Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

Word length and lexical activation: Longer is better.

Many models of spoken word recognition posit the existence of lexical and sublexical representations, with excitatory and inhibitory mechanisms used to affect the activation levels of such representations. Bottom-up evidence provides excitatory input, and inhibition from phonetically similar representations leads to lexical competition. In such a system, long words should produce stronger lexical activation than short words, for 2 reasons: Long words provide more bottom-up evidence than short words, and short words are subject to greater inhibition due to the existence of more similar words. Four experiments provide evidence for this view. In addition, reaction-time-based partitioning of the data shows that long words generate greater activation that is available both earlier and for a longer time than is the case for short words. As a result, lexical influences on phoneme identification are extremely robust for long words but are quite fragile and condition-dependent for short words. Models of word recognition must consider words of all lengths to capture the true dynamics of lexical activation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

一种零知识证明协议的安全分析与改进

论文指出了文献[1]中的零知识证明协议在公钥参数选取上的安全漏洞,并从求解离散对数的角度进行了分析证明。然后吸取了DSA数字签名算法中安全密钥选取高效简单的优点,提出了一种比Schnorr身份识别方案快1／3的简单、安全、高效的零知识证明身份识别方案。     

Measurement and Identification of the Parameters of SPICE-Models of a Bipolar Transistor

A method of measuring and identifying the static parameters of a bipolar transistor is considered. The characteristic of the transistor, from which the parameters of the model are determined, is chosen depending on what group the calculated parameters belong to. The characteristics are measured in such a way that the equations of the model describing them can be reduced to the simplest form.     

一种新的线性分布参数系统辨识方法

本文应用块脉冲函数（ＢＰＦ）将含有未知参数的线性分布参数系统偏微分方程转换为矩阵方程，通过最小二乘法辨识线性分布参数系统的参数，辨识结果表明，ＢＰＦ辨识法具有原理简捷，实现方便，辨识精度高等优点。     

存在噪声和未建模动态系统传递函数的渐近无偏估计

存在未建模动态时，如何去除系统传递函数估计中噪声引起的偏差是系统辨识关心的重要问题。本文将我们已提出的ＢＥＬＳ方法进行推广得出：当系统未建模动态可用－ＦＩＲ模型描述时，应用本文的方法可得到系统传递函数的无偏辨识，而不需知道有关噪声和未建模动态概率分布函数的先验知识。     

系统备份及其误区分析

文章对系统备份的几个主要方面进行了讨论，并在此基础上，对系统备分的若干认识误区进行了分析。     

模糊数学识别火山岩岩性

针对火岩地层岩性识别成功率低的状况，应用模糊数学原理，建立模糊数学识别火山岩岩性模式，识别和划分火山岩地层岩性，以便进一步深入研究火山岩油气藏储层，选择8个反映火山岩岩性测井参数，确定阈值；利用所建立的火山岩岩性识别模式，对火山岩岩性进行分类；依据最大隶属原则，识别火山岩岩性，应用实地研究资料，识别10种火山岩岩性，并与岩心鉴定统计分析结果对比，一致性达85％。表明该方法识别火山岩岩性精度较高，在地质上具有很好的应用前景。