Adaptive Local Outlier Probability for Dynamic Process Monitoring

Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

基于Local-BGM法的冷涡暴雨集合预报试验及评估检验

基于传统增长模繁殖法(Breeding Growing Mode,BGM)和局地增长模繁殖法(Local Breeding Growing Mode,Local-BGM)生成初始扰动成员,对一次冷涡暴雨过程进行集合预报试验,从多方面比较两种方案的预报效果,并且在邻域概率法(Neighborhood Probability,NP)中引入时间邻域,评估概率预报结果。结果表明,引入局地化思想的Local-BGM方案能够生成比传统BGM方案更合理的初始扰动,具有很明显的局地特征。对于扰动变量的预报,Local-BGM方案在均方根误差和离散度等方面均表现更好,同时能够提高各量级降水的预报技巧。邻域集合概率法能够综合各个集合成员预报的降水信息得到优于集合平均的概率预报,分数技巧评分更高。并且在考虑时间不确定性后,无论是控制预报、集合平均还是邻域集合概率法,分数技巧评分均有很大改善,并且降水阈值越大改善效果越明显,能够为极端强降水天气提供较为客观的概率预报信息。

基于强跟踪的自适应PHD-SLAM算法

同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术使移动机器人在缺乏先验环境信息的条件下,能够在估计自身位姿的同时构建环境地图。然而,在海洋、矿洞等复杂环境中,移动机器人容易受到随机突变噪声的干扰,进而导致SLAM性能下降。现有的概率假设密度(Probability Hypothesis Density,PHD)SLAM算法未考虑随机突变噪声,受到干扰时在线自适应调整能力较弱。为解决移动机器人因随机突变噪声导致状态估计和建图精度降低的问题,本文结合强跟踪滤波器(Strong Tracking Filter,STF)与PHD滤波器,提出了一种基于强跟踪的自适应PHD-SLAM滤波算法(Strong Tracking Probability Hypothesis Density Simultaneous Localization and Mapping,STPHD-SLAM)。该算法以PHD-SLAM为框架,针对过程噪声协方差和量测噪声协方差随机突变问题,本文通过在特征预测协方差中引入STF中的渐消因子,实现了对特征预测的自适应修正和卡尔曼增益的动态调整,从而增强了算法的自适应能力。其中渐消因子根据量测新息递归更新,确保噪声突变时每个时刻的量测新息保持正交,从而充分利用量测信息,准确并且快速地跟踪突变噪声。针对渐消因子激增导致的滤波器发散问题,本文对渐消因子进行边界约束,提高算法的鲁棒性。仿真结果表明,在量测噪声协方差和过程噪声协方差随机突变的情况下,所提算法相较于PHD-SLAM 1.0和PHD-SLAM 2.0的定位和建图精度都得到了提高,同时保证了计算效率。

基于声散射统计特性的压缩感知水下目标定位与观测矩阵性能

近年来,压缩感知理论在水下目标定位领域得到了广泛的应用,压缩感知定位算法将水下目标定位问题转化为稀疏模型重构问题,具有较高的定位准确性,但定位结果受散射环境影响。该文开展水下目标定位的水池试验,对水听器阵列接收的试验数据进行处理,通过定位误差、可定位声源数量和定位信噪比分析定位结果,研究压缩感知定位算法的宽带性能、散射环境对定位结果的影响,并使用Spark系数和声散射统计特性近似分析了观测矩阵性能。水池试验结果表明,压缩感知定位算法在较宽的频率范围内有较好的适用性;随着环境散射增强,水声定位观测矩阵的列相干性减小,稀疏重构性能提升,使得定位结果更加准确;在近似分析水声定位观测矩阵性能时,声散射统计特性与Spark系数的变化趋势相似,并具有直观且计算简单的优点。

