WSN Mobile Target Tracking Based on Improved Snake-Extended Kalman Filtering Algorithm

A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.

Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

GLOBAL OPTIMIZATION OF PUMP CONFIGURATION PROBLEM USING EXTENDED CROWDING GENETIC ALGORITHM

An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.

Solution of Algebraic Lyapunov Equation on Positive-Definite Hermitian Matrices by Using Extended Hamiltonian Algorithm

This communique is opted to study the approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices.We choose the geodesic distance between􀀀AHX􀀀XA and P as the cost function,and put forward the Extended Hamiltonian algorithm(EHA)and Natural gradient algorithm(NGA)for the solution.Finally,several numerical experiments give you an idea about the effectiveness of the proposed algorithms.We also show the comparison between these two algorithms EHA and NGA.Obtained results are provided and analyzed graphically.We also conclude that the extended Hamiltonian algorithm has better convergence speed than the natural gradient algorithm,whereas the trajectory of the solution matrix is optimal in case of Natural gradient algorithm(NGA)as compared to Extended Hamiltonian Algorithm(EHA).The aim of this paper is to show that the Extended Hamiltonian algorithm(EHA)has superior convergence properties as compared to Natural gradient algorithm(NGA).Upto the best of author’s knowledge,no approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices is found so far in the literature.

An Extended Algorithm of Flexibility Analysis in Chemical Engineering Processes

An extended algorithm of flexibility analysis with a local adjusting method for flexibility region of chemical processes, which is based on the active constraint strategy, is proposed, which fully exploits the flexibility region of the process system operation. The hyperrectangular flexibility region determined by the extended algorithm is larger than that calculated by the previous algorithms. The limitation of the proposed algorithm due to imperfect convexity and its corresponding verification measure are also discussed. Both numerical and actual chemical process examples are presented to demonstrate the effectiveness of the new algorithm.

An extended particle swarm optimization algorithm based on coarse-grained and fine-grained criteria and its application

In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.

CONVERGENCE OF A MODIFIED SLP ALGORITHM FOR THE EXTENDED LINEAR COMPLEMENTARITY PROBLEM

A modified sequential linear programming algorithm is presented, whose subproblem is always solvable, for the extended linear complementarity problem (XLCP), the global convergence of the algorithm under assumption of X-row sufficiency or X-colunm monotonicity is proved. As a result, a sufficient condition for existence and boundedness of solution to the XLCP are obtained.

Extended Active Contour Algorithm Based on Color Variance

一般活跃轮廓算法，使用图象的紧张，被用来活跃地分割 chjects.Because cbjects 有类似的紧张但是不同颜色，从 others.Moreover 分割任何目标是困难的，自从扩大活跃轮廓 algarithm 基于颜色 variance.In 建筑群想象，对解决这些 problems.This 纸的顺序建议的 noise.In 很敏感，这个算法能仅仅在简单环境被使用。

A Numerical Study of the TipWake of aWind Turbine Impeller Using Extended Proper Orthogonal Decomposition

The behavior of the tip wake of a wind turbine is one of the hot issues in the wind power field.This problem can partially be tackled using Computational Fluid Dynamics(CFD).However,this approach lacks the ability to provide insights into the spatial structure of important high-order flows.Therefore,with the horizontal axis wind turbine as the main focus,in this work,firstly,we conduct CFD simulations of the wind turbine in order to obtain a data-driven basis relating to multiple working conditions for further analysis.Then,these data are studied using an extended Proper Orthogonal Decomposition(POD)algorithm.The quantitative results indicate that the tip vortex in the wake has a complicated spatio-temporal morphological configuration in the higher-order extended POD space.The radial velocity modes obtained are effective and credible,and such reconstructed flow of the tip vortex becomes clearer with the increase of the reconstruction orders.Interestingly,the changes of relatively high-order correlation coefficients are essentially affected by the periodic fusion of tip and central eddies in the wake.

