LEAST-SQUARES METHOD-BASED FEATURE FITTING AND EXTRACTION IN REVERSE ENGINEERING

The main purpose of reverse engineering is to convert discrete data pointsinto piecewise smooth, continuous surface models. Before carrying out model reconstruction it issignificant to extract geometric features because the quality of modeling greatly depends on therepresentation of features. Some fitting techniques of natural quadric surfaces with least-squaresmethod are described. And these techniques can be directly used to extract quadric surfaces featuresduring the process of segmentation for point cloud.

PCR ALGORITHM FOR PARALLEL COMPUTING MINIMUM-NORM LEAST-SQUARES SOLUTION OF INCONSISTENT LINEAR EQUATIONS

This paper presents a new highly parallel algorithm for computing the minimum-norm least-squares solution of inconsistent linear equations Ax = b(A∈Rm×n,b∈R (A)). By this algorithm the solution x = A + b is obtained in T = n(log2m + log2(n - r + 1) + 5) + log2m + 1 steps with P=mn processors when m × 2(n - 1) and with P = 2n(n - 1) processors otherwise.

NEW EFFICIENT ORDER-RECURSIVE LEAST-SQUARES ALGORITHMS

Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order of the underlying model isunknown.On the basis of several universal formulae for updating nonsymmetric projec-tion operators,this paper presents three kinds of LS algorithms,called nonsymmetric,symmetric and square root normalized fast ORLS algorithms,respectively.As to the au-thors’ knowledge,the first and the third have not been so far provided,and the second isone of those which have the lowest computational requirement.Several simplified versionsof the algorithms are also considered.

Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm

Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal dispatch. Now get coal consumption curve is generally obtained by least square method, but which are static curve and these curves remain unchanged for a long time, and make them are incompatible with the actual operation situation of the unit. Furthermore, coal consumption has the characteristics of typical nonlinear and time varying, sometimes the least square method does not work for nonlinear complex problems. For these problems, a method of coal consumption curve fitting of the thermal power plant units based on genetic algorithm is proposed. The residual analysis method is used for data detection;quadratic function is employed to the objective function;appropriate parameters such as initial population size, crossover rate and mutation rate are set;the unit’s actual coal consumption curves are fitted, and comparing the proposed method with least squares method, the results indicate that fitting effect of the former is better than the latter, and further indicate that the proposed method to do curve fitting can best approximate known data in a certain significance, and they can real-timely reflect the interdependence between power output and coal consumption.

An improved bicubic imaging fitting algorithm for 3D radar detection target

3D ground-penetrating radar has been widely used in urban road underground disease detection due to its nondestructive,efficient,and intuitive results.However,the 3D imaging of the underground target body presents the edge plate phenomenon due to the space between the 3D radar array antennas.Consequently,direct 3D imaging using detection results cannot reflect underground spatial distribution characteristics.Due to the wide-beam polarization of the ground-penetrating radar antenna,the emission of electromagnetic waves with a specific width decreases the strong middle energy on both sides gradually.Therefore,a bicubic high-precision 3D target body slice-imaging fitting algorithm with changing trend characteristics is constructed by combining the subsurface target characteristics with the changing spatial morphology trends.Using the wide-angle polarization antenna’s characteristics in the algorithm to build the trend factor between the measurement lines,the target body change trend and the edge detail portrayal achieve a 3D ground-penetrating radar-detection target high-precision fitting.Compared with other traditional fitting techniques,the fitting error is small.This paper conducts experiments and analyses on GpaMax 3D forward modeling and 3D ground-penetrating measured radar data.The experiments show that the improved bicubic fitting algorithm can eff ectively improve the accuracy of underground target slice imaging and the 3D ground-penetrating radar’s anomaly interpretation.

A Gradient-Simulated Annealing Algorithm of Pre-location-Based Best Fitting of Blank to Complex Surfaces Machining

The algorithm is divided into two steps. The first step pre-locates the blank by aligning its centre of gravity and approximate normal vector with those of destination surfaces, with largest overlap of projections of two objects on a plane perpendicular to the normal vector. The second step is optimizing an objective function by means of gradient-simulated annealing algorithm to get the best matching of a set of distributed points on the blank and destination surfaces. An example for machining hydroelectric turbine blades is given to verify the effectiveness of algorithm.