基于智能电表量测的配电网线路异常参数辨识及定位方法

为提高配网线路参数质量,提出一种基于智能电表量测的配网线路异常参数辨识及定位方法。该方法把传统辨识算法的非线性辨识方程求解问题转化成参数最优分布的推断问题,进而在参数辨识的基础上,利用概率统计法进行异常参数定位。首先,给定线路参数初始分布,利用马尔科夫链蒙特卡洛方法生成参数样本,并通过树状结构估计方法和损失函数进行参数分布更新,并以损失函数结果收敛时的参数分布期望作为线路参数辨识值;其次,计算线路参数相对偏移距离,通过概率统计方法判断辨识数据是坏数据或异常参数,并将坏数据直接剔除;最后,计算分析反映线路参数错误的异常因子,进行线路异常参数定位。通过实际29节点的10 kV馈线展示了参数辨识的流程,并通过实际97节点的10 kV馈线进行异常参数定位,证明了所提方法的可行性与有效性。

基于朴素贝叶斯的属性混合大数据分类提取

与一般数据类型不同,属性混合大数据在分类提取过程中易受边缘数据的干扰,存在分类提取误差较大和抗噪能力不佳的问题,于是提出基于朴素贝叶斯算法的属性混合大数据分类提取方法。采用离散小波变换法消除大数据中存在的噪声,避免分类提取过程受到噪声干扰。通过监督判别投影法对大数据实行降维处理,将预处理后的属性混合大数据输入到朴素贝叶斯分类器中,通过先验知识与后验概率的结合,完成属性混合大数据的分类提取。实验结果表明,所提方法的运算耗时短、分类提取误差小、抗噪声能力强,验证了所提方法的应用效果。

A Learning Particle Swarm Optimization Algorithm for Odor Source Localization

This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.

Adaptive local routing strategy on a scale-free network

Due to the heterogeneity of the structure on a scale-free network, making the betweennesses of all nodes become homogeneous by reassigning the weights of nodes or edges is very difficult. In order to take advantage of the important effect of high degree nodes on the shortest path communication and preferentially deliver packets by them to increase the probability to destination, an adaptive local routing strategy on a scale-free network is proposed, in which the node adjusts the forwarding probability with the dynamical traffic load （packet queue length） and the degree distribution of neighbouring nodes. The critical queue length of a node is set to be proportional to its degree, and the node with high degree has a larger critical queue length to store and forward more packets. When the queue length of a high degree node is shorter than its critical queue length, it has a higher probability to forward packets. After higher degree nodes are saturated （whose queue lengths are longer than their critical queue lengths）, more packets will be delivered by the lower degree nodes around them. The adaptive local routing strategy increases the probability of a packet finding its destination quickly, and improves the transmission capacity on the scale-free network by reducing routing hops. The simulation results show that the transmission capacity of the adaptive local routing strategy is larger than that of three previous local routing strategies.

Local Galerkin Method for the Approximate Solutions to General FPK Equations

In this paper, the method proposed recently by the author for the solution of probability density function (PDF) of nonlinear stochastic systems is presented in detail and extended for more general problems of stochastic differential equations (SDE), therefore the Fokker Planck Kolmogorov (FPK) equation is expressed in general form with no limitation on the degree of nonlinearity of the SDE, the type of δ correlated excitations, the existence of multiplicative excitations, and the dimension of SDE or FPK equation. Examples are given and numerical results are provided for comparing with known exact solution to show the effectiveness of the method.

Localization of s-Wave and Quantum Effective Potential of a Quasi-free Particle with Position-Dependent Mass

The properties of the 8-wave for a quasl-free partide with position-dependent mass （PDM） have been discussed in details. Differed from the system with constant mass in which the localization of the s-wave for the free quantum particle around the origin only occurs in two dimensions, the quasi-free particle with PDM can experience attractive forces in D dimensions except D = 1 when its mass function satisfies some conditions. The effective mass of a particle varying with its position can induce effective interaction, which may be attractive in some cases. The analytical expressions of the eigenfunctions and the corresponding probability densities for the 8-waves of the two- and three-dimensional systems with a special PDM are given, and the existences of localization around the origin for these systems are shown.

Relating thermal conductivity of soil skeleton with soil texture by the concept of“local thermal conductivity fluctuation”

The thermal conductivity of the soil skeletonλ;is an essential parameter from the point of view of the correct assessment of soil overall/effective conductivity.This work introduces the concept of“local thermal conductivity fluctuation”which characterizes the microscale variation of conductivity within the solid phase.It is proposed to link the“local fluctuation”of thermal conductivityλwith the soil texture-the information that is available at the scale of engineering applications.It was possible to relate the skeleton thermal conductivity with the grain size distribution of the soil.Finally,based on a large series of numerical simulations,the paper provides four triangle diagrams(at different organic matter contents:0%,2%,4%and 6%)relating the value ofλ;with volume fraction of individual soil separates.This result is extremely important from the practical point of view.One can quickly evaluateλ;value provided that information on the grain size distribution and organic matter content is available.