基于观测方程重构滤波算法的锂离子电池荷电状态估计

滤波算法中观测方程的准确性在电池状态评估中起着决定性作用。然而,该文通过试验发现,由于温度、工作电流和荷电状态(SOC)的影响,即使使用精度较高的电池模型,扩展卡尔曼滤波(EKF)算法中观测方程的输出值与实际电压之间仍会存在较大误差,即产生了较大的新息。该文提出一种基于观测方程重组的增强型扩展卡尔曼滤波(E-EKF)算法。该算法的核心思想是利用具有温度、SOC和电流自适应能力的误差修正策略对观测方程进行重组,实现算法中新息的降低,进而提高SOC估计的准确性。使用两种不同温度下的典型工况试验对E-EKF算法的性能进行了验证。试验结果表明,该算法能够适应不同的温度和工况,并具有较高的SOC估计精度。

基于优化功率跟随控制的E-REV能量管理策略研究

基于功率跟随控制的增程式电动汽车能量管理策略具有减缓电池寿命衰减与提高车辆NVH性能等优势,但存在阈值参数依赖性强、增程器启停频繁等问题,为此提出了一种基于优化功率跟随控制的E-REV能量管理策略。依据车速、SOC状态与驾驶员的加速踏板力度等信息特征,制定基于功率跟随控制的能量管理策略。在此基础上,针对固定规则参数的局限性,以车辆行驶总成本与SOC变化梯度为目标函数,结合灰狼优化算法对增程器启停功率阈值参数进行优化,减少发动机频繁启停现象。运用Matlab/Simulink搭建控制策略模型,并联合基于Simcenter/AMESIM搭建的整车物理模型进行仿真试验,结果表明:CHTC-LT循环工况下,优化功率跟随控制策略与功率跟随控制策略相比,SOC最大波动值降低了28%,增程器启停次数减少了28.5%,整车燃油经济性提升了6.89%。

具有串并行异类工序约束的多柔性车间联合调度

为了减少具有串并行异类工序约束多车间联合调度的总延期时间,提出了基于知识牵引遗传算法的调度求解方法。首先,采用扩展工艺树描述串并行异类工序约束,并基于无向图描述机器在多车间的分布;针对染色体初始化和进化过程中的扩展工艺树约束,定义了紧前工序数和剩余紧前工序数的概念,基于剩余紧前工序数设计了染色体初始化和进化方法;为了提高遗传算法的进化能力,将种群进化能力和最优个体进化能力作为知识,用于牵引算法的进化方式和方向,从而提出了知识牵引遗传算法的求解方法。经实验验证,知识牵引遗传算法调度的总延期时间均值最小,为30.8 h,说明该算法在多车间调度中具有最好的优化性能;且总延期时间盒须图长度最小,说明知识牵引遗传算法的稳定性也较好。

多传感器融合的室内机器人SLAM

为了解决基于二维激光雷达的SLAM在室内环境中存在定位误差较大和建图不完善的问题,提出一种多传感器融合的室内机器人SLAM算法。该算法针对传统ICP算法在激光SLAM前端存在误匹配问题,采用更适合室内环境的PL-ICP算法,并利用扩展卡尔曼滤波融合轮式里程计和IMU为其提供初始的运动估计值。在建图阶段利用深度相机获取的三维点云数据转化的伪二维激光数据和二维激光雷达获取的数据进行融合,弥补二维激光雷达建图没有垂直方向视野的缺陷。实验结果表明:融合里程计数据相比于单一轮式里程计定位精度至少提升了33%,为PL-ICP算法提供了更高精度的初始迭代值。同时融合建图弥补了单一二维激光雷达建图的缺陷,构建了环境信息更加完善的环境地图。

引入PID反馈的SHAEKF算法估算电池SOC

电池荷电状态(SOC)的估算精度是电动汽车电池组的重要指标。为提升SOC估算精度,在融合Sage-Husa扩展卡尔曼滤波(SHEKF)算法与自适应扩展卡尔曼滤波(AEKF)算法的基础上,增加比例积分微分(PID)反馈环节,形成改进算法。采用粒子群优化(PSO)算法对二阶RC等效电路模型进行参数辨识;用开源电池数据集对模型和算法进行实验和分析。改进的SHAEKF算法在电池动态应力测试(DST)、北京动态应力测试(BJDST)和美国联邦城市驾驶(FUDS)等工况下的平均估计误差都在1%以内,与单纯的融合算法SHAEKF算法相比,最大误差可减小5%。

基于自适应ST算法的机器人轨迹跟踪研究

为实现更精确地机器人轨迹跟踪控制,提高系统的抗干扰能力,研究提出基于自适应搜索和追踪算法的机器人轨迹跟踪方法,先建立机器人模型和跟踪轨迹,再采用支持实时动态参数调整的自适应ST算法。实验表明,三种不同的控制方法均能够使水下机器人达到所需的位置和姿态。其中,指令滤波控制方法表现出更快的平均收敛时间,仅为1.3秒。结果表明,研究所提方法能实现更短的调节时间,减小轨迹跟踪误差,同时保持稳定性和抗干扰性。

