Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method

In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme.     

Evaluating the quality of airport service using the fuzzy multi‐criteria decision‐making method: a case study of Taiwanese airports

In today's highly competitive air transportation environment, the quality of airport service is an important issue. In this paper, we present a fuzzy multi‐criteria decision‐making (MCDM) method for the evaluation of the quality of airport service. The data are collected from the passengers who depart from two international airports in Taiwan. The fuzzy MCDM model is defuzzied using the inverse function arithmetic representation method. The results show that the average quality of the Taoyuan International Airport service is better than that of the Kaohsiung International Airport service. This paper also analyses the quality of airport service using the fuzzy expert system to improve the performance of airport service. The analysis results show that the Kaohsiung International Airport and the Taoyuan International Airports' improvement efforts should be concentrated on seven and five specific service items, respectively. The findings of this paper are to help the decision‐makers and the managers of airports improve the quality of airport service.     

Preliminary cost estimates for thin-film transistor liquid–crystal display inspection and repair equipment: A hybrid hierarchical approach

The thin-film transistor liquid–crystal display (TFT-LCD) industry has developed rapidly in recent years. Because TFT-LCD manufacturing is highly complex and requires different tools for different products, accurately estimating the cost of manufacturing TFT-LCD equipment is essential. Conventional cost estimation models include linear regression (LR), artificial neural networks (ANNs), and support vector regression (SVR). Nevertheless, in accordance with recent evidence that a hierarchical structure outperforms a flat structure, this study proposes a hierarchical classification and regression (HCR) approach for improving the accuracy of cost predictions for TFT-LCD inspection and repair equipment. Specifically, first-level analyses by HCR classify new unknown cases into specific classes. The cases are then inputted into the corresponding prediction models for the final output. In this study, experimental results based on a real world dataset containing data for TFT-LCD equipment development projects performed by a leading Taiwan provider show that three prediction models based on HCR approach are generally comparable or better than three conventional flat models (LR, ANN, and SVR) in terms of prediction accuracy. In particular, the 4-class and 5-class support vector machines in the first-level HCR combined with individual SVR obtain the lowest root mean square error (RMSE) and mean average percentage error (MAPE) rates, respectively.     

Recurrent Radial Basis Function Network-Based Fuzzy Neural Network Control for Permanent-Magnet Linear Synchronous Motor Servo Drive

We propose a recurrent radial basis function network-based (RBFN-based) fuzzy neural network (FNN) to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) to track periodic reference trajectories. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, it performs the structureand parameter-learning phases concurrently. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method, using a delta adaptation law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties     

Grain boundary effect on electron-beam-induced current in La3Ga5.5Ta0.5O14 crystal

La₃Ga_5.5Ta_0.5O₁₄ crystal recently attracted more attention due to its superior electromechanical properties and high Q × f product. We report that the first electron-beam-induced current experiment on La₃Ga_5.5Ta_0.5O₁₄ single crystal. This method is employed to study the effect of the crystal's grain boundary on the incident electron beam. The experimental results clearly show that when the electron beam scans over the grain boundary of the crystal, a fraction of the carriers recombine at the grain boundary and is unavailable for the current generation. This recombination rate will be enhanced when the electron beam was close to the boundary and cause a dip in the collected current. Although the crystal is an insulator, this effect still can be observed if the coating metal is proper to be chosen. It is also pointed out that the different diffusion lengths of the crystal might be due to the tilted grain boundary.     

A fault-tolerant object service in the OMG's object management architecture

The object management architecture (OMA) has been recognized as a de facto standard in the development of object services in a distributed computing environment. In a distributed system, the provision for failure-recovery is always a vital design issue. However, the fault-tolerant service has not been extensively considered in the current OMA framework, despite the fact that an increasing number of useful common services and common facilities have been adopted in OMA. In this paper, we propose a fault-tolerance developing environment, called Phoinix, which is compatible to the OMA framework. In Phoinix, object services can be developed with embedded fault-tolerance capability to tolerate both hardware and software failures. The fault-tolerance capability in Phoinix is classified into two levels: restart, and rollback-recovery; where the fault-tolerance capability enhances as the level increases. Currently, Phoinix is ported on Orbix 2.0 and on SunOS 4.2. In this paper, the design and implementation of Phoinix is presented and its performance is evaluated.     

Computing planar swept polygons under translation

A translational sweep is the translating of a polygon, called the generatrix G, around another polygon, called the directrix D, under two conditions: (1) G is always in contact with D; and (2) the interiors of G and D do not intersect. Three classes of translational sweep are studied, including the case in which both G and D are convex; the case in which G is convex, D monotone; and the case in which both are monotone. Efficient algorithms for computing the trajectory and the swept area as well as geometric and computational properties are presented for each class. A notion called the inverse generatrix, which reveals a duality between the trajectory and the swept polygon, is introduced to reduce complexity.