基于遗传算法的射影重构

在实现分层重构的过程中，射影重构是关键的第1步。目前，大多已有算法对模拟数值是非常有效的，但对于真实图象效果并不理想。为了寻求更为鲁棒的算法，提出了一种基于遗传算法的射影重构算法。该算法对于射影深度采用十进制编码，并以测量矩阵的秩为4作为约束，来定义适应度函数，然后利用遗传算法，并结合奇异值分解（SVD）技术来迭代估计射影深度，进而实现射影重构，该算法是行之有效的，且鲁棒性较好。     

面向高可靠片上网络通信的可重构路由算法

为了满足系统芯片对通信带宽的要求,片上网络正逐渐取代总线成为当前多核及众核系统的主流互连方案,然而由于芯片特征尺寸的不断减小,芯片内发生故障的概率显著增加.为了提供可靠的片上通信,提出一种低成本的可重构路由算法.该算法基于无共享边界的矩形故障模型,按照故障区与网络边界的相对位置对故障区进行分类;针对不同类型的故障区定义了具体的路由器状态更新策略;重构后的片上网络可以容忍任意数目、任意分布的路由器以及链路故障.与当前容错设计方案不同,文中算法不需要增加虚拟通道来保证网络的无死锁特性,因此具有低成本、高可靠的特性.仿真实验结果表明,文中算法适用于处理器与缓存,或缓存与缓存之间的片上通信.     

压缩视频超分辨率重构的一种新算法

针对传统重构算法不能很好地解决压缩视频的超分辨率图像重构的问题，文中提出了一种基于凸集投影（POCS）理论的压缩视频超分辨率重构算法。整个算法是在严格数学推理基础上形成的，具有较强的逻辑性和严密性。实验结果也表明，该算法不仅在峰值信噪比（PSNR）值和重构效果对压缩视频有较大提高和明显改善，而且易于扩展，具有广泛的应用范围。     

基于移不变字典学习和稀疏编码的滚动轴承故障识别算法

针对现有旋转机械故障识别算法过度依赖专家先验知识的问题,提出了一种基于移不变字典学习和稀疏编码（SIDL-SC）的自适应故障识别算法。首先,将不同故障状态下的振动信号进行分段和平滑预处理以降低数据处理复杂度,接着将加入自适应惩罚因子的移不变字典学习算法用于提取不同故障状态下的移不变基函数;然后,利用高效的特征符号搜索算法求解待识别信号在不同基函数下的稀疏系数以实现对待识别信号的重构;最后,以重构残差作为对该信号故障状态识别的判断依据。滚动轴承振动数据库和实测航空发动机振动信号的实验结果表明,该算法相较于现有算法具有更高的故障识别准确率,在实际中具有较强的可行性。     

卡尔曼滤波器在传感器软故障诊断中的应用

传感器发生软故障的特征是变化缓慢、不易检测。为克服传感器检测这一缺点设计了一种软故障诊断算法。该算法是在多重故障假设基础上,使用卡尔曼滤波器产生残差,采用加权二乘方（Weighted Sum of Squared Residuals,WSSR）方法对滤波残差进行处理,从而方便快速地检测出传感器的软故障,并实现隔离和重构。仿真实验表明,算法能很好地实现传感器软故障的检测、隔离和重构,不会发生误报和漏报现象。     

基于人工免疫算法的配电网络重构

提出了一种人工免疫算法，有效地提高了算法的收敛速度，避免了不成熟收敛，将该算法应用于配电网络重构中．取得了较好效果。另外本文提出用支路的开关状态（0或1）作为控制参数，避开了繁琐的编码方法，缩短了编码长度，所有这些都有效地提高了计算效率。     

面向片上网络路由器FIFO故障的实时容错设计

为满足对片上网络路由器FIFO故障的实时容忍需求,设计了一种可实时检测路由器FIFO故障并对故障容忍的方法。首先建立了路由器FIFO的功能模型及故障模型,在此基础上利用测试地址在线生成法,提出了一种针对于片上网络路由器FIFO的故障实时检测算法,并提出利用FIFO重定向机制容忍FIFO故障。实验结果表明,对测试电路参数进行合理地设置,可将测试电路对路由器性能影响降到很小范围,且在故障条件下,吞吐率和延时得到较好的改善。     

基于概化闭包压缩规则的关联分类器构建算法研究

在分析关联规则挖掘领域中概化闭包（GC）项集压缩方法基础上，为克服训练数据集中的噪声干扰，改进雎分类器，设计了一种基于概化闭包压缩规则的关联分类器（ACGCCR）构建算法模型。ACGCCR改进了GC的容忍限度值设置方法，根据类分布状态自适应设置容忍限度值；并规定一种新的概化闭包类规则裁剪方法，避免概化过程中出现学习能力不足的问题。ACGCCR分类规则在压缩存储表现、预测准确度、算法鲁棒性等方面性能表现良好。     