改进蚁群算法的森林防火移动机器人路径规划

为解决森林防火移动机器人在森林地形条件的最优路径规划问题,提出一种基于拓展邻域的改进蚁群算法。首先引入定向邻域拓展策略,并将搜索邻域从8个拓展至10个拓展,以求扩大搜索效率与范围;然后综合考虑影响移动机器人的多种因素,利用路径长度和能耗改进启发函数;接着通过位置信息改进初始信息素;最后结合最大-最小蚂蚁系统(MMAS)和精英蚂蚁等算法模型的优点,改进信息素更新规则。结果表明,所提出的改进蚁群算法与传统蚁群算法、基于多启发因素的改进蚁群算法相比,路径长度分别缩短7.66%、6.53%,能耗指标分别下降62.2%、49.3%,综合指标分别下降32.6%、23.1%。研究显示所提出的改进蚁群算法具有更强的全局搜索能力和较好的应用价值。

基于联合参数辨识的粒子群优化扩展粒子滤波的锂电池荷电状态估计

提高参数辨识的精度和SOC算法的精度是提高SOC估计的关键,该文提出了基于联合参数辨识的粒子群优化扩展粒子滤波的荷电状态(SOC)估计方法。在参数辨识阶段,结合遗忘因子递推最小二乘法在线辨识的优势,弥补粒子群辨识精度高但前期缺乏数据无法实时辨识的劣势,联合进行参数辨识;在SOC估计阶段,利用扩展卡尔曼滤波生成重要性密度函数,去克服粒子退化,同时采用粒子群优化算法优化重采样策略改进采样过程缓解粒子贫化。最后在联邦城市运行(FUDS)和US06高速公路运行(US06)工况下将所提算法与F-PF、F-PSO-PF、FPSO-PSO-PF进行了对比,结果表明,在FUDS工况下,方均根误差分别提高了65.4%、56.3%和43.5%;在US06工况下,方均根误差分别提高了45.8%、35.9%和35.1%,验证了所提算法具有较好的适应性和鲁棒性。

一种基于Madgwick-EKF融合算法的卫星姿态测量方法

针对低地球轨道卫星姿态测量时,传感器易受噪声干扰、陀螺仪漂移等问题,提出一种基于Madgwick扩展卡尔曼滤波合算法(EKF)的卫星姿态测量方法。该方法采用陀螺仪、加速度计、磁强计等多传感器数据进行融合,并结合Madgwick算法和EKF算法的优点,实现姿态测量。首先,通过Madgwick算法,利用多个传感器测量数据计算初始姿态。然后,基于初始姿态和实际测量数据,应用EKF算法进行数据融合和噪声滤除,以获得最终准确的姿态估计。实验结果表明:相较Madgwick算法,本算法在测量精度上提升了65.8%,且具有较高的鲁棒性,为低地球轨道卫星姿态测量提供了一种有效的方案。

面向目标体系网络的节点重要性排序方法

针对现有复杂网络节点重要性排序方法无法处理目标体系网络节点异质连边有向有权的难题,提出一种面向目标体系网络的节点重要性排序方法。利用K-shell算法计算网络节点的初始重要值,并在PageRank算法的节点重要性传递中考虑重要性分配趋强的特点和连边权重,提出K-shell和PageRank扩展(Extended K-shell and PageRank,EKSPR)算法,并给出EKSPR算法的收敛性证明,进行了作战仿真实验验证和算例对比分析。实验结果表明,EKSPR算法相对于K-shell算法和PageRank算法更适用于处理目标体系网络节点重要性排序,并且效率优于均值EKSPR算法。

基于WMIAEKF的锂离子电池SOC与容量联合估算

精确的荷电状态(SOC)估算对可靠的电池管理系统来说十分关键。基于二阶等效电路模型,提出了一种加权多新息自适应扩展卡尔曼滤波(WMIAEKF)算法,该算法可以解决传统多新息算法中误差累积的问题,从而提高SOC估算精度。实验仿真结果表明,所提算法比传统的自适应扩展卡尔曼(AEKF)以及多新息自适应扩展卡尔曼(MIAEKF)精度要高,最大误差控制在1.15%以内。此外,基于该算法提出了一种改进的多时间尺度双卡尔曼滤波算法,其中,WMIAEKF用于SOC估算,AEKF用于容量估算,两者结合对电池的SOC和容量进行实时的联合估算。所提算法能够对电池SOC以及容量进行较精确的估计,在新欧洲行驶工况(NEDC)下,SOC估算误差始终控制在1.2%,并且在面对错误容量初始值时也能保持较好的鲁棒性。