Enhanced sample concentration on a three-dimensional origami paper-based analytical device with non-uniform assay channel

Traditional microfluidic paper-based analytical devices (μPADs) consist of a flat straight channel printed on a paper substrate. Such devices provide a promising low-cost solution for a variety of biomedical assays. However, they have a relatively high sample consumption due to their use of external reservoirs. Moreover, in μPADs based on the ion concentration polarization (ICP) effect, controlling the cross-sectional area of the Nafion membrane relative to that of the hydrophilic channel is difficult. Accordingly, the present study utilizes an origami technique to create a μPAD with a three-dimensional (3D) structure. The μPAD features short channels and embedded reservoirs, and therefore reduces both the driving voltage requirement and the sample consumption. Moreover, the preconcentration effect is enhanced through the use of an additional hydrophilic area adjacent to the Nafion membrane. The existence of electroosmotic flow (EOF) within the proposed device is confirmed using a current-monitoring method. In addition, the occurrence of ICP is evaluated by measuring the current–voltage response of the device at external voltages ranging from 0 to 50 V. The experimental results obtained for a fluorescein sample with an initial concentration of 10^?5 M show that a 100-fold enhancement factor can be achieved given the use of a non-uniform-geometry design for the assay channel and an additional hydrophilic region with an area equal to approximately 10% of the channel cross-sectional area. Finally, a 100-fold factor can also be achieved for a fluorescein isothiocyanate sample with an initial concentration of 10^?6 M given an external driving voltage of 40 V.     

Design of fault-tolerant large-scale VOD servers: With emphasis onhigh-performance and low-cost

Recent technological advances in digital signal processing, data compression techniques, and high-speed communication networks have made Video-on-Demand (VOD) servers feasible. A challenging task in such systems is servicing multiple clients simultaneously while satisfying real-time requirements of continuous delivery of objects at specified rates. To accomplish these tasks and realize economies of scale associated with servicing a large user population, a VOD server requires a large disk subsystem. Although a single disk is fairly reliable, a large disk farm can have an unacceptably high probability of disk failure. Furthermore, due to real-time constraints, the reliability requirements of VOD systems are even more stringent than those of traditional information systems. Traditional RAID solutions are inadequate due to poor resource usage. Thus, in this paper, we present alternative schemes which provide a high degree of reliability at low disk storage, bandwidth, and memory costs for on-demand multimedia servers. Moreover, we discuss some of the main issues and trade-offs associated with providing fault tolerance in multidisk VOD systems. We would like to impress upon the reader that one of the main points of this paper is the exposition of trade-offs and issues associated with designing fault-tolerant VOD servers. It is not the case that one fault tolerance scheme is absolutely better than another, but rather that one must understand the trade-offs as well as one's system constraints and then choose a fault tolerance scheme accordingly     

Robust Adaptive Backstepping Motion Control of Linear Ultrasonic Motors Using Fuzzy Neural Network

A robust adaptive fuzzy neural network (RAFNN) backstepping control system is proposed to control the position of an X-Y-Theta motion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. First, an X-Y-Theta motion control stage is introduced. Then, the single-axis dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which includes cross-coupled interference and friction force, is derived. Moreover, a conventional backstepping approach is proposed to compensate the uncertainties occurred in the motion control system. Furthermore, to improve the control performance in the tracking of the reference contours, an RAFNN backstepping control system is proposed to remove the chattering phenomena caused by the sign function in the backstepping control law. In the proposed RAFNN backstepping control system, a Sugeno-type adaptive fuzzy neural network (SAFNN) is employed to estimate the lumped uncertainty directly and a compensator is utilized to confront the reconstructed error of the SAFNN. In addition, the motions at the X axis, Y axis, and Theta axis are controlled separately. The experimental results show that the contour tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained, as well using the proposed RAFNN backstepping control system.     

A corner differentiation algorithm by a single sonar sensor for mobile robots

Ultrasonic sensors have been widely applied in mobile robots to obtain environmental information and avoid obstacles. In general, a typical domestic environment consists of planes, edges and corners. It is usually difficult to distinguish a plane from a corner directly with a single ultrasonic sensor. To overcome this difficulty, a corner differentiation algorithm for a single ultrasonic sensor is proposed in this paper. The algorithm is based on the features of all of the actual reflection points from the environment obtained by a reflection search algorithm from which the corners are realized by a corner differentiation algorithm. The experimental results show that the developed algorithm can successfully detect all planes and corners. Furthermore, an environmental map can be built based on the information obtained on planes and corners. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

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