基于Vector Fitting的光伏并网逆变器控制器参数频域辨识方法

光伏并网逆变器通常含有内外环、锁相环等不同带宽控制环节,且控制器参数往往并不可知,即存在“灰箱”问题。为准确辨识不同带宽控制器参数,提出一种基于端口导纳特性的光伏并网逆变器控制器参数频域辨识方法。首先,建立典型控制下光伏并网逆变器交流端口的dq理论导纳模型,得到其理论导纳标准式;然后,通过扫频手段获得光伏并网逆变器交流端口的测量导纳数据,并采用Vector Fitting算法对测量的端口导纳数据进行矢量拟合,得到拟合导纳标准式;最后,运用最小二乘原理使理论导纳标准式与拟合导纳标准式对应项系数差值的平方和最小,从而辨识得到光伏并网逆变器控制器参数的估计值。参数辨识实例表明,所提方法能够同时准确辨识出不同带宽控制器参数。

No Fit Polygon for Nesting Problem Solving with Hybridizing Ant Algorithms

In design science, these two kinds of problems are mutually nested, however, the nesting could not blind us for the fact that their problem-solving and solution justification methods are different. The ant algorithms research field, builds on the idea that the study of the behavior of ant colonies or other social insects is interesting, because it provides models of distributed organization which could be utilized as a source of inspiration for the design of optimization and distributed control algorithms. In this paper, a relatively new type of hybridizing ant search algorithm is developed, and the results are compared against other algorithms. The intelligence of this heuristic approach is not portrayed by individual ants, but rather is expressed by the colony as a whole inspired by labor division and brood sorting. This solution obtained by this method will be evaluated against the one obtained by other traditional heuristics.

Iterative circle fitting based on circular attracting factor

An intuitive method for circle fitting is proposed. Assuming an approximate circle(CA,n) for the fitting of some scattered points, it can be imagined that every point would apply a force to CA,n, which all together form an overall effect that "draws" CA,n towards best fitting to the group of points. The basic element of the force is called circular attracting factor(CAF) which is defined as a real scalar in a radial direction of CA,n. An iterative algorithm based on this idea is proposed, and the convergence and accuracy are analyzed. The algorithm converges uniformly which is proved by the analysis of Lyapunov function, and the accuracy of the algorithm is in accord with that of geometric least squares of circle fitting. The algorithm is adopted to circle detection in grayscale images, in which the transferring to binary images is not required, and thus the algorithm is less sensitive to lightening and background noise. The main point for the adaption is the calculation of CAF which is extended in radial directions of CA,n for the whole image. All pixels would apply forces to CA,n, and the overall effect of forces would be equivalent to a force from the centroid of pixels to CA,n. The forces from would-be edge pixels would overweigh that from noisy pixels, so the following approximate circle would be of better fitting. To reduce the amount of calculation, pixels are only used in an annular area including the boundary of CA,n just in between for the calculation of CAF. Examples are given, showing the process of circle fitting of scattered points around a circle from an initial assuming circle, comparing the fitting results for scattered points from some related literature, applying the method proposed for circular edge detection in grayscale images with noise, and/or with only partial arc of a circle, and for circle detection in BGA inspection.

GENETIC ALGORITHM WITH FUZZY FITNESS EVALUATION

Using a fuzzy estimator to evaluate the fitness of chromosomes in a genetic algorithm and adaptively training it in the evolutionary process, the genetic algorithm with fuzzy fitness evaluation is proposed to reduce the computation time of the algorithm. An analysis on the optimization performance of the proposed algorithm shows that it maintains good performance with its computation time saved. Finally, simulation results on design of a fuzzy controller are presented.

A reordered first fit algorithm based novel storage scheme for parallel turbo decoder

In this paper we discuss a novel storage scheme for simultaneous memory access in parallel turbo decoder. The new scheme employs vertex coloring in graph theory. Compared to a similar method that also uses unnatural order in storage, our scheme requires 25 more memory blocks but allows a simpler configuration for variable sizes of code lengths that can be implemented on-chip. Experiment shows that for a moderate to high decoding throughput （40-100 Mbps）, the hardware cost is still affordable for 3GPP＇s （3rd generation partnership project） interleaver.

Multi-path planning algorithm based on fitness sharing and species evolution

A new algorithm is proposed for underwater vehicles multi-path planning. This algorithm is based on fitness sharing genetic algorithm, clustering and evolution of multiple populations, which can keep the diversity of the solution path, and decrease the operating time because of the independent evolution of each subpopulation. The multi-path planning algorithm is demonstrated by a number of two-dimensional path planning problems. The results show that the multi-path planning algorithm has the following characteristics: high searching capability, rapid convergence and high reliability.

Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time durations.At the same time,vehicles exhibit high velocity,leading to more vehicle disconnections.Both of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability.Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs.But one such difficulty was reducing the cluster number under increasing transmitting nodes.This article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for VANET.The major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping vehicles.In addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given space.The DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean distance.The experimental assessment of the DHOGO-EAC technique was carried out under distinct aspects.The comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications.In spite of this,it is challenging to design energy-efficient WSN.The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network.In order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network development.In this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for WSN.The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN.To accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution quality.Besides,the inclusion of DE in traditional AOA helps in overcoming local optima problems.In addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance.In order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches.

