Robust fault diagnosis and fault‐tolerant control for non‐Gaussian uncertain stochastic distribution control systems

The purpose of fault diagnosis of stochastic distribution control systems is to use the measured input and the system output probability density function to obtain the fault estimation information. A fault diagnosis and sliding mode fault‐tolerant control algorithms are proposed for non‐Gaussian uncertain stochastic distribution control systems with probability density function approximation error. The unknown input caused by model uncertainty can be considered as an exogenous disturbance, and the augmented observation error dynamic system is constructed using the thought of unknown input observer. Stability analysis is performed for the observation error dynamic system, and the H_∞ performance is guaranteed. Based on the information of fault estimation and the desired output probability density function, the sliding mode fault‐tolerant controller is designed to make the post‐fault output probability density function still track the desired distribution. This method avoids the difficulties of design of fault diagnosis observer caused by the uncertain input, and fault diagnosis and fault‐tolerant control are integrated. Two different illustrated examples are given to demonstrate the effectiveness of the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.     

Design of light weight exact discrete event system diagnosers using measurement limitation: case study of electronic fuel injection system

Employing a state-based Discrete Event System (DES) modelling framework, this paper proposes a new fault diagnosis approach called measurement limitation-based abstract DES diagnosis (MLAD), which attempts to reduce state space complexity of the diagnosis process while simultaneously preserving full diagnosability. The MLAD approach carefully applies a set of distinct measurement limitation operations on the state variables of the original DES model based on fault compartmentalisation to obtain separate behaviourally abstracted DES models and corresponding abstract diagnosers with far lower state spaces. The set of measurement limitation operations are so designed that although, any single abstract diagnoser may compromise diagnosability in seclusion, the additive combination of all diagnosers running in parallel always ensures complete diagnosability. Effective measurement limitation also ensures that the combined state space of the abstract diagnosers is much lower than that of the single full diagnoser that may be derived from the original DES model. As a case study, we have employed MLAD to incorporate failure diagnosability in a practical electronic fuel injection system. Evaluations on standard practical benchmarks show that MLAD achieves significant reduction in state space as compared to conventional monolithic full diagnosis approaches.     

A diagnosability analysis by means of fault distance

A new concept of fault distance is introduced in a failure-diagnosis model. Diagnosability measures such as t-fault diagnosability and t/s-diagnosability are expressed in terms of the fault distance. A new diagnosability measure of partial t-fault diagnosability characterized in terms of fault distance is proposed. In addition, the fault distance of various types of self-diagnosis models are expressed in graph theoretical terms. Applying the fault distance to various types of self-diagnosis models, conditions for diagnosabilities of these models, which have been studied separately, are obtained in a unified manner. Conditions for t-fault diagnosability and sufficient conditions for t/s-diagnosability are obtained with this fault distance.     

Conditional fault diagnosis of hierarchical hypercubes

The design of large dependable multiprocessor systems requires quick and precise mechanisms for detecting the faulty nodes. The system-level fault diagnosis is the process of identifying faulty processors in a system through testing. This paper shows that the largest connected component of the survival graph contains almost all remaining vertices in the hierarchical hypercube HHC _n when the number of faulty vertices is up to two or three times of the traditional connectivity. Based on this fault resiliency, we establish that the conditional diagnosability of HHC _n (n=2 ^m +m, m≥2) under the comparison model is 3m?2, which is about three times of the traditional diagnosability.     

一种基于K-Means局部最优性的高效聚类算法

K-Means聚类算法只能保证收敛到局部最优,从而导致聚类结果对初始代表点的选择非常敏感.许多研究工作都着力于降低这种敏感性.然而,K-Means的局部最优和结果敏感性却构成了K-MeanSCAN聚类算法的基础.K-MeanSCAN算法对数据集进行多次采样和K-Means预聚类以产生多组不同的聚类结果,来自不同聚类结果的子簇之间必然会存在交集.算法的核心思想是,利用这些交集构造出关于子簇的加权连通图,并根据连通性合并子簇.理论和实验证明,K-MeanScan算法可以在很大程度上提高聚类结果的质量和算法的效率.     

