Entropy‐Based Optimal Sensor Placement for Model Identification of Periodic Structures Endowed with Bolted Joints

The number of sensors and the corresponding locations are very important for the information content obtained from the measured data, which is a recognized challenging problem for large‐scale structural systems. This article pays special attention to the sensor placement issues on a large‐scale periodically articulated structure representing typical pipelines to extract the most information from measured data for the purpose of model identification. The minimal model parameter estimation uncertainties quantified by the information entropy (IE) measure is taken as the optimality criterion for sensors placement. By utilizing the inherent periodicity property of this type of structure together with the Bloch theorem, a novel tailor‐made modeling approach is proposed and the computational cost required for dynamic analysis to form the IE with respect to the entire periodic structure can be dramatically reduced regardless of the number of contained periodic units. In addition, to avoid the error of dynamic modeling induced by conventional finite element method based on static shape function, the spectral element method, a highly accurate dynamic modeling method, is employed for modeling the periodic unit. Moreover, a novel discrete optimization method is developed, which is very efficient in terms of the number of function evaluations. The proposed methodology is demonstrated by both numerical and laboratory experiments conducted for a bolt‐connected periodic beam model.     

Fame in the sciences: a culturomics approach

Scientometrics - Although scientists, like many other professionals, aspire to fame and recognition, research in the emergent field of fame and celebrity has as yet neglected to explore their fame...     

A probabilistic approach to stability and stabilization of networked control systems

In this paper, a probabilistic approach is proposed to study the modeling, stability, and stabilization problems for networked control systems (NCSs) with simultaneous random network communication delay and data dropout. Different from existing modeling approaches, the proposed NCSs model naturally captures the stochastic characteristics of the considered NCSs by taking into account the effects of the random network communication delay and data dropout. Based on the developed NCSs model, necessary and sufficient conditions of stability analysis, state, and output feedback stabilization problems are tackled. Moreover, necessary and sufficient stability and stabilization conditions are also presented for the case when the occurrence probabilities of delay and data dropout are partially known. Finally, three numerical examples are provided to demonstrate the effectiveness and the reduced conservatism of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.     

Metabolomic Profiling of Plasma from Melioidosis Patients Using UHPLC-QTOF MS Reveals Novel Biomarkers for Diagnosis

To identify potential biomarkers for improving diagnosis of melioidosis, we compared plasma metabolome profiles of melioidosis patients compared to patients with other bacteremia and controls without active infection, using ultra-high-performance liquid chromatography-electrospray ionization-quadruple time-of-flight mass spectrometry. Principal component analysis (PCA) showed that the metabolomic profiles of melioidosis patients are distinguishable from bacteremia patients and controls. Using multivariate and univariate analysis, 12 significant metabolites from four lipid classes, acylcarnitine (n = 6), lysophosphatidylethanolamine (LysoPE) (n = 3), sphingomyelins (SM) (n = 2) and phosphatidylcholine (PC) (n = 1), with significantly higher levels in melioidosis patients than bacteremia patients and controls, were identified. Ten of the 12 metabolites showed area-under-receiver operating characteristic curve (AUC) >0.80 when compared both between melioidosis and bacteremia patients, and between melioidosis patients and controls. SM(d18:2/16:0) possessed the largest AUC when compared, both between melioidosis and bacteremia patients (AUC 0.998, sensitivity 100% and specificity 91.7%), and between melioidosis patients and controls (AUC 1.000, sensitivity 96.7% and specificity 100%). Our results indicate that metabolome profiling might serve as a promising approach for diagnosis of melioidosis using patient plasma, with SM(d18:2/16:0) representing a potential biomarker. Since the 12 metabolites were related to various pathways for energy and lipid metabolism, further studies may reveal their possible role in the pathogenesis and host response in melioidosis.     

Evaluation of multiple features for violent scenes detection

Multimedia Tools and Applications - Violent scenes detection (VSD) is a challenging problem because of the heterogeneous content, large variations in video quality, and complex semantic meanings of...     

A new exact numerical series for the determination of the Biot number: Application for the inverse estimation of the surface heat transfer coefficient in food processing

As a corollary to a previous work, a new exact numerical series is deduced for the Biot number for the case of elementary geometries. This series is rapidly converging, allowing a very accurate approximation when truncated at its first term.On the basis of this first approximation to the series, the equation is generalized and proposed to indirectly estimate the Biot number for regular geometries.     

State‐space characterization of all solutions to a positive real control problem

Abstract

The purpose of this paper is to give a state‐space characterization of all internally stabilizing finite‐dimensional linear time‐invariant output feedback controllers for a given finite‐dimensional linear time‐invariant plant which ensure that the resulting closed‐loop transfer function is extended strictly positive real(ESPR). All such controllers are parameterized by a fixed linear fractional transformation with an ESPR, stable free parameter. The parameterized controllers have a state dimension not less than that of the open‐loop plant. The development uses only elementarily algebraic ideas beginning with a change of variables, an extended version of Kalman‐Yacubovich‐Popov positive real lemma, and Youla parameterization, thus the proofs given are simple and clear.