A flexible encapsulated MEMS pressure sensor system for biomechanical applications

 The use of pressure sensors made of conductive polymers is common in biomechanical applications. Unfortunately, hysteresis, nonlinearity, non-repeatability and creep have a significant effect on the pressure readings when such conductive polymers are used. The objective of this paper is to explore the potential of a new flexible encapsulated micro electromechanical system (MEMS) pressure sensor system as an alternative for human interface pressure measurement. A prototype has been designed, fabricated, and characterized. Testing has shown that the proposed packaging approach shows very little degradation in the performance characteristics of the original MEMS pressure sensor. The much-needed characteristics of repeatability, linearity, low hysteresis, temperature independency are preserved. Thus the flexible encapsulated MEMS pressure sensor system is very promising and shows superiority over the commercially available conductive polymer film sensors for pressure measurement in biomechanical applications. Received: 1 December 1999/Accepted: 17 August 2000     

Regularized orthogonal linear discriminant analysis

In this paper the regularized orthogonal linear discriminant analysis (ROLDA) is studied. The major issue of the regularized linear discriminant analysis is to choose an appropriate regularization parameter. In existing regularized linear discriminant analysis methods, they all select the “best” regularization parameter from a given parameter candidate set by using cross-validation for classification. An obvious limitation of such regularized linear discriminant analysis methods is that it is not clear how to choose an appropriate candidate set. Therefore, up to now, there is no concrete mathematical theory available in selecting an appropriate regularization parameter in practical applications of the regularized linear discriminant analysis. The present work is to fill this gap. Here we derive the mathematical relationship between orthogonal linear discriminant analysis and the regularized orthogonal linear discriminant analysis first, and then by means of this relationship we find a mathematical criterion for selecting the regularization parameter in ROLDA and consequently we develop a new regularized orthogonal linear discriminant analysis method, in which no candidate set of regularization parameter is needed. The effectiveness of our proposed regularized orthogonal linear discriminant analysis is illustrated by some real-world data sets.     

Extending UML to model Web 2.0‐based context‐aware applications

Web 2.0 Mashups offer entirely new opportunities for context‐aware application (CAA) developers by integrating Web 2.0 technologies to facilitate interoperability among heterogeneous context‐aware systems. From a software engineering perspective, a visualized approach for Web 2.0‐based CAA modeling is crucial. Current CAA development, however, cannot provide a conceptual model for Web 2.0‐based CAA. Therefore, the development efficiency and potential for reuse are decreased. The UML is a general purpose modeling language with potential for use in many application domains. However, UML often lacks elements needed to model concepts in specific domains, such as Web 2.0‐based CAA modeling. To address the above issues, this study presents the Web 2.0‐based CAA UML profile, a UML profile for modeling Web 2.0‐based CAA. Copyright © 2011 John Wiley & Sons, Ltd.     

Polymeric carbon nanocomposites from multiwalled carbon nanotubes functionalized with segmented polyurethane

Multiwalled carbon nanotubes (MWNT) were functionalized with segmented polyurethanes (PU) by the “grafting to” approach. Raman and X‐ray photoelectron spectroscopy (XPS) spectra show that the sidewalls of MWNTs have been functionalized with acid treatment, and the amount of COOH increases with increasing acid treatment time. FTIR and X‐ray diffraction (XRD) spectra confirm that PU is covalently attached to the sidewalls of MWNTs by esterification reaction. Similar to the parent PU, the functionalized carbon nanotube samples are soluble in highly polar solvents, such as dimethyl sulfoxide (DMSO) and N,N‐dimethylformamide (DMF). The functionalized acid amount and the grafted PU amount were determined by thermogravimetric analyses (TGA). Comparative studies, based on SEM images between the PU‐functionalized and chemically defunctionalized MWNT samples, also reveal the covalent coating character. Dynamic mechanical analysis (DMA) of nanocomposite films prepared from PU and PU‐functionalized MWNTs show enhanced mechanical properties and increased soft segment T_g. Tensile properties indicate that PU‐functionalized MWNTs are effective reinforcing fillers for the polyurethane matrix. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Hybrid Subset Simulation method for reliability estimation of dynamical systems subject to stochastic excitation