SAT‐based and CP‐based declarative approaches for Top‐Rank‐K closed frequent itemset mining

Top‐Rank‐K Frequent Itemset (or Pattern) Mining (FPM) is an important data mining task, where user decides on the number of top frequency ranks of patterns (itemsets) they want to mine from a transactional dataset. This problem does not require the minimum support threshold parameter that is typically used in FPM problems. Rather, the algorithms solving the Top‐Rank‐K FPM problem are fed with $K$, the number of frequency ranks of itemsets required, to compute the threshold internally. This paper presents two declarative approaches to tackle the Top‐Rank‐K Closed FPM problem. The first approach is Boolean Satisfiability‐based (SAT‐based) where we propose an effective encoding for the problem along with an efficient algorithm employing this encoding. The second approach is CP‐based, that is, utilizes Constraint Programming technique, where a simple CP model is exploited in an innovative manner to mine the Top‐Rank‐K Closed FPM itemsets from transactional datasets. Both approaches are evaluated experimentally against other declarative and imperative algorithms. The proposed SAT‐based approach significantly outperforms IM, another SAT‐based approach, and outperforms the proposed CP‐approach for sparse and moderate datasets, whereas the latter excels on dense datasets. An extensive study has been conducted to assess the proposed approaches in terms of their feasibility, performance factors, and practicality of use.     

Conditional diagnosability of hypercubes under the comparison diagnosis model

Processor fault diagnosis plays an important role in multiprocessor systems for reliable computing, and the diagnosability of many well-known networks has been explored. Lai et al. proposed a novel measure of diagnosability, called conditional diagnosability, by adding an additional condition that any faulty set cannot contain all the neighbors of any vertex in a system. We make a contribution to the evaluation of diagnosability for hypercube networks under the comparison model and prove that the conditional diagnosability of n-dimensional Hypercube Q_n is 3(n ? 2) + 1 for n ? 5. The conditional diagnosability of Q_n is about three times larger than the classical diagnosability of Q_n.     

Generation of test sequences from formal specifications: GSM 11‐11 standard case study

This paper presents the results of a case study on generating test cases for a fragment of the smart card GSM 11‐11 standard. The generation method is based on an original approach using the B notation and techniques of constraint logic programming with sets. The GSM 11‐11 technical specifications were formalized with the B notation. From this B specification, a system of constraints was derived, equivalent to this formal model. Using a set constraint solver, boundary states were computed and test cases were obtained by traversing the constrained reachability graph of the specifications. The purpose of this project was to evaluate the contribution of this testing environment, called B ‐TESTING ‐TOOLS , in an industrial process on a real life‐size application, by comparing the generated test sequences with the already used and high‐quality manually‐designed tests. This comparison enabled us to validate our approach and showed its effectiveness in the validation process of critical applications: the case study gives a wide coverage (about 85%) of the generated tests compared to the pre‐existing tests and a saving of 30% in test design time. Copyright © 2004 John Wiley & Sons, Ltd.     

控制系统可诊断性的内涵与研究综述

作为表征控制系统故障诊断能力的属性, 故障可诊断性揭示了故障诊断深层次的内涵.将可诊断性分析纳入控制系统与诊断方案的设计环节, 可以从根本上提高系统对故障的诊断能力, 为研究故障诊断提供新的思路.本文分别从可诊断性的内涵、研究现状以及潜在发展趋势三个角度系统地对可诊断性进行分析.首先, 从定义、影响因素、与已有概念的关系以及应用四个方面剖析了控制系统可诊断性的内涵和研究意义.其次, 分别从可诊断性评价与设计两个方面对可诊断性的研究现状进行分析.最后, 通过对可诊断性已有成果进行总结归纳, 探讨了可诊断性研究存在的不足以及未来发展的趋势.     

The conditional diagnosability of crossed cubes under the comparison model

The growing size of the multiprocessor systems increases their vulnerability to component failures. It is crucial to locate and replace the faulty processors to maintain the system's high reliability. The fault diagnosis is the process of identifying faulty processors in a system through testing. The conditional diagnosis requires that for each processor v in a system, all the processors that are directly connected to v do not fail simultaneously. In this paper, we show that the conditional diagnosability of the crossed cubes CQ _n under the comparison diagnosis model is 3n?5 when n≥7. Hence, the conditional diagnosability of CQ _n is three times larger than its classical diagnosability.     

