混流式转轮内部流场的涡动力学分析

本文通过理论分析,推导了水轮机叶片所受力矩的涡动力学表达式,阐述了用涡动力学分析从全局和局部诊断水轮机内部流动的原理。用数值方法对混流式水轮机的转轮内部流场进行了计算,采用了全三维全流道的湍流计算方法,基于标准k-ε湍流模型和SIMPLEC数值方法,从导叶进口到尾水管出口,包含所有流道在内的整体一次完成计算。根据得到的流场数据,对转轮内部流场进行了涡动力学分析,得到了叶片表面的BVF分布和表面摩擦力线的分布,进行了全局和局部流场诊断,通过和用压力、速度等传统分析方法的对比,结合试验数据,验证了涡动力学分析方法的实用性。     

复杂光流场运动分析与特征提取

针对车辆行车前方场景视频图像序列这-复杂光流场,提出一种基于光流分析的车辆行车环境特征实时提取方法:对同定在驾驶室的摄像机所拍摄的驾驶员视野前方行车视频图像序列进行区域分割,得到3个特征区域,采用五置信点光流算法对其中两个区域的光流场进行光流估计,并应用数理统计方法对其作置信空间和算术平均处理,提高了光流估计的鲁棒性,...     

多调整因子模糊控制器的进化优化

针对一类具有多调整因子的自调整模糊逻辑控制器，提出了基于进化计算的两种优化策略：传统的带线性量子的优化方法和具有评价机制的非线性量化因子优化法。其中，进化算法采用的是兼顾全局搜索和局部搜索的双种群自适应遗传算法，目标函数为反映全局控制性能的ITAE函数。跟踪实验有鲁棒性实验表明，该方法整体效果好，好稳态性能优，鲁棒性强。     

一种快速运动目标检测与跟踪算法

运动目标的检测与跟踪是计算机视觉和图像编码研究的主要内容,有着广泛的应用领域。基于光流场的检测与跟踪是其常用的方法之一,但其计算结果和效率是不能令人满意的。提出了基于帧间差阈值法和光流场相结合的快速运动目标检测与跟踪算法,计算结果表明该算法简单实用、运算速度快,克服了单纯光流场方法的不足。     

配网规划中加入扰动策略的蚁群算法

作为一种求解组合优化问题的启发式方法,基本蚁群算法以其正反馈、并行计算和鲁棒性强等优点被许多领域的专家所关注;但是如果正反馈应用不得当,也会出现搜索时间过长或陷入局部最优解的情况。为此,提出采用加入扰动策略的蚁群算法,通过对各计算因子的动态调整,控制其正反馈过程和算法的搜索区域,以提高蚁群算法得到全局最优解的概率和收敛速度。配网规划的应用算例证明了这一改进的有效性。     

配网规划中加入扰动策略的蚁群算法

作为一种求解组合优化问题的启发式方法,基本蚁群算法以其正反馈、并行计算和鲁棒性强等优点被许多领域的专家所关注;但是如果正反馈应用不得当,也会出现搜索时间过长或陷入局部最优解的情况.为此,提出采用加入扰动策略的蚁群算法,通过对各计算因子的动态调整,控制其正反馈过程和算法的搜索区域,以提高蚁群算法得到全局最优解的概率和收敛速度.配网规划的应用算例证明了这一改进的有效性.     

一种变形体图像轮廓光流场检测系统的研制

本文根据变形体的运动理解得到变形光流场的一种检测方法，即对序列图像二值化轮廓作聚类中心特征点匹配的算法，由这些基础理论我们得到了实际变形体光流场检测系统的设计，它包括了软件的构成和硬件的设置。文章最后还介绍了该系统被用于人体心脏左室轮廓光流场的实际检测结果。     

基于填充函数方法的最小静态负荷裕度计算

电力系统运行中约束条件的多样性和复杂性很容易造成优化问题非凸和多峰,传统的优化方法很容易落入局部最优解中.填充函数可以使最优解跳出局部最优,以更大的可能性得到全局最优值.文中将全局的填充函数方法结合原对偶内点法应用于电力系统最小静态负荷裕度计算.传统负荷增长裕度的计算需要预先给定负荷增长方向,文中提出了一种可以考虑不同负荷增长方向的计算模型,只要给定负荷增量的功率因数,就可以求得最小静态负荷裕度及相应的负荷增长方向.此模型非凸并且负荷裕度边界超平面是多峰的,填充函数的使用可以有效避免局部最优.算例计算说明了该模型和求解方法的有效性,具有很好的实用价值.     