Heterogeneous sensors data fusion method based on peak picking in probability density space

The multi-sensor multi-target localization and data fusion problem is discussed, and a new data fusion method called joint probability density matrix （JPDM） has been proposed, which can associate with and fuse measurements from spatially distributed heterogeneous sensors to produce good estimates of the targets. Based on probabilistic grids representation, the uncertainty regions of all the measurements are numerically combined in a general framework. The NP-hard multi-sensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion methods, the JPDM method does not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.

Structural Reliability Assessment by a Modified Spectral Stochastic Meshless Local Petrov-Galerkin Method

This study presents a new tool for solving stochastic boundary-value problems. This tool is created by modify the previous spectral stochastic meshless local Petrov-Galerkin method using the MLPG5 scheme. This modified spectral stochastic meshless local Petrov-Galerkin method is selectively applied to predict the structural failure probability with the uncertainty in the spatial variability of mechanical properties. Except for the MLPG5 scheme, deriving the proposed spectral stochastic meshless local Petrov-Galerkin formulation adopts generalized polynomial chaos expansions of random mechanical properties. Predicting the structural failure probability is based on the first-order reliability method. Further comparing the spectral stochastic finite element-based and meshless local Petrov-Galerkin-based predicted structural failure probabilities indicates that the proposed spectral stochastic meshless local Petrov-Galerkin method predicts the more accurate structural failure probability than the spectral stochastic finite element method does. In addition, generating spectral stochastic meshless local Petrov-Galerkin results are considerably time-saving than generating Monte-Carlo simulation results does. In conclusion, the spectral stochastic meshless local Petrov-Galerkin method serves as a time-saving tool for solving stochastic boundary-value problems sufficiently accurately.

基于GMM-DBC的CSI室内定位算法

针对贝叶斯室内定位技术存在定位精度低及时间复杂度较高的问题,提出了一种基于高斯混合模型和密度聚类(GMM-DBC)的信道状态信息(CSI)定位算法.通过对分模型参数的初次估计构建GMM概率分布模型并进行误差计算;引入确定分模型个数(DSM)策略,结合误差计算结果更新GMM模型参数,减小由模型精度引起的定位误差;基于不同参考点的分布特征,判断参考点间紧密程度,将紧密相连的参考点划为一类,减小搜索范围,降低时间复杂度;根据分簇结果,利用改进的贝叶斯概率算法进行权值计算,得到最终定位结果.实验结果表明:所提算法能较好地提高定位精度,降低时间复杂度.

基于局部方差和后验概率分类的快速模板匹配算法

具有旋转不变性的模板匹配算法在工业制造上具有广泛的应用。为解决传统的模板匹配方法在目标旋转、匹配速度上的问题,提出一种基于局部方差和后验概率分类的模板匹配方法。为减少计算量,在匹配中通过局部方差过滤掉部分候选窗口,并在后验概率分类模块中通过对比不同区域稳定特征点对的灰度来计算窗口相关性。使用后验概率分类计算窗口相关度能在预处理过程实现旋转不变性,并保证准确率在95%以上。实验结果表明,该算法在80万像素级的任意角度匹配图像上选择合适的窗口移动步长后,可将匹配时间减少到10 ms以内,相较于现有算法速度更快。

充液管道超声导波传播特性及损伤定位的研究

管道运输是我们国家综合运输体系的重要组成部分,其在流动介质的运输方面具有特殊的优势。在这类管道中液体介质为超声导波能量的泄漏提供途径,不利于后续的损伤定位研究。因此研究超声导波在充液管道中的传播特性非常必要,从探究充液管道超声导波频散方程出发,建立有限元模型研究不同液体填充量的管道响应信号的能量变化。提出基于径向基函数的概率成像算法,其中权重函数仅与损伤的位置有关而与传感器路径的方向无关。对管道中非通透圆孔损伤进行定位,周向与轴向定位精度相比传统算法均有所提高。在此基础上提出基于L(0,2)模态的概率成像法,分别在两种尺寸的管道中进行损伤定位,结果表明定位精度得到大幅提升,该算法具有广阔的应用前景。