Synthesis and design of tunable bandpass filters with constant absolute bandwidth using varactor‐loaded microstrip resonator

This article presents two new types of tunable filters with constant absolute bandwidth using varactor‐loaded microstrip resonators. First, the second‐ and third‐order Butterworth tunable filters are designed based on the parallel coupled‐line J inverters. Second, a fourth‐order Chebyshev tunable filter is designed based on the alternative J/K inverters, in this design, two adjacent resonators are coupled with each other through a short‐circuited transmission line as the K inverter. The proposed two topologies can be easily extended to high‐order tunable filter. Three tunable bandpass filters with J and alternative J/K inverters, respectively, are built with a tuning range from ～1.8 to ～2.3 GHz. The measured second‐order filter has a 3‐dB bandwidth of 160 ± 6 MHz and an insertion loss of 2.4–3.8 dB. The third‐order filter shows a 3‐dB bandwidth of 197 ± 5 MHz and an insertion loss of 3.8–4.8 dB. The fourth‐order filter shows a 3‐dB bandwidth of 440 ± 5 MHz and an insertion loss of 2.1–2.6 dB. For all the designed filters, the measured results are found in excellent agreement with the predicted and simulated results. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:681–689, 2014.     

Conditional diagnosability of balanced hypercubes under the MM∗ model

Fault diagnosis plays an important role in ensuring the reliability of a massive multiprocessor system. Diagnosability of a system is the maximum number of faulty nodes guaranteed to be identified during the diagnosis process, and thus is a critical metric to the reliability of the system. To have a greater number of identified faulty nodes, a new measure called conditional diagnosability for fault diagnosis was introduced, which has a normally used assumption. This paper addresses the conditional diagnosability of balanced hypercubes under the MM^? model, which is a realistic model to the fault diagnosis of a system. We show that the conditional diagnosability of the n-dimensional balanced hypercube is 4n?4 for n≥2.     

Dynamic software updates for parallel high‐performance applications

Despite using multiple concurrent processors, a typical high‐performance parallel application is long‐running, taking hours, even days to arrive at a solution. To modify a running high‐performance parallel application, the programmer has to stop the computation, change the code, redeploy, and enqueue the updated version to be scheduled to run, thus wasting not only the programmer's time, but also expensive computing resources. To address these inefficiencies, this article describes how dynamic software updates (DSU) can be used to modify a parallel application on the fly, thus saving the programmer's time and using expensive computing resources more productively. The net effect of updating parallel applications dynamically can reduce the total time that elapses between posing a problem and arriving at a solution, otherwise known as time‐to‐discovery. To explore the benefits of dynamic updates for high performance applications, this article takes a two‐pronged approach. First, we describe our experiences of building and evaluating a system for dynamically updating applications running on a parallel cluster. We then review a large body of literature describing the existing state of the art in DSU and point out how this research can be applied to high‐performance applications. Our experimental results indicate that DSU have the potential to become a powerful tool in reducing time‐to‐discovery for high‐performance parallel applications. Copyright © 2010 John Wiley & Sons, Ltd.     

IPOG/IPOG‐D: efficient test generation for multi‐way combinatorial testing

This paper presents two strategies for multi‐way testing (i.e. t‐way testing with t>2). The first strategy generalizes an existing strategy, called in‐parameter‐order, from pairwise testing to multi‐way testing. This strategy requires all multi‐way combinations to be explicitly enumerated. When the number of multi‐way combinations is large, however, explicit enumeration can be prohibitive in terms of both the space for storing these combinations and the time needed to enumerate them. To alleviate this problem, the second strategy combines the first strategy with a recursive construction procedure to reduce the number of multi‐way combinations that have to be enumerated. Both strategies are deterministic, i.e. they always produce the same test set for the same system configuration. This paper reports a multi‐way testing tool called FireEye, and provides an analytic and experimental evaluation of the two strategies. Copyright © 2007 John Wiley & Sons, Ltd.     

Diagnosability Analysis of Intermittent Faults in Discrete Event Systems Using a Twin-plant Structure

Most research in fault diagnosis of discrete event systems has been focused on permanent failures. However, experience with monitoring of dynamic systems shows that intermittent faults are predominant, and that their diagnosis constitutes one of the most challenging tasks for surveillance activities. Among the main existing approaches to deal with permanent faults, two were widely investigated while considering different settings: the Diagnoser based approach, and the Twin-plant based approach. The latter was developed to cope with some complexity limitations of the former. In the present paper, we propose a twin-plant based approach to deal with diagnosability of intermittent faults. Firstly, we discuss various notions of diagnosability, while considering the occurrence of faults, their recovery, and the identification of the system status. Then, we establish the necessary and sufficient conditions for each notion, and develop on-the-fly algorithms to check these properties. The discussed approach is implemented in a prototype tool that is used to conduct experiments on a railway control benchmark.

     

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.