心脏结构序列截面像光流场检测的研究

心脏各结构的运动和变形保证了人体的血液流动。本文叙述了一种变形体光流场的检测方法 ,即边缘聚类中心特征点的共同运动匹配算法 ,以此来达到不同帧运动序列图像特征点的跟踪 (匹配 ) ,进而得到轮廓特征点的光流场 ;文中还介绍这种轮廓光流场检测对心脏临床医学的实际应用     

考虑多种运行状态的台区拓扑辨识

由于供电可靠性的要求提高以及电力用户的增加,导致配电网的规模和复杂程度不断加大,增大了配电网拓扑的难度与时间。因此,为了加快拓扑的速度,提出一种改进矩阵算法,将拓扑分为全局拓扑、局部拓扑,并对两种拓扑分别进行改进。针对全局拓扑,对邻接矩阵使用节点消去法,对连通矩阵使用行扫描法,对关联矩阵使用两次节点消去法,形成局部拓扑关联矩阵;全局拓扑后,针对变位断路器,进行局部拓扑。首先,根据遥信变化,获知网络中哪些断路器发生变位;再根据局部拓扑关联矩阵,得到变位断路器与局部网络节点的连接情况;最后,根据这些信息对连通矩阵进行局部修改并进行矩阵的行扫描、母线分析和电气岛分析,就可以得到网络的局部拓扑,进而反映网络的全局拓扑。该方法的可靠性高,适用范围广,并且可以充分提高拓扑计算的效率。     

PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach

The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called probabilistic principal component analysis (PPCA) to impute the missing flow volume data based on historical data mining. First, we review the current missing data-imputation method and why it may fail to yield acceptable results in many traffic flow applications. Second, we examine the statistical properties of traffic flow volume time series. We show that the fluctuations of traffic flow are Gaussian type and that principal component analysis (PCA) can be used to retrieve the features of traffic flow. Third, we discuss how to use a robust PCA to filter out the abnormal traffic flow data that disturb the imputation process. Finally, we recall the theories of PPCA/Bayesian PCA-based imputation algorithms and compare their performance with some conventional methods, including the nearest/mean historical imputation methods and the local interpolation/regression methods. The experiments prove that the PPCA method provides significantly better performance than the conventional methods, reducing the root-mean-square imputation error by at least 25%.     

一种改进的光流估计方法

运动估计一直是视频处理领域的核心课题之一,基于Horn和Schunk所提出的光流约束方程的光流场估计类算法是图像运动估计的一类重要算法.本文针对光流法在亮度突变条件下,运动参数估计不准确的弱点,基于图像的边缘变化特征,对平滑约束项加以改进,给出了一种基于区域平滑约束的光流估计算法.该算法能够提高光流估计的精确性,降低Horn-Schunck法中全局平滑约束对运动边界的影响,提高峰值信噪比.实验结果表明,该方法更为有效和实用.     

结合全局和局部稀疏表示的SAR图像目标识别方法

提出结合全局和局部稀疏表示的合成孔径雷达(SAR)目标识别方法。基于全局字典的稀疏表示可以比较各个训练类别对于测试样本的相对表征能力。而基于局部字典的稀疏表示则体现各个类别对于测试样本的绝对描述能力。因此,两者的结果具有良好的互补性,可以为正确决策提供更充分的信息。采用D-S (Dempster-Shafer)证据理论对两者的决策矢量(即重构误差)进行决策融合从而得到更为稳健的识别结果。基于MSTAR数据集进行了目标识别实验并与其他SAR目标识别方法进行了充分对比,实验结果证明了提出方法的有效性。     

基于邻域降噪正交自编码器的工业过程故障检测

针对采用自编码器提取过程特征进行故障检测时,没有考虑数据的局部结构信息,提出邻域降噪正交自编码器（neighborhood denoising quadrature autoencoder, NDQAE)的方法。邻域保持嵌入算法提取数据的邻域信息作为权重对过程数据进行加权,强化数据局部结构信息。正交自编码器进一步提取带有局部信息加权的过程数据非线性特征。通过加入噪声增强自编码器的鲁棒性,并采用反向传播算法训练网络参数,获得能够捕捉数据局部特性和全局特性的鲁棒自编码器模型。在该模型的隐特征和重构残差空间分别构建T2和SPE统计量,并计算统计量控制限用于故障检测。在田纳西-伊斯曼（TE）化工过程和三相流过程进行仿真实验,结果表明了所提算法的有效性。     

Hierarchical fusion robust Kalman filter for clustering sensor network time‐varying systems with uncertain noise variances

For the clustering time‐varying sensor network systems with uncertain noise variances, according to the minimax robust estimation principle, based on the worst‐case conservative system with conservative upper bounds of noise variances, applying the optimal Kalman filtering, the two‐level hierarchical fusion time‐varying robust Kalman filter is presented, where the first‐level fusers consist of the local decentralized robust fusers for the clusters, and the second‐level fuser is a global decentralized robust fuser for the cluster heads. It can reduce the communication load and save energy resources of sensors. Its robustness is proved by the proposed Lyapunov equation method. The concept of robust accuracy is presented, and the robust accuracy relations of the local, decentralized, and centralized fused robust Kalman filters are proved. Specially, the corresponding steady‐state robust local and fused Kalman filters are also presented, and the convergence in a realization between the time‐varying and steady‐state robust Kalman filters is proved by the dynamic error system analysis method. A simulation example shows correctness and effectiveness. Copyright © 2013 John Wiley & Sons, Ltd.     