基于局部传播中心性的影响力最大化算法

针对度中心性等方法选择种子节点时未考虑节点间传播概率及邻居拓扑连接的影响,提出局部传播中心性LPC(Local Propagation Centrality)的概念。为减少贪心算法时间复杂度高且不可扩展的问题,提出一种新的启发式算法IMLPC(Influence Maximization Algorithm based on LPC)。该算法通过计算每个节点的LPC,依次选择影响力最大的节点。实验结果表明,IMLPC的影响范围和运行时间较现有启发式算法相比有显著提升。在不同数据集下,IMLPC影响范围稳定、可扩展性好。

山东省某滨海核电厂可能最大降雨分析计算

可能最大降雨(Probable Maximum Precipitation,PMP)是评价核电厂防洪安全和排水系统设计的重要参数。在分析山东省某滨海核电厂厂址区域暴雨特性和天气成因的基础上,采用当地暴雨放大方法(水汽放大法和水汽效率放大法)和暴雨移置放大法推求出厂址24 h PMP。通过计算方案的合理性检查,对比山东省可能最大24 h点降雨量等值线图和其他核电厂工程24 h PMP成果,经综合分析确定该厂址24 h PMP为1100.0 mm,研究成果可为全国类似流域的PMP研究提供参考和借鉴。

基于道路概率场的车队协同定位方法

标量场方法通常被用于无人机集群和潜艇集群的协同定位,但在车队场景中,应用磁异常场、水深场等类似的标量场存在困难。本研究提出了1种基于道路概率场和车辆运动模型的车队协同定位方法,该方法利用开源数据库获取电子地图,对地图进行缓冲区处理、栅格化处理和数学形态学处理,构建了道路概率场。同时,基于GNSS技术建立了车辆运动模型,利用车队内车辆之间的相对位置作为协同信息,把车辆预测位置在道路概率场中的取值作为权重计算标准,使用粒子滤波定位算法不断更新预测车辆行驶轨迹。新方法建立了道路概率场这一标量场,把车辆位置对应的道路概率值作为判定车辆位置的重要依据,将电子地图中的地理空间信息应用于车队协同定位。与传统的标量场方法不同,道路概率场无需新的专门测量,并且可以利用已有的海量电子地图资源进行生成,而车辆也无需新增传感器。此外,新方法根据不同场景设计了车辆运动模型,在车辆行驶过程中利用道路概率场不断优化轨迹,与传统车队定位方法相比更注重单个时刻点定位的差异。在真实场景和仿真场景中设置不同缓冲区宽度和车辆数进行对比测试,结果显示:根据定位误差作为定位效果的判定标准,与经典的利用车辆运动模型的扩展卡尔曼滤波方法相比,新方法在仿真场景和真实场景中将改进幅度分别提高了49.6%和49.8%;与空道路概率场的车队协同定位方法相比,在仿真场景和真实场景中新方法分别改进了59.5%和50.3%。本研究为车队协同定位提供了1种新的方法,通过构建道路概率场并利用车辆运动模型,相较于传统方法,该方法提高了定位的精度和可靠性,具有重要的应用前景。

基于粒子采样投影的雷达低旁瓣复合波形设计

非合作无人机机动性强、易被建筑物遮挡,往往具备信号截获能力,给传统雷达探测带来严峻挑战。对此,提出基于粒子采样投影的恒模波形设计方法来获得局部低旁瓣、低截获波形。首先,利用混沌相位编码的随机性构造复合波形,增强波形低截获性能;接着,引入低旁瓣波形设计的数学模型,并转化为多变量交替范数迭代优化问题;进而,借鉴粒子滤波采样思想、交替投影框架,构建粒子集群同步投影、采样更新机制。仿真实验表明,所提算法运算效率高、稳定性强,波形兼具低旁瓣、低截获与多普勒容忍性,与当前流行算法相比具有显著优势。