A wavelet-based multiscale ensemble time-scale algorithm

The wide-spread availability of ensembles of high-performance clocks has motivated interest in time-scale algorithms. There are many such algorithms in use today in applications ranging from scientific to commercial. Although these algorithms differ in key aspects and are sometimes tailored for specific applications and mixtures of clocks, they all share the goal of combining measured time differences between clocks to form a reference time scale that is more stable than any of the clocks in the ensemble. A new approach to forming time scales is presented here, the multiscale ensemble timescale (METS) algorithm. This approach is based on a multiresolution analysis afforded by the discrete wavelet transform. The algorithm does not assume a specific parametric model for the clocks involved and hence is well-suited for an ensemble of highly disparate clocks. The approach is based on an appealing optimality criterion which yields a reference time scale that is more stable than the constituent clocks over all averaging intervals (scales). The METS algorithm is presented here in detail and is shown in a simulation study to compare favorably with a time-scale algorithm based on Kalman filtering.     

A weight-based multiobjective immune algorithm: WBMOIA

A novel immune algorithm is suggested for finding Pareto-optimal solutions to multiobjective optimization problems based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. In the proposed algorithm, a randomly weighted sum of multiple objectives is used as a fitness function, and a local search algorithm is incorporated to facilitate the exploitation of the search space. Specifically, a new truncation algorithm with similar individuals (TASI) is proposed to preserve the diversity of the population. Also, a new selection operator is presented to create the new population based on TASI. Simulation results on seven standard problems (ZDT2, ZDT6, DEB, VNT, BNH, OSY and KIT) show that the proposed algorithm is able to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the vector immune algorithm and the elitist non-dominated sorting genetic system.     

一种新型的基于LQI的加权质心定位算法

提出一种基于链路质量指示(LQI)的测距技术与加权质心定位算法相结合的新型定位算法。经过数据分析比较,验证了LQI要比接收信号强度指示(RSSI)更稳定、抗干扰能力更强。采用高斯滤波模型对实验测量值进行处理,增加了数据的准确性。建立基于RSSI和LQI的对数拟合测距公式,利用质心定位算法进行仿真实验,结果表明:采用基于LQI测距的加权质心算法定位,其定位精度有了较大提高,达到了较为理想的定位效果。     

多坐标系下目标跟踪的联合滤波

针对雷达目标观测和处理在不同的坐标系下完成，本文提出了联合滤波算法来跟踪机动目标。该算法以卡尔曼滤波器为基础，直角坐标系下和极坐标系下的算法相联合，不仅克服了两种坐标系下滤波算法的不足，而且对机动目标有很好的跟踪效果。仿真实验结果表明了该算法的有效性。     

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disassembly operations are evaluated and taken into consideration in a disassembly line balancing problem. A multi-objective mathematical model is constructed to minimise the number of workstations, maximise the smoothing rate and minimise the average maximum hazard involved in the disassembly line. Subsequently, a Pareto firefly algorithm is proposed to solve the problem. The random key encoding method based on the smallest position rule is used to adapt the firefly algorithm to tackle the discrete optimisation problem of the disassembly line balancing. To avoid the search being trapped in a local optimum, a random perturbation strategy based on a swap operation is performed on the non-inferior solutions. The validity of the proposed algorithm is tested by comparing with two other algorithms in the existing literature using a 25-task phone disassembly case. Finally, the proposed algorithm is applied to solve a refrigerator disassembly line problem based on the field investigation and a comparison of the proposed Pareto firefly algorithm with another multi-objective firefly algorithm in the existing literature is performed to further identify the superior performance of the proposed Pareto firefly algorithm, and eight Pareto optimal solutions are obtained for decision makers to make a decision.     

彩色图像量化的FSCAMMD聚类算法

提出了一种基于模式识别技术的彩色图像量化的新算法－－基于最小距离最大的快速统计聚类算法（FSCAMMD）。本算法克服了SCA算法对聚类中心初始值选取的不足,给出了最大频度与类内最小距离最大相结合的方法－－初始值优选法,实验结果表明,本算法可较大幅度地减少图像量化后的总方差以颜色失真度。     

一种修正的基于混沌预测误差的目标检测算法

基于混沌预测误差的目标检测算法是检测混沌背景下目标的算法.本文对该算法的检测结果进行了分析,分析结果表明其检测到的目标区域比实际的目标区域大.针对这个问题,对基于混沌预测误差的目标检测结果进行了修正,修正后的结果更接近于目标的真实大小.用Lorenz混沌系统产生的仿真数据和实测的机载海面合成孔径雷达(Synthetic Aperture Radar,SAR)图像进行了实验.实验结果与理论分析的结果一致,从而证明了理论分析的正确性.     

