On partitioned controllability of switched linear systems

When a switched linear system is not completely controllable, the controllability subspace is not enough to describe the controllability of the system over whole state space. In this case the state space can be divided into two or three control-invariant sub-manifolds, which form a control-related partition of the state space. This paper investigates when each component is a controllable sub-manifold. First, we consider when a sub-manifold is controllable for no control input case. Then the results are used to produce a necessary and sufficient condition assuring the controllability of the partitioned control-invariant sub-manifolds of a class of switched linear systems. An example is given to demonstrate the effectiveness of the results.     

A structured P2P network based on the small world phenomenon

In this paper, we propose a new structured P2P overlay network, named SW-Uinta(small-world). In order to reduce the routing latency, we firstly construct the Uinta network in which both physical characteristics of network and data semantic are considered. Furthermore, based on Uinta, a nondeterministic caching strategy is employed to allow for poly-logarithmic search time while having only a constant cache size. Compared with the deterministic caching strategy proposed by previous P2P systems, the nondeterministic caching strategy can reduce communication overhead for maintaining the routing cache table. Cache entries in the cache table of peer nodes can be updated by subsequent queries rather than only by running stabilization periodically. In the following, a novel cache replacement scheme, named the SW cache replacement scheme, is used to improve lookup performance, which has proved to satisfy the small-world principle. So we call this network SW-Uinta(small-world). After that, according to the theoretical analysis, it can be proved that SW-Uinta(small-world) can get O((log ² N)/k) search time with O(k) cache size. Lastly, the performance of SW-Uinta(small-world) is compared with those of other structured P2P networks such as Chord and Uinta. It shows that SW-Uinta(small-world) can achieve improved object lookup performance and reduce maintenance cost.

Adaptive binary tree for fast SVM multiclass classification

This paper presents an adaptive binary tree (ABT) to reduce the test computational complexity of multiclass support vector machine (SVM). It achieves a fast classification by: (1) reducing the number of binary SVMs for one classification by using separating planes of some binary SVMs to discriminate other binary problems; (2) selecting the binary SVMs with the fewest average number of support vectors (SVs). The average number of SVs is proposed to denote the computational complexity to exclude one class. Compared with five well-known methods, experiments on many benchmark data sets demonstrate our method can speed up the test phase while remain the high accuracy of SVMs.     

A MEMS affinity glucose sensor using a biocompatible glucose-responsive polymer

We present a MEMS affinity sensor that can potentially allow long-term continuous monitoring of glucose in subcutaneous tissue for diabetes management. The sensing principle is based on detection of viscosity changes due to affinity binding between glucose and poly(acrylamide-ran-3-acrylamidophenylboronic acid) (PAA-ran-PAAPBA), a biocompatible, glucose-specific polymer. The device uses a magnetically driven vibrating microcantilever as a sensing element, which is fabricated from Parylene and situated in a microchamber. A solution of PAA-ran-PAAPBA fills the microchamber, which is separated from the surroundings by a semi-permeable membrane. Glucose permeates through the membrane and binds reversibly to the phenylboronic acid moiety of the polymer. This results in a viscosity change of the sensing solution, which is obtained by measuring the damped cantilever vibration using an optical lever setup, allowing determination of the glucose concentration. Experimental results demonstrate that the device is capable of detecting glucose at physiologically relevant concentrations from 27 mg/dL to 324 mg/dL. The glucose response time constant of the sensor is approximately 3 min, which can be further improved with device design optimization. Excellent reversibility and stability are observed in sensor responses, as highly desired for long-term, stable continuous glucose monitoring.     

Design of micromirror actuator by ionic polymer metal composites

Microactuators for micromirror system have found many applications in various areas including projection displays, optical switches, RF switches and so on. In this paper we demonstrated micromirror actuator using ionic polymer metal composites (IPMC) that is a suitable candidate, since it has many attractive qualities such as durability, aquatic, miniature and light-weighted. Specially, IPMC has extraordinary advantages which are simple bending motion for low driving voltage (1–2 V), low power consumption, and simple structure. The IPMC actuator is made of Nafion NE-1110 (Dupont Co, Ltd., 260 µm thick) layer and electrode (platinum) layers and driven by 1–4 V. The displacement measured vertically is 0.25 mm and tilting angle is 11.3°. The angular motion, which is more than 10°, is a good advantage in the field of display module. This paper shows that the IPMC actuator has enough possibility for other applications.     

Analysis of the nonlinear dynamic stability for an electrically actuated viscoelastic microbeam

In this article, based on the standard linear solid model, the Euler-Bernoulli hypothesis and the Galerkin method, an analysis of the nonlinear dynamic stability for a clamped-guided viscoelastic microbeam under both a periodic axial force and a symmetric electrostatic load is presented. By using the incremental harmonic balanced method, the boundary of the principal region of instability is got. In the numerical calculation, the effect of the environmental and inner damping, geometric nonlinear, creep quantity and the symmetric electrostatic load on the principal region of instability is discussed.     

Structure identification of Bayesian classifiers based on GMDH

This paper introduces group method of data handing (GMDH) theory to Bayesian classification, and proposes GMBC algorithm for structure identification of Bayesian classifiers. The algorithm combines two structure identification ideas of search & scoring and dependence analysis, and is able to accomplish the process of adaptive structure identification. We experimentally test two versions of Bayesian classifiers (GMBC-BDe and GMBC-BIC) over 25 data sets. The results show that, the structure identification of the two Bayesian classifiers especially GMBC-BDe is very effective. And when the data sets contain lots of noise, the superiority of Bayesian classifiers learned by GMBC is more obvious. Finally, giving a classification domain without any prior information about the noise, we recommend adopting GMBC-BDe rather than GMBC-BIC.     

State estimation in high-mix semiconductor manufacturing

The traditional way of state estimation in semiconductor manufacturing, known as “threaded” state estimation, segregates the process data into different bins and uses the ones that match the current event of the specific context information (such as tools, layers, products) to update the process state. The limitation of threaded state estimation is that a narrowly defined process stream can result in too many different threads and insufficient data for each thread. This limitation becomes more severe in high-mix manufacturing, where there can be many products and many tools. Hence there is great interest in estimation methods that utilize all available data in the analysis. In this work, the characteristics inherent in state estimation of high-mix semiconductor manufacturing processes are analyzed, and a general framework is introduced for the non-threaded state estimation methods, i.e., state estimation without segregating the process data into different bins. The framework is based on the best linear unbiased estimate (BLUE) of a simplified stationary singular Gauss–Markov process, and non-threaded state estimation methods based on the Kalman filter, least squares and recursive least squares (RLS) are analyzed using the general framework. Simulation examples are presented to illustrate the equivalence between different algorithms. As real processes are rarely stationary, modifications to the Kalman filter and RLS are discussed. We show that in non-threaded state estimation, how to regulate the estimate covariance plays a significant role in estimation performance. To handle nonstationary disturbances that often occur in semiconductor processes, Bayesian-enhanced adaptive versions of the Kalman filter and RLS are proposed. Both simulated and industrial nonstationary processes are used to demonstrate the effectiveness of the proposed adaptive methods.