A hybrid Subset Simulation approach is proposed for reliability estimation for general dynamical systems subject to stochastic excitation. This new stochastic simulation approach combines the advantages of the two previously proposed Subset Simulation methods, Subset Simulation with Markov Chain Monte Carlo (MCMC) algorithm and Subset Simulation with splitting. The new method employs the MCMC algorithm before reaching an intermediate failure level and splitting after reaching the level to exploit the causality of dynamical systems. The statistical properties of the failure probability estimators are derived. Two examples are presented to demonstrate the effectiveness of the new approach and to compare with the previous two Subset Simulation methods. The results show that the new method is robust to the choice of proposal distribution for the MCMC algorithm and to the intermediate failure events selected for Subset Simulation.     

Optimal power control system of a research nuclear reactor

The power control system is a key control system for a nuclear reactor, which directly concerns the safe operation of a nuclear reactor. Much attention is paid to the power control system performance of nuclear reactor in engineering. The designers put a high value upon design of an optimal power control system. In this paper, a design method is applied to the design of power control system. According to the optimal control theory, an objective function, quadratic performance index with weight factors is proposed. Then, the objective function is transformed into frequency domain form by use of Paserval's theorem. In frequency domain, an optimal transfer function can be obtained at the lowest value of objective function. The system with optimal transfer function has an optimal performance. The transfer function of the power control system is derived from a typical research nuclear reactor. Using the state feedback theory, the transfer function is synthesized to the optimal transfer function. The simulative results with the optimal controller and with a conventional controller show that the performance of the optimal power control system is largely improved on dynamic characters. The method applied here not only can be used for research nuclear reactor but also can be easily extended to pressurized water reactor power plant and other fields.     

模型库管理系统的设计和实现*

模型库管理系统是决策支持系统的核心，其基本功能包括模型的定义、组合、存储和调用.该文介绍一个支持客户/服务器模式的模型库管理系统的设计和实现，在这个系统中，模型被视为程序模块，通过运用面向对象的方法进行组织以及对模型的远程和动态的透明调用提供支持，使实现的系统具有良好的动态可扩充性.同时,通过提供模型定义语言和模型库管理，便于决策支持系统客户软件的开发.     

Delivery of video mail on the World Wide Web

This study was initiated to examine the feasibility of providing multimedia mail, in particular video mail, on the existing World Wide Web platform. In this instance, the Web browser will act as the user agent of the mailing system. With this, users will be able to compose, send and read video mail directly on the Web. This paper examines a number of possible system architectures that can be employed for the development of video mail. Based on the following key issues: portability of system, flexibility of system, Internet standard support, security and authentication, and message compression and storage, a final architecture was selected to build video mail. The approach taken is that the system will be interfaced to the Web using a standard HyperText Transfer Protocol (HTTP) server and a standard Web browser. This is achieved through the use of Common Gateway Interface (CGI) programs and forms which reside in a standard HTTP server to provide the functionality of video mail, and a separate mail server for the storage and management of messages. This will cause the HTTP server to behave as if it is an electronic mail (E-mail) system. Video mail has been successfully implemented at the School of Applied Science, Nanyang Technological University, Singapore. The development work has been carried out on a Sun SPARC5 workstation and the Typhoon relational database system was used to construct the mail server. All software codes were written in C or C++. A video capturing application used in conjunction with a SunVideo card has been developed for video and audio recording. The paper also addresses implementation issues pertaining to the use of CGI programs, mail server development, and video and audio management.     

Extended latent class models for collaborative recommendation

With the advent of the World Wide Web, providing just-in-time personalized product recommendations to customers now becomes possible. Collaborative recommender systems utilize correlation between customer preference ratings to identify "like-minded" customers and predict their product preference. One factor determining the success of the recommender systems is the prediction accuracy, which in many cases is limited by lacking adequate ratings (the sparsity problem). Recently, the use of latent class model (LCM) has been proposed to alleviate this problem. In this paper, we first study how the LCM can be extended to handle customers and products outside the training set. In addition, we propose the use of a pair of LCMs (called dual latent class model-DLCM), instead of a single LCM, to model customers' likes and dislikes separately for enhancing the prediction accuracy. Experimental results based on the EachMovie dataset show that DLCM outperforms both LCM and the conventional correlation-based method when the available ratings are sparse.