基于FxLMS的汽车车内噪声变步长主动均衡算法

提出了一种可用于汽车车内噪声主动均衡控制的变步长主动噪声均衡（Active Noise Equalization,ANE）算法,与传统车内噪声主动抵消控制方法所采用的滤波x最小均方（Filtered-x Least Mean Square,FxLMS）算法相比具有更好的实用性。应用固定步长主动噪声均衡（Active Noise Equalization,ANE）算法、所提出变步长ANE算法和已有变步长ANE算法分别进行汽车车内噪声主动均衡控制。结果表明,所提出变步长ANE算法具有更快的收敛速度和较低的稳态误差,并且能进一步降低汽车车内噪声响度,为汽车车内声品质主动控制提供了一种新方法。     

A Grouping Algorithm for Qualitative Data with A Dichotomous Outcome Variable

This paper presents an algorithm for grouping the values of qualitative predictor variables while minimizing the loss of information about a dichotomous dependent variable. The algorithm is based on Shannon's measure of uncertainty. Subpopulations corresponding to the predictor values are ranked by their conditional Bernoulli parameter. At each iteration the increase in uncertainty resulting from grouping each pair of adjacent subpopulations is computed, and the pair with the least increase is grouped. Stopping rules based on the number of values remaining, the cumulative loss of information and the Maximum Likelihood Chi-Square Statistic are proposed, A numerical example is included.     

A DISPATCHING ALGORITHM FOR MULTIPLE-LOAD AGVS USING A FUZZY DECISION-MAKING METHOD IN A JOB SHOP ENVIRONMENT

This paper proposes a dispatching algorithm for automated guided vehicles (AGV) with multiple-load capacity operating in a job shop environment. At each dispatching point, the algorithm prioritizes all move requests based on the fuzzy urgency which is determined by the fuzzy multi-criteria decision-making method. Then, the algorithm selects a set of requests under the restriction of target dispatching time interval. Through simulation, the performance of the proposed algorithm is compared with several dispatching algorithms in terms of system throughput in a hypothetical job shop environment.     

一种新颖的汉字笔划提取算法

一些手写体汉字识别系统的笔划提取大都采取基于像素跟踪的细化算法,这些方法具有很大的不稳定性。考虑到汉字的结构特点,我们提出了汉字的直线段描述算法,直线段有0, 45, 90 和 135四种方向。在算法中,充分采用了基于结构的差分方法和基于统计的概率估计方法。实验表明,该算法有很好的鲁棒性,这为汉字的识别创造了条件。     

汽车车内噪声主动控制变步长NFB-LMS算法

为规避最小均方（Least Mean Square,LMS）算法不能同时提高收敛速度和降低稳态误差的固有缺陷,以及已有变步长LMS算法存在收敛速度慢和稳态误差估计精度差的问题,文中提出了一种基于变步长归一化频域块（Normalized Frequency-domain Block,NFB） LMS算法的汽车车内噪声主动控制方法。为了比较,应用传统的LMS算法、基于反正切函数的变步长LMS算法和变步长NFB-LMS算法分别进行实测汽车车内噪声的主动控制。结果表明,与其他两个算法相比,变步长NFB-LMS算法的收敛速度提高了70%以上,稳态误差减小了90%以上。变步长NFB-LMS算法在处理车内噪声信号时具有很高的效率,为进行汽车车内噪声主动控制提供了一种新方法。     

A dynamic scheduling algorithm for singe-arm two-cluster tools with flexible processing times

This article presents a dynamic algorithm for job scheduling in two-cluster tools producing multi-type wafers with flexible processing times. Flexible processing times mean that the actual times for processing wafers should be within given time intervals. The objective of the work is to minimize the completion time of the newly inserted wafer. To deal with this issue, a two-cluster tool is decomposed into three reduced single-cluster tools (RCTs) in a series based on a decomposition approach proposed in this article. For each single-cluster tool, a dynamic scheduling algorithm based on temporal constraints is developed to schedule the newly inserted wafer. Three experiments have been carried out to test the dynamic scheduling algorithm proposed, comparing with the results the ‘earliest starting time’ heuristic (EST) adopted in previous literature. The results show that the dynamic algorithm proposed in this article is effective and practical.     

基于天牛须改进粒子群算法的平面度误差评定研究

基于天牛须改进粒子群算法(BAS-PSO)对平面度误差进行了评定研究.首先,建立基于最小区域的平面度误差评定的数学模型,并将目标函数转化为非线性最优化问题;接着,在粒子群算法(PSO)的基础上,引人局部搜索能力较强的天牛须算法(BAS),加速全局搜索和局部搜索的并行计算,避免算法早熟收敛并陷入局部最优,提高平面度误差评...     

