Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications

The battery sensors fault diagnosis is of great importance to guarantee the battery performance, safety and life as the operations of battery management system (BMS) mainly depend on the embedded current, voltage and temperature sensor measurements. This paper presents a systematic model-based fault diagnosis scheme to detect and isolate the current, voltage and temperature sensor fault. The proposed scheme relies on the sequential residual generation using structural analysis theory and statistical inference residual evaluation. Structural analysis handles the pre-analysis of sensor fault detectability and isolability possibilities without the accurate knowledge of battery parameters, which is useful in the early design stages of diagnostic system. It also helps to find the analytical redundancy part of the battery model, from which subsets of equations are extracted and selected to construct diagnostic tests. With the help of state observes and other advanced techniques, these tests are ensured to be efficient by taking care of the inaccurate initial State-of-Charge (SoC) and derivation of variables. The residuals generated from diagnostic tests are further evaluated by a statistical inference method to make a reliable diagnostic decision. Finally, the proposed diagnostic scheme is experimentally validated and some experimental results are presented.     

A Bayesian approach to model dispersal for decision support

In agricultural and environmental sciences dispersal models are often used for risk assessment to predict the risk associated with a given configuration and also to test scenarios that are likely to minimise those risks. Like any biological process, dispersal is subject to biological, climatic and environmental variability and its prediction relies on models and parameter values which can only approximate the real processes. In this paper, we present a Bayesian method to model dispersal using spatial configuration and climatic data (distances between emitters and receptors; main wind direction) while accounting for uncertainty, with an application to the prediction of adventitious presence rate of genetically modified maize (GM) in a non-GM field. This method includes the design of candidate models, their calibration, selection and evaluation on an independent dataset. A group of models was identified that is sufficiently robust to be used for prediction purpose. The group of models allows to include local information and it reflects reliably enough the observed variability in the data so that probabilistic model predictions can be performed and used to quantify risk under different scenarios or derive optimal sampling schemes.     

Topological evolution of laminar juncture flows under different critical parameters

Horseshoe vortex topological structure has been studied extensively in the past, traditional “saddle of separation” and new “attachment saddle point” topologies found in literature both have theoretical basis and experimental and computational evidences for support. The laminar incompressible juncture flows at low Reynolds numbers especially are observed to have new topology. Studies concerning the existence of the new topology though found in literature, the topological evolution and its dependency on various critical flow parameters require further investigation. A Particle Image Velocimetry based analysis is carried out to observe the effect of aspect ratio, δ*/D and shape of the obstacle on laminar horseshoe vortex topology for small obstacles. Rise in aspect ratio evolves the topology from the traditional to new for all the cases observed. The circular cross section obstacles are found more apt to having the new topology compared to square cross sections. It is noted that the sweeping effect of the fluid above the vortex system in which horseshoe vortex is immersed plays a critical role in this evolution. Topological evolution is observed not only in the most upstream singular point region of horseshoe vortex system but also in the corner region. The corner vortex topology evolves from the traditional type to new one before the topological evolution of the most upstream singular point, resulting in a new topological pattern of the laminar juncture flows “separation-attachment combination”. The study may help extend the understanding of the three-dimensional boundary layer separation phenomenon.     

Adiabatic photo-steering theory in topological insulators

Feasible external control of material properties is a crucial issue in condensed matter physics. A new approach to achieving this aim, named adiabatic photo-steering, is reviewed. The core principle of this scheme is that several material constants are effectively turned into externally tunable variables by irradiation of monochromatic laser light. Two-dimensional topological insulators are selected as the optimal systems that exhibit a prominent change in their properties following the application of this method. Two specific examples of photo-steered quantum phenomena, which reflect topological aspects of the electronic systems at hand, are presented. One is the integer quantum Hall effect described by the Haldane model, and the other is the quantum spin Hall effect described by the Kane–Mele model. The topological quantities associated with these phenomena are the conventional Chern number and spin Chern number, respectively. A recent interesting idea, time-reversal symmetry breaking via a temporary periodic external stimulation, is also discussed.     