一种新的求解0-1背包问题的混合算法

该文汲取了蚁群算法（ACA）和抗体免疫克隆算法（AICA）的优点,提出了一种求解0-1背包问题的混合型算法,该算法充分利用了前者的搜索能力和后者的种群多样性。仿真实验对算法的部分参数进行了分析,并与其他文献的算法进行比较,结果表明,该算法是一种具有较高性能的混合优化算法。     

一种网格环境下的动态故障检测算法

针对现有网格系统出错几率较大、已有故障检测算法不能有效满足网格系统需求问题，提出了一种网格环境下的动态故障检测算法．根据网格系统的特点，基于不可靠故障检测思想，建立了网格系统模型和故障检测模型；结合心跳（heartbeat）策略和灰色预测方法，设计了一种动态心跳机制，并给出了预测模型和实时预测策略；提出了基于该动态心跳机制的网格故障检测算法，分析了算法的可靠性．仿真实验结果表明，该算法是正确、有效的，可用于网格环境下的故障检测．     

Consensus with dual failure modes

The problem of achieving consensus in a distributed system is discussed. Systems are treated in which either or both of two types of faults may occur: dormant (essentially omission and timing faults) and arbitrary (exhibiting arbitrary behavior, commonly referred to as Byzantine). Previous results showed that are number of dormant faults may be tolerated when there are no arbitrary faults and that, at most, [n-1/3] arbitrary faults may be tolerated when there are no dormant faults (n is the number of processors). A continuum is established between the previous results: an algorithm exists iff n >f_max+2m_max and c >f_max+m_max (where c is the system connectivity), when faults are constrained so that there are at most f_max and at most m_max of these that are arbitrary. An algorithm is given and compared to known algorithms. A method is given to establish virtual links so that the communications graph appears completely connected     

Learning-Based Switched Reliable Control of Cyber-Physical Systems With Intermittent Communication Faults

This study deals with reliable control problems in data-driven cyber-physical systems(CPSs) with intermittent communication faults, where the faults may be caused by bad or broken communication devices and/or cyber attackers. To solve them, a watermark-based anomaly detector is proposed, where the faults are divided to be either detectable or undetectable.Secondly, the fault's intermittent characteristic is described by the average dwell-time(ADT)-like concept, and then the reliable control issues, under the undetectable faults to the detector, are converted into stabilization issues of switched systems. Furthermore,based on the identifier-critic-structure learning algorithm, a datadriven switched controller with a prescribed-performance-based switching law is proposed, and by the ADT approach, a tolerated fault set is given. Additionally, it is shown that the presented switching laws can improve the system performance degradation in asynchronous intervals, where the degradation is caused by the fault-maker-triggered switching rule, which is unknown for CPS operators. Finally, an illustrative example validates the proposed method.     

Fault-free Hamiltonian cycles in crossed cubes with conditional link faults

The crossed cube, which is a variation of the hypercube, possesses some properties superior to the hypercube. In this paper, assuming that each node is incident with at least two fault-free links, we show that an n-dimensional crossed cube contains a fault-free Hamiltonian cycle, even if there are up to 2n − 5 link faults. The result is optimal with respect to the number of link faults tolerated. We also verify that the assumption is practically meaningful by evaluating its occurrence probability, which is very close to 1.     

边故障[k]元[n]立方体的超级哈密顿交织性

[k]元[n]立方体（记为[Qkn]）是优于超立方体的可进行高效信息传输的互连网络之一。[Qkn]是一个二部图当且仅当[k]为偶数。令[G[V0,V1]]是一个二部图,若（1）任意一对分别在不同部的顶点之间存在一条哈密顿路,且（2）对于任意一点[v∈Vi],其中[i∈{0,1}],[V1-i]中任意一对顶点可以被[G[V0,V1]-v]中的一条哈密顿路相连,则图[G[V0,V1]]被称为是超级哈密顿交织的。因为网络中的元件发生故障是不可避免的,所以研究网络的容错性就尤为重要。针对含有边故障的[Qkn],其中[k4]是偶数且[n2],证明了当其故障边数至多为[2n-3]时,该故障[Qkn]是超级哈密顿交织图,且故障边数目的上界[2n-3]是最优的。     

Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection

In this paper, the extension of the Bayesian framework for sensor fault detection of nonlinear systems proposed in [25] is studied utilizing particle filtering and the expectation maximization (EM) algorithm, in which the fault probability is calculated. The proposed algorithm is implemented on a wind turbine benchmark model to detect drivetrain sensor faults, which are one of the most addressed and likely faults in offshore wind turbines. The fault probability estimation effectively eliminates the need for installing identical redundant sensors. Indeed, because of the use of the unknown wind speed estimator, the residual signal, constructed based on the drivetrain estimated states, is not able to clearly signify the fault periods, a situation in which the fault probability accurately does this task. Also, using the proposed algorithm, the fault size for each sensor is estimated via a one‐step calculation, which decreases the complexity of this algorithm. The fault identification is performed using the recursive least square method and two other modifications, including exponentially weighted and windowed estimates. Additionally, in the fault accommodation step, the concept of a virtual sensor is used to remove the need for reconfiguring the current controller, which reduces complexity and expense. In the simulation section, using a real measured wind speed for two different fault scenarios, the proposed algorithm is evaluated and finally, conclusions are stated.     

An efficient method of determining functional equivalence classes of a network with reconvergent fan-out

This paper is a study of the effects of the faults on tho functional operation of a combinational logic circuit. The conditions whereby two different faults can produce tho sancio functional output arc investigated. In this approach two fault graphs of the circuits arc drawn. By manipulating these fault graphs the faults which are functionally equivalent can be obtained. An algorithm for determining the functionally equivalent classes of faults in a combinational circuit is presented. The unique feature of the algorithm is that it produces tho true functional equivalence (not structural equivalence) even for the circuit with reconvergent fan-out with unequal parity.     

Simulation of Complete Binary Tree Structures in a Faulty Flexible Hypercube

The Flexible Hypercube is a generalization of binary hypercube networks in that the number of nodes can be arbitrary in contrast to a strict power of 2. Restated, the Flexible Hypercube retains the connectivity and diameter properties of the corresponding hypercube. Although the embedding of complete binary trees in faulty hypercubes has received considerable attention, to our knowledge, no paper has demonstrated how to embed a complete binary tree in a faulty Flexible Hypercube. Therefore, this investigation presents a novel algorithm to facilitate the embedding job when the Flexible Hypercube contains faulty nodes. Of particular concern are the network structures of the Flexible Hypercube that balance the load before as well as after faults start to degrade the performance of the Flexible Hypercube. Furthermore, to obtain the replaceable node of the faulty node, 2-expansion is permitted such that up to (n – 2) faults can be tolerated with congestion 1, dilation 4 and load 1. That is, (n – 1) is the dimension of a Flexible Hypercube. Results presented herein demonstrate that embedding methods are optimized.     

Model-based real-time thermal fault diagnosis of Lithium-ion batteries

Ensuring safety and reliability is a critical objective of advanced Battery Management Systems (BMSs) for Li-ion batteries. In order to achieve this objective, advanced BMS must implement diagnostic algorithms that are capable of diagnosing several battery faults. One set of such critical faults in Li-ion batteries are thermal faults which can be potentially catastrophic. In this paper, a diagnostic algorithm is presented that diagnoses thermal faults in Lithium-ion batteries. The algorithm is based on a two-state thermal model describing the dynamics of the surface and the core temperature of a battery cell. The residual signals for fault detection are generated by nonlinear observers with measured surface temperature and a reconstructed core temperature feedback. Furthermore, an adaptive threshold generator is designed to suppress the effect of modelling uncertainties. The residuals are then compared with these adaptive thresholds to evaluate the occurrence of faults. Simulation and experimental studies are presented to illustrate the effectiveness of the proposed scheme.     

Fault-Tolerant Rate-Monotonic Scheduling

Due to the critical nature of the tasks in hard real-time systems, it is essential that faults be tolerated. In this paper, we present a scheme which can be used to tolerate faults during the execution of preemptive real-time tasks. We describe a recovery scheme which can be used to re-execute tasks in the event of single and multiple transient faults and discuss conditions that must be met by any such recovery scheme. We then extend the original Rate Monotonic Scheduling (RMS) scheme and the exact characterization of RMS to provide tolerance for single and multiple transient faults. We derive schedulability bounds for sets of real-time tasks given the desired level of fault tolerance for each task or subset of tasks. Finally, we analyze and compare those bounds with existing bounds for non-fault-tolerant and other variations of RMS.     

Advanced fault collapsing [logic circuits testing]

The partitioning of faults into equivalence classes so that only one representative fault per class must be explicitly considered in fault simulation and test generation, called fault collapsing, is addressed. Two types of equivalence, which are relevant to the work reported, are summarized. New theorems on fault equivalence and dominance, forming the basis of an algorithm that collapses all the structurally equivalent faults in a circuit, plus many of the functionally equivalent faults, are presented. Application of the algorithm to a set of benchmark circuits establishes that identification of functionally equivalent faults is feasible, and that, in some cases, they are a large fraction of the faults in a circuit. The collapsing algorithm applies not only to combinational designs but to synchronous sequential circuits as well