基于H∞鲁棒控制的质子交换膜燃料电池空气供应系统设计

自适应聚焦粒子群算法是根据粒子群优化(particle swarm optimization,PSO)算法的全局搜索与局部搜索平衡特性,改进得到的一种具有较好全局搜索能力和寻优速度的自适应群体智能优化算法。为实现对质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)空气供应系统的控制,建立空气供应系统机理模型,并采用多目标自适应聚焦粒子群(adaptive focusing particle swarm optimization,AFPSO)算法提出H∞鲁棒控制方法。仿真结果证明该H￥鲁棒控制方法能够实现对PEMFC空气供应系统的控制,在模拟电动车行驶过程时可使系统稳定运行,并与其它控制方法比较,证明该H￥鲁棒控制方法具有明显的优越性和有效性,对PEMFC实时控制系统的研究有重要的指导作用。     

Resolution of the unit commitment problems by using the hybrid Taguchi-ant colony system algorithm

This work presents a hybrid Taguchi-ant colony system (HTACS) algorithm to solve the unit commitment (UC) problem. The proposed algorithm integrates the Taguchi method and the conventional ant colony system (ACS) algorithm, providing a powerful global exploration capability. The Taguchi method is incorporated into the ACS process before its global pheromone update mechanism. Based on the systematic reasoning ability of the Taguchi method, improved UC solutions are selected quickly to represent potential UC schedules, subsequently, enhancing the ACS algorithm. Therefore, the proposed HTACS algorithm can be highly robust, statistically sound and quickly convergent. Additionally, feasibility of the proposed algorithm is demonstrated on a 10-unit system. Analysis results demonstrate that the proposed algorithm is feasible, robust, and more effective in solving the UC problem than conventional ACS methods.     

Weighted fusion robust steady-state estimators for multisensor networked systems with one-step random delay and inconsecutive packet dropouts

In this paper, the weighted fusion robust steady-state Kalman filtering problem is studied for a class of multisensor networked systems with mixed uncertainties. The uncertainties include same multiplicative noises in system parameter matrices, uncertain noise variances, as well as the one-step random delay and inconsecutive packet dropouts, which modeled by sequences of Bernoulli variables with different probabilities. By defining a new observation vector and applying the augmented method, the system under study is converted into one with only uncertain noise variances. The sufficient conditions for the existence of steady-state estimators are given. According to the minimax robust estimation principle, based on the worst-case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady-state Kalman estimators (predictor, filter, and smoother) are proposed. Applying the optimal fusion algorithm weighted by matrices and the covariance intersection fusion algorithm, the two kinds of robust fusion steady-state Kalman estimators are derived in a unified framework. The robustness of the proposed fusion estimators is proved by applying the permutation matrices and the global Lyapunov equations method, such that, for all admissible uncertainties, the actual steady-state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fusion steady-state Kalman estimators are proved. An example with application to autoregressive moving average signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.     

Robust 2DOF fuzzy gain scheduling control for DC servo speed controller

This paper proposes a new design method called “robust 2DOF fuzzy gain scheduling control” for a DC servo speed control system. The proposed technique utilizes the basic concept of 2DOF robust loop shaping, whose time‐domain specifications are combined during the controller design using a reference model. In addition, the local controllers are fixed‐ structure robust controllers whose structure can be specified as for a simple controller. A fuzzy approach is adopted in both system identification process and global control structure to accomplish an entirely robust system. Although the design of robust control in a fuzzy system is not easy, genetic algorithms (GAs) simplify the control design problem to design the fuzzy controller such that the average stability margin is minimized. Implementation of a DC servo speed control was adopted to investigate the effectiveness of the proposed controller. As seen from the results, the proposed controller has more robust performance and can be adopted in applications with a wide operating range. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于因果模型的高压给水系统故障诊断

基于因果模型的故障诊断方法将系统按层次分解，利用变量之间的关系描述子系统的因果性，在局部的层次上使用故障检测和分离技术生成残差和分析残差，而在全局的层次上按照系统的因果结构进行推理。它是一种结合定量计算和定性推理的动态故障诊断方法。但因果模型的推理机制建立在局部线性模型的基础上，而实际系统绝大多数是非线性系统，固有的缺陷限制了它的应用。文中对因果模型进行了改进，将变量之间的影响扩展到非线性关系，并且论证了只要满足连续可微的条件，因果模型的推理机制就仍然适用。最后对某电站300 MW机组的高压给水系统进行了事故工况仿真，仿真结果表明这是一种有效的、鲁棒的故障诊断方法。