier B.V. All rights reserved.

Hsieh J.-W.,National Taiwan Ocean University | Chen L.-C.,Yuan Ze University | Chen L.-C.,Lee Ming Institute of Technology | Chen D.-Y.,Yuan Ze University
IEEE Transactions on Intelligent Transportation Systems | Year: 2014

Speeded-Up Robust Features (SURF) is a robust and useful feature detector for various vision-based applications but it is unable to detect symmetrical objects. This paper proposes a new symmetrical SURF descriptor to enrich the power of SURF to detect all possible symmetrical matching pairs through a mirroring transformation. A vehicle make and model recognition (MMR) application is then adopted to prove the practicability and feasibility of the method. To detect vehicles from the road, the proposed symmetrical descriptor is first applied to determine the region of interest of each vehicle from the road without using any motion features. This scheme provides two advantages: there is no need for background subtraction and it is extremely efficient for real-time applications. Two MMR challenges, namely multiplicity and ambiguity problems, are then addressed. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem results from vehicles from different companies often sharing similar shapes. To address these two problems, a grid division scheme is proposed to separate a vehicle into several grids; different weak classifiers that are trained on these grids are then integrated to build a strong ensemble classifier. The histogram of gradient and SURF descriptors are adopted to train the weak classifiers through a support vector machine learning algorithm. Because of the rich representation power of the grid-based method and the high accuracy of vehicle detection, the ensemble classifier can accurately recognize each vehicle. © 2014 IEEE.

Chen C.-L.,Lee Ming Institute of Technology | Cheng C.-H.,National Cheng Kung University
International Communications in Heat and Mass Transfer | Year: 2012

In the present paper a numerical study has been performed of the flow behavior and natural convection heat transfer characteristics of liquid fluids contained in an inclined arc-shaped enclosure. The governing equations are discretized using the finite-volume method and curvilinear coordinates. The Prandtl number (Pr) of the liquid fluids is assigned to be 4.0 and the Grashof number (Gr) is ranged within the regime 1×10 5≦≦4×10 6. On the other hand, the inclination angle (θ) of the enclosure is varied within 0°≦θ≦360° of major concern are the effects of the inclination and the buoyancy force on the flow and the thermal fields, and based on the numerical data of the thermal field the local and overall Nusselt numbers are calculated. Results show that the arc-shaped enclosure for Pr=4.0 at Gr=4×10 6 and θ=90° exhibits the best heat transfer performance. The poor heat transfer performance for Pr=4.0 fixed at Gr=1×10 5 and θ=180° exhibits the arc-shaped enclosure, respectively. As the value of Grashof number is elevated from 10 5 to 4×10 6, at θ=90°, the magnitude of Nu is elevated from 13.946 to 25.3 (81.4% increase); however, at θ=180°, the magnitude is elevated from 11.655 to 13.475 (15.6% increase) only. © 2011 Elsevier Ltd.

Li I.-H.,Lee Ming Institute of Technology | Lee L.-W.,Lunghwa University of Science and Technology
Applied Soft Computing Journal | Year: 2012

To develop a controller that deals with noise-corrupted training data and rule uncertainties for interconnected multi-input-multi-output (MIMO) non-affine nonlinear systems with unmeasured states, an interval type-2 fuzzy system is integrated with an observer-based hierarchical fuzzy neural controller (IT2HFNC) in this paper. Also, an H ∞ control technique and a strictly positive real Lyapunov (SPR-Lyapunov) design approach are employed for attenuating the influence of both external disturbances and fuzzy logic approximation error on the tracking of errors. Moreover, the proposed hierarchical fuzzy structure can greatly reduce the number of adjusted parameters of the IT2HFNC, and then, the problem of online computational burden can be solved. According to the design of the interval type-2 fuzzy neural network and H ∞ control technique, the IT2HFNN controller can improve its robustness to noise, uncertainties, approximation errors, and external disturbances. Simulation results are reported to show the performance of the proposed control system mode and algorithms. © 2012 Elsevier B.V.

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