基于正弦函数拟合的高动态捷联惯导姿态更新算法

为提高捷联惯导在高动态条件下的姿态解算精度,基于等效旋转矢量泰勒级数展开法,提出一种基于正弦函数拟合的高动态捷联惯导姿态更新算法。以正弦函数拟合载体运动角速度,考虑Bortz方程高阶项的影响,对陀螺角增量表示的旋转矢量进行泰勒六阶展开,对比旋转矢量不同形式表达式求得误差补偿系数。在MATLAB平台上,以圆锥运动与大角速率转动并存环境作为仿真条件,对所提算法与传统算法进行对比仿真分析。仿真结果表明,在小半锥角低频圆锥运动伴随高速角速率转动情况下,所提算法性能较好,当半锥角为0.5°、角频率为2.26πrad/s、常值角速率为5.30 rad/s、姿态解算周期为0.02 s时,所提正弦函数拟合三子样旋转矢量算法与传统扩展形式频率级数/显示频率三子样圆锥算法相比误差降低了2个数量级。

改进蚁群算法的送餐机器人路径规划

蚁群算法拥有良好的全局性、自组织性、鲁棒性,但传统蚁群算法存在许多不足之处。为此,针对算法在路径规划问题中的缺陷,在传统蚁群算法的状态转移公式中,引入目标点距离因素和引导素,加快算法收敛性和改善局部最优缺陷。在带时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)上,融合蚁群算法和遗传算法,并将顾客时间窗宽度以及机器人等待时间加入蚁群算法状态转移公式中,以及将蚁群算法的解作为遗传算法的初始种群,提高遗传算法的初始解质量,然后进行编码,设置违反时间窗约束和载重量的惩罚函数和适应度函数,在传统遗传算法的交叉、变异操作后加入了破坏-修复基因的操作来优化每一代新解的质量,在Solomon Benchmark算例上进行仿真,对比算法改进前后的最优解,验证算法可行性。最后在餐厅送餐问题中把带有障碍物的仿真环境路径规划问题和VRPTW问题结合,使用改进后的算法解决餐厅环境下送餐机器人对顾客服务配送问题。

基于轨迹线改进的临界多边形算法

在异形件的下料排样问题中,最为困难的就是求解裁片在板料中的位置以保证材料较高的利用率,算法复杂度随着料片数量和料片轮廓复杂度的增加迅速上升。临界多边形算法是计算异形件之间靠接位置和重叠关系的一种基础性几何工具,临界多边形算法的性能与下料排样算法效率密切相关。本文在基于轨迹线的求解临界多边形算法基础上,提出一种求解临界多边形(NFP)的改进算法。该算法有效地将移动碰撞算法和轨迹线算法相结合,充分发挥两类算法各自的优点,提高了临界多边形求解的计算速度。仿真实例验证了改进方法的正确性和有效性。

基于动作时限曲线拟合思想的含DG型配电网后备保护新方法

分布式电源(distributed generation,DG)的接入可能会改变电力系统的短路电流特性,从而影响到反时限过流保护的选择性和配合关系。为此,提出一种基于动作时限曲线拟合思想的含DG型配电网后备保护新方法。从配电网主保护和后备保护的动作时间差入手,建立曲线拟合模型,并运用梯度下降法对反时限过流保护的动作时限曲线进行优化。通过在优化整定计算中考虑保护的选择性以及DG接入所带来的影响,使优化参数适用于配电网中各分段位置。然后,在配电网中设置两相、三相短路故障,并根据故障位置、类型进行整定参数再调整,最终得到参数的全局最优解。基于PSCAD/EMTDC仿真软件进行仿真分析,验证了所提方法的有效性和实用性。

基于偏好和虚拟适应度的两阶段依赖任务卸载算法

针对云边端协同环境中依赖任务卸载时效率低以及任务卸载失败的问题,提出一种基于偏好和虚拟适应度的两阶段依赖任务卸载算法.第一阶段,根据提出的二维卸载偏好因子对依赖任务的部分子任务进行直接卸载决策,从而有效缩小遗传算法初始种群的规模.第二阶段,提出基于虚拟适应度的启发式交叉方法,并对基于参考点的快速非支配排序遗传算法(non-dominated sorting genetic algorithmⅢ, NSGA-Ⅲ)的交叉算子进行改进,保留了种群多样性并提升了算法收敛速度,最后使用改进的算法对所有依赖任务的子任务进行最优卸载决策集的搜索.实验结果表明,与其他算法相比,该算法在任务完成时间、任务能耗和边缘云集群成本方面平均优化了10.2%~18.3%,并且将任务失败率平均降低了10.7%~25.6%.

改进布谷鸟算法在装配序列规划中的应用研究

针对装配序列规划问题,建立考虑装配序列的几何可行性、稳定性、聚合性、重定向性的装配关系模型以及基于适应度函数的装配序列优化数学模型。提出一种改进布谷鸟算法对装配序列规划问题进行求解,采用随机键和最小位置规则的方法设计基于零件编号、装配方向、装配工具的3层编码方案;设计基于最小装配成本的初始化策略与随机初始化策略相结合的混合种群初始化策略,提高种群质量;改进种群进化和搜索方式,将种群分为3个子群,并分别采用自适应步长飞行、标准步长飞行和交叉、变异的方式进行种群更新,提高算法的收敛速度和求解精度。最后通过实例应用及与其他算法的比较,验证了所提出的改进布谷鸟算法在求解装配序列规划问题上的有效性和优越性。