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.

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.

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。

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。

Tower crane path planning based on improved ant colony algorithm

In order to solve the problem of path planning of tower cranes,an improved ant colony algorithm was proposed.Firstly,the tower crane was simplified into a three-degree-of-freedom mechanical arm,and the D-H motion model was established to solve the forward and inverse kinematic equations.Secondly,the traditional ant colony algorithm was improved.The heuristic function was improved by introducing the distance between the optional nodes and the target point into the function.Then the transition probability was improved by introducing the security factor of surrounding points into the transition probability.In addition,the local path chunking strategy was used to optimize the local multi-inflection path and reduce the local redundant inflection points.Finally,according to the position of the hook,the kinematic inversion of the tower crane was carried out,and the variables of each joint were obtained.More specifically,compared with the traditional ant colony algorithm,the simulation results showed that improved ant colony algorithm converged faster,shortened the optimal path length,and optimized the path quality in the simple and complex environment.

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。

基于MapReduce与相关子空间的局部离群数据挖掘算法

针对高维海量数据,在Map Reduce编程模型下,提出了一种基于相关子空间的局部离群数据挖掘算法.该算法首先利用属性维上的局部稀疏程度,重新定义了相关子空间,从而能够有效地刻画各种局部数据集上的分布特征;其次,利用局部数据集的概率密度,给出了相关子空间中的局部离群因子计算公式,有效地体现了相关子空间中数据对象不服从局部数据集分布特征的程度,并选取离群程度最大的N个数据对象定义为局部离群数据;在此基础上,采用LSH分布式策略,提出了一种Map Reduce编程模型下的局部离群数据挖掘算法;最后,采用人工数据集和恒星光谱数据集,实验验证了该算法的有效性、可扩展性和可伸缩性.

我国地方政府债务风险评价

与西方国家不同,我国的政治经济体制决定了中央政府与地方政府之间存在紧密的"父子关系",在地方政府债务风险的评价中,对中央政府"父爱主义"的忽略往往会夸大地方政府债务风险。本文将中央政府"父爱主义"提炼成地方政府债务的可转移性特征,通过引入地方政府债务可转移性这一因素,提出了相应的KMV修正模型,并以2008-2010年全国18个省市的相关年度数据为样本,经相关面板数据分析,利用KMV修正模型对我国地方政府债务违约风险以及在给定1%和5%违约概率前提下地方政府债务转移率进行了实证分析。结果表明:我国地方政府债务本质上并不存在经济上的违约风险,但地方政府债务却存在一定程度的道德风险。对此,本文最后依据压力测试分析提出了相关的政策建议。

基于面元的机器人三维激光雷达室内实时定位和建图方法

为了满足室内服务机器人定位能力要求,在考虑室内环境结构化特征和激光雷达独特优势的基础上,提出了从雷达点云中提取面元表达环境以实现室内精确定位和建图的方法。对激光雷达的扫描线进行分割,通过计算分割段内各点的凸系数的连续性条件确定面元覆盖范围。定位和建图分为两个阶段,首先求解点云帧间运动,作为全局位姿的估计;其次对全局位姿进一步优化,依概率将帧面元融入地图。两个阶段均优化隐式移动最小二乘距离代价,而面元不确定模型则由激光雷达测量误差确定。最后,分别在空旷和狭窄两种场景下进行试验,结果表明,所提方法相对定位精度分别为0.022%和0.11%,地图一致性好、满足实时性要求。

基于局域灰度概率分布的小目标检测方法

文章将红外图像目标与背景的灰度差转换为局部范围内的灰度概率差,在局域概率分布图像上实现小目标的检测,然后通过多帧累加判决的方式将真实目标从噪声点和伪目标中检测出来,仿真实验表明,该方法能有效检测像素占1～3个的小目标,同时具有良好的实时性能.

自主移动机器人室内定位方法研究综述

自主移动机器人的室内定位作为机器人研究领域中最基本的问题已被广泛研究。根据定位技术和传感器的不同,将室内定位方法分为航迹推算定位、地图匹配定位和基于信标定位三类。详细介绍了超声波网络定位系统和基于无线射频识别(RFID)的定位方法。对几种基于概率的定位算法做了分析和对比,并对自主移动机器人室内定位方法的研究方向做了展望。

复杂流动下泥沙起动概率的图像测量

提出了一种适合于复杂流动下泥沙起动概率的测量方法——图像测量法.该方法对床面上砂颗粒的运动图像进行采集,根据采集到的图像采用互相关算法计算出砂颗粒的运动速度,由砂颗粒运动速度得到泥沙的起动概率.采用该方法测量了槽道流动及后台阶流动2种情况下床面泥沙的起动概率.在槽道流动下,床面泥沙的起动概率随着床面剪应力的增大而增大.由本文测量方法得到的临界起动剪应力与希尔兹曲线所得到的结果相一致.在后台阶流动下,测量结果表明,再附点附近处泥沙的起动概率最大.这一结果能够解释在后台阶下游床面局部冲刷实验中发现的再附点附近冲刷最为严重的现象.