An Adaptive M-Estimator and Its Application to a Selection Problem

A robust adaptive estimation procedure for location estimation problems is developed. Classification in this procedure is done on the basis of skewness and tailweight, using two statistics that are ratios of linear functions of sample order statistics. The associated estimators are of the general type known as M-estimators, Following the development of the adaptive location estimation procedure, an application to the k population selection problem is given. Monte Carlo results show the superiority of the adaptive procedure to the sample means procedure, the rank sum procedure, and the previously developed adaptive procedure of Randles, Ramberg, and Hogg.     

TOWARD AN ARRAY OF PHENOMENOLOGICAL IDEAL-TYPES FOR THE SOCIAL SCIENCES

Beginning with the isolation of four essentially unique mathematical intervals on a continuum, we show the process by which an array of four phenomenological ideal-types can be generated which carry rather specific analytical and administrative implications. The basic vehicle of the paper is deductive inference, employed to generate successively more specific taxonomies of system types, eventuating in the four felt to be a specific interest as real-world references for the social scientist, with empirical point included wherever they can defend the deductions.     

On application of input data partitioning to Bayesian weighted averaging of biomedical signals

Averaging in the time domain may be used for noise attenuation in case of biomedical signals with a quasi‐cyclical character. Traditional arithmetic averaging technique assumes the constancy of the noise power cycle‐wise, however, most types of noise are not stationary and the variability of noise power is observed. It constitutes a motivation for using methods of weighted averaging, in particular Bayesian weighted averaging. This paper presents the computational study of Bayesian weighted averaging with traditional (sharp) and fuzzy partition of the input data in the presence of non‐stationary noise. There is presented the known empirical Bayesian weighted averaging method (EBWA), with the parameter p describing the probabilistic model, and its modification NBWA which eliminates the parameter. Both methods can be extended by partitioning of the input data. The performance of presented methods is experimentally evaluated for an analytical signal as well as a real ECG signal and compared with traditional arithmetic averaging method. However, the methods can be applied to any signal with a quasi‐cyclical character. The aim of the paper is to show the influence of the type of partition as well as the number of parts on the quality of the averaged signal.     

Color constancy,color‐mixing ability,and color inference

In the laboratory, the performance of color constancy has always been unstable. The variables posed by the subjects themselves tend to be neglected. In this study, a simple color‐mixing test was used to sort subjects into three groups. They were also provided with hue and brightness in a fixed color order as guidance during experimental tasks in an attempt to observe any changes in cognitive skills among subjects of varying color‐mixing abilities under changed illuminations. Three hypotheses were proposed: (1) Matching hypothesis: people remember and apply the spatial locations of color surrounds to the target color to achieve color constancy; (2) Comparing hypothesis: people identify the relative difference between color surrounds to achieve color constancy; and (3) Reasoning hypothesis: people compare the color surrounds and refer to color knowledge accumulated in the past to achieve color constancy through reasoning. The experimental results were as follows if the Comparing hypothesis is taken into account, (a) when hue guidance is hidden the subjects' identification rate (IR) performance supported the Reasoning hypothesis; (b) when hue guidance was shown, the IR performance of subjects in all three groups supported the Matching hypothesis. Based on these results, this study offers two recommendations: (a) color surrounds with a fixed relative location should be avoided because spatial location memory leading to people using matching skills and (b) subjects should be screened based on their color‐mixing ability because significant differences in color constancy performance exist between people with varying levels of color‐mixing ability. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

Expectations and interpretations during causal learning.

In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A–1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

四桥臂有源电力滤波器三维空间矢量调制技术的研究

三相四桥臂拓扑结构的有源电力滤波器（APF）不但能抑制谐波电流,也能补偿三相不平衡。在此介绍了系统的拓扑结构;通过对四桥臂电压型变换器进行建模分析,得到了APF的控制策略;详细分析了APF的三维空间矢量脉宽调制法（3D-SVM）;最后搭建了一台实验样机。实验结果验证了控制策略和调制方法的正确性和有效性。