A novel distributed approach to robust fault detection and identification

In this paper, we propose a novel distributed robust fault detection and identification (RFDI) scheme for a class of nonlinear systems. Firstly, a detection and identification estimator—robust fault tracking approximator (RFTA) is designed for online health monitoring. A novel feature of the RFTA is that it can simultaneously detect and accurately identify the shape and magnitude of the fault and disturbance. Moreover, it takes less online training time compared with the traditional neural network based fault diagnosis schemes. For some distributed systems, a network of distributed estimators is constructed where the RFTA is embedded into each estimator. Then we use consensus filter to filter the outputs of each estimator. One of the most important merits of the consensus filter is that its outputs can dramatically improve the accuracy of fault detection and identification. Next, the stability of the distributed RFDI scheme is rigorously investigated. Finally, two numerical examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

A priori ordering protocols to support consensus-building in multiple stakeholder contexts

In a complex social, political, economic, technological, and/or environmental context, corporate, military, government, and other organizations are often faced with collective decision-making situations. The rationale for group decision exercises is that the judgment of many will usually prove superior to the judgment of one. However, it has been shown that collective decision exercises are often highly dependent on matters of perspective, values and opinion, all of which - being essentially subjective in nature - are beyond the reach of existing formal decision technology. Furthermore, it can be expected that many collective decision exercises - particularly those of strategic import - will not lend themselves to the quantitative analysis instruments that have long dominated the management and decision science repertoire. This does not, however, mean that they must remain entirely and forever outside the bounds of scientific rationality. The ordering protocols which are currently available do not have enough technical mechanics to be relied upon to bring us to any satisfying resolution of a priori disputation. Hence, we propose an a priori ordering reference model that might support consensus-building in a multiple stakeholders context. In order to detect whether the subjective arguments are products of proper reasons or merely instances of raw rhetoric and to suggest how any logical or syntactical flaws might best be repaired, we propose logical ordering facilities and protocols for integrating procedural and instrumental provisions for a group-decision process with two lines of technical innovation: the superimpositional ordering function and the logical ordering support facilities.     

A method for the efficient prioritization of infrastructure renewal projects

The infrastructure renewal program at MIT consists of a large number of projects with an estimated budget that could approach $1 billion. Infrastructure renewal at the Massachusetts Institute of Technology (MIT) is the process of evaluating and investing in the maintenance of facility systems and basic structure to preserve existing campus buildings. The selection and prioritization of projects must be addressed with a systematic method for the optimal allocation of funds and other resources. This paper presents a case study of a prioritization method utilizing multi-attribute utility theory. This method was developed at MIT's Department of Nuclear Engineering and was deployed by the Department of Facilities after appropriate modifications were implemented to address the idiosyncrasies of infrastructure renewal projects and the competing criteria and constraints that influence the judgment of the decision-makers. Such criteria include minimization of risk, optimization of economic impact, and coordination with academic policies, programs, and operations of the Institute. A brief overview of the method is presented, as well as the results of its application to the prioritization of infrastructure renewal projects. Results of workshops held at MIT with the participation of stakeholders demonstrate the feasibility of the prioritization method and the usefulness of this approach.     

Nutritive quality of the alkaloid-poor Washington lupin (Lupinus polyphyllus lindl var SF/TA) as a potential protein crop

Abstract : An alkaloid-poor line of Washington lupin (Lupinus polyphyllus Lindl var SF/TA) was developed in an experiment started in 1982. The nutritive quality (alkaloid content, protein and amino acids, fat and fatty acids. macro- and micronutrients, fibre, sugars) yields, and seed quality of this line were studied. The results show that the total alkaloid content was low and varied in different seeds from 226 μg g^?1 to 366 μg g^?1 of dry matter. The main alkaloid was lupanine, but 16 other alkaloids (including sparteine and gramine) were also present. The var SF/TA cannot yet be used for human nutrition without processing although it would be a valuable protein crop. The results confirm that seeds which look different also vary in chemical composition.     

