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.     

Novel error concealment method with adaptive prediction to the abrupt and gradual scene changes

In this paper, the impact of scene change on the conventional error concealment method is addressed and a novel error concealment method is proposed to improve the insufficiency of the conventional temporal error concealment algorithm due to the occurrence of scene change. Combining with the low complexity scene change detection algorithm using macroblock type information, the corrupt blocks resulting from bit errors are concealed either temporally or spatially depending on whether or not an abrupt scene change is found. In the case of gradual scene change, a novel error concealment method of interpolation and extrapolation is proposed to utilize the linear property of gradual scene change sequence, and effectively reduce the concealment error in comparison with the conventional algorithm. A great improvement of about 3 to 5 dB PSNR on average and 6 to 8 dB in some cases is obtained with very little memory and computation overhead.     

Designing learning objects that afford learners the experience of important variations in Chinese characters

Effective teaching should focus the attention of learners to its essential aspects. It follows that instructional software can be designed in such a way that allows learners to experience the important variations in the critical aspects of the content to be learned. This paper reports on the experience of designing such special kinds of instructional learning objects for the learning of Chinese characters. The design of these learning objects takes into consideration not only what Chinese characters are all about but also how learners commonly make errors while they learn to write the characters. Out of the analysis of these learners' errors, variations in the structural features of Chinese characters were pulled out and embodied in the design of the learning objects. Learners tinkering with the learning objects can thus implicitly develop a sense of the structural features or regularity of Chinese characters, which most importantly should prepare the learners to learn more new characters in the future. The main proposal of this paper is the notion of this variation‐affording instructional software that allows learners to attend to the essential aspects of what is to be learned. Furthermore, the idea of the learning object also differs from other instructional software in its small, self‐contained and reusable nature, such that teachers can flexibly embed the learning objects into their own teaching materials.