基于零陷展宽的双基地声纳直达波抑制算法

双基地声纳系统中存在目标信号与直达波干扰混叠且两者高相关的情况,常规的自适应抵消法因此失效。在双基地声纳系统配置已知的前提下,提出一种基于零陷展宽技术的多约束最小方差直达波抑制算法。该方法通过施加线性约束对特定方向的干扰进行抑制,并针对直达波干扰方位存在扰动时,多约束最小方差算法性能急剧下降的现象运用零陷展宽的方法对算法加以改进。基于零陷展宽的多约束最小方差算法直观、快捷。仿真及实验数据处理的结果表明,在直达波干扰方位大致可知的情况下,该算法能够对直达波进行有效抑制,且对直达波的方位估计误差具有稳健性。     

一种基于蝙蝠算法的多目标跟踪数据关联方法

针对多目标跟踪中的数据关联问题,提出了基于蝙蝠算法的数据关联方法。首先将多目标数据关联问题建模成组合优化问题,结合数据关联的特点,对蝙蝠算法的搜索更新规则进行改进,使其可以应用于多目标的数据关联问题,并给出了基于蝙蝠算法的多目标数据关联的详细流程。通过仿真实验和被动声呐实测数据测试表明,基于蝙蝠算法的多目标数据关联方法切实可行并且具有较好的效果。     

求解一类无关并行机调度的遗传迭代贪心算法

以最小化最大完工时间为优化目标,建立带工单加工约束和序相关设置时间无关并行机调度问题的混合整数规划模型;考虑现实生产对求解算法在质量、收敛速度和鲁棒性等方面的较高要求,构建一种混合遗传-迭代贪心算法。在遗传变异操作中嵌入一种迭代贪心策略的破坏和构建机制,用于提高算法的种群多样性;引入基于破坏与构建操作设计而成的快速局部搜索算法来增强算法的局部开发能力;基于实际生产数据的相关特征随机生成了一系列计算案例,并通过实验说明所提新型混合算法相较于传统混合算法的优越性。     

A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems

Job shop scheduling problem (JSSP) is a typical NP-hard problem. In order to improve the solving efficiency for JSSP, a hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search is proposed in this paper, which combines the merits of Estimation of distribution algorithm and Differential evolution (DE). Meanwhile, to strengthen the searching ability of the proposed algorithm, a chaotic strategy is introduced to update the parameters of DE. Two mutation operators are adopted. A neighbourhood search (NS) algorithm based on blocks on critical path is used to further improve the solution quality. Finally, the parametric sensitivity of the proposed algorithm has been analysed based on the Taguchi method of design of experiment. The proposed algorithm was tested through a set of typical benchmark problems of JSSP. The results demonstrated the effectiveness of the proposed algorithm for solving JSSP.     

A biogeography-based optimization for optimum discrete design of skeletal structures

This article presents a modified biogeography-based optimization (MBBO) algorithm for optimum design of skeletal structures with discrete variables. The main idea of the biogeography-based optimization (BBO) algorithm is based on the science of biogeography, in which each habitat is a possible solution for the optimization problem in the search space. This algorithm consists of two main operators: migration and mutation. The migration operator helps the habitats to exploit the search space, while the mutation operator guides habitats to escape from the local optimum. To enhance the performance of the standard algorithm, some modifications are made and an MBBO algorithm is presented. The performance of the MBBO algorithm is evaluated by optimizing five benchmark design examples, and the obtained results are compared with other methods in the literature. The numerical results demonstrate that the MBBO algorithm is able to show very competitive results and has merits in finding optimum designs.     

基于图像匹配的移动机器人导航研究

针对移动机器人导航过程中基于尺度不变特征变换(SIFT)算法图像匹配速度较慢,提出了基于减法聚类和特征描述符二值化的改进SIFT算法。通过减法聚类消除大量特征点中的冗余特征点,在不影响原SIFT算法稳定性的前提下有效降低了特征点数量,然后将生成的特征描述符进行二值化,依据Hash函数生成索引,以汉明距离作为度量准则。实验结果表明:与原SIFT算法相比,改进的SIFT算法中特征点数量下降30%~40%;匹配对数基本维持不变;匹配率上升6%~12%;匹配时间下降60%~70%。与基于颜色矩的改进SIFT分级图像匹配算法相比,改进的SIFT算法中特征点数量下降15%~25%;匹配对数基本维持不变;匹配率上升5%~10%;匹配时间下降45%~55%。