多尺度空间特征提取的脊柱图像拼接算法

针对脊柱图像视野有限,提出一种基于优化MSFE的脊柱图像自动拼接算法。设计出一种基于尺度因子变化的高斯卷积模板尺寸自适应调整以及双向配对办法;相似性度量采用城市距离;利用RANSAC算法去除错配,从而确定待拼接图像之间的变换参数,最后利用加权平均对图像融合。对实际取得的多幅脊柱图像拼接结果表明该算法具有很好的实时性和鲁棒性。     

A distributed solution to the network reconstruction problem

It has been recently shown in Ren et al. (2010) that by collecting noise-contaminated time series generated by a coupled-oscillator system at each node of a network, it is possible to robustly reconstruct its topology, i.e. determine the graph Laplacian. Restricting ourselves to linear consensus dynamics over undirected communication networks, in this paper we introduce a new dynamic average consensus least-squares algorithm to locally estimate these time series at each node, thus making the reconstruction process fully distributed and more easily applicable in the real world. We also propose a novel efficient method for separating the off-diagonal entries of the reconstructed Laplacian, and examine several concepts related to the trace of the dynamic correlation matrix of the coupled single integrators, which is a distinctive element of our network reconstruction method. The theory is illustrated with examples from computer, power and transportation systems.     

一类非线性随机时滞系统的故障诊断

针对一类非线性随机时滞系统提出了一种新的故障检测算法,该方法不同于传统的故障检测方法,是通过构建一种带有Consensus滤波器的故障诊断滤波器的方法来进行故障诊断.首先采用一组传感器测量系统实际输出,然后根据传感器的测量值构建一组残差生成器,将每个残差生成器看作一个小世界网络模型中的一个节点,采用动态Consensus算法计算出残差生成器的残差,并根据残差来判断系统是否有故障发生.仿真结果表明了本文所提出方法的可行性和有效性.     

A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network

This paper focuses on consensus reaching process (CRP) under social network in which the trust relationship expressed by linguistic information. A new feedback mechanism in social network group decision making (SN-GDM) is proposed, which mainly consists of the following two aspects: (1) The propagation of distributed linguistic trust is investigated to study trust relation among experts; (2) A maximum self-esteem degree based feedback mechanism is developed to produce personalized advice for reaching higher group consensus. To do so, a novel linguistic trust propagation method is proposed to obtain the complete trust relationship among group. The self-esteem degree is used to define the extent that an individual makes concessions. Then, a maximum self-esteem degree based optimal feedback mechanism is built to produce personalized advice to help inconsistent experts make change of their opinion. Its novelty lies in the establishment of an optimization model with the nonlinear group self-esteem degree function as the objective function while group consensus threshold as the restrictions. Therefore, the inconsistent experts will reach a group consensus with the minimum loss of self-esteem degree, and then, it achieves the optimal balance between individual self-esteem and group consensus. Finally, a ranking process is applied to derive the appropriate consensus solution.     

有适应力的分布式状态估计方法

为提高智能体系统对攻击的免疫力,研究了测量攻击下的适应力分布式状态估计方法。每个智能体对系统状态进行连续的本地线性测量。由于不同智能体的本地测量模型相互异构,对系统状态可能不具有本地可观测性,且攻击者能够操控部分智能体的测量数据,随意改变其测量结果。而智能体的目标是协同处理本地测量数据,并正确估计出未知的系统状态。因此,该问题的挑战在于在不对真实测量数据和恶意智能体的测量数据进行分辨时,如何设计算法估计得到真实的系统状态。为了解决这个问题,设计了适应性分布式最大后验概率估计算法。在该算法中,只要恶意智能体的数量小于某个特定值,所有智能体都能够收敛到系统状态。首先,根据卡尔曼滤波给出集中式最大后验概率(Maximum A Posteriori,MAP)估计方法,并与分布式一致性结合,进而得到分布式最大后验概率估计方法。然后,考虑到测量攻击,从估计一致性的角度,利用自适应饱和度增益设计了适应性分布式最大后验概率估计方法。最后,通过仿真实验验证算法的有效性。