完全欧几里德距离变换的最优算法

欧几里德距离变换（ＥＤＴ）对由黑白素构成的二值图象中所有象素找出其到最近黑素的距离，应用于图象分析，计算机视觉，在本文之前，该问题的最好复杂度为Ｏ（ｎ＾２ｌｏｇｎ）。本文提出了一个复杂度为Ｏ（ｎ＾２）的算法，使复杂度达到最优，该算法可以并行化，在有ｒ个处理单元的ＥＲＥＷＰＲＡＭ计算模型上，若ｒｌｏｇｒ≤２２／６ｎ，则时间复杂度为Ｏ（ｎ／ｒ）否则为Ｏ（ｎｌｏｇｒ）。     

基于微粒群优化算法的超市最优选址定量化研究

论文尝试使用微粒群优化算法与GIS相结合解决超市最优选址问题。首先,对影响超市经营好坏的因子进行了分析,包括:人口密度、交通因子以及竞争因子的影响;然后,详细阐述了微粒群优化算法与GIS技术相结合用于解决超市最优选址的实施方法;最后,以广州市芳村区为例,对PSO方法进行实例验证。通过与穷举法进行对比实验,证明微粒群优化算法具有较好的收敛速度、较高的结果精度,是解决超市最优选址的一种有效方法。     

一种加权距离连续K中心选址问题求解方法

K中心选址作为一种经典问题,学者们提出了很多好的解决方法,但是对于加权距离连续K中心选址问题的研究一直没有很好的进展.本文针对连续K中心选址问题,以最小加权距离作为优化目标提出改进的粒子群优化算法(SA-PSO).本文将模拟退火机制引入PSO算法并且加入惯性权重等策略对算法进行改进,使得该算法可以更快收敛于全局最优.仿真实验结果表明,SA-PSO算法相比于GA算法和K-means算法,具有更强的稳定性,收敛速度更快,并且优化得到的加权距离更小.     

基于距离直方图的最优视点选择

基于物体内蕴几何量,提出一种观察三维物体的最优视点选择方法.首先在三维物体表面均匀采样获取采样点,并计算物体形心,然后利用采样点到物体形心的距离来构造距离直方图,最后计算距离直方图的Shannon熵并将其作为衡量视点优劣的标准.根据认知心理学理论,最优视点是存在的,也是恒定的,故文中视点在包围球上选取.实验结果表明,采用该方法获得的最优视点能观察到三维物体更多的功能结构和更显著特征,与其他方法相比更符合人类的感官选择.     

垃圾中转站最优选址及垃圾最优转运方案研究

针对城市垃圾中转站选址问题,建立了中转站最优选址数学模型。给出了一种中心转移算法,其不但解决了中转站的最优选址问题,而且给出了最优选址方案下,每个居民点垃圾的最优转运方案。由于解决这一优化问题的中心转移算法是一种单调迭代算法,因此其不但使用方便,而且有很好的运算效率。     

Hypercube多处理器上图的最优算法

已知一个无向图G(V,E),|V|=n.本文在SIMD机器-Hype-rcube上提出了计算图的连通分支和最小生成树的两个最优算法.若Hypercu-be由P个处理器组成,则上述两个算法的时间复杂性都是O(n~2/p),1≤p且PlogP≤n.     

基于结点间距离统计的无向无权图同构判别

按照同构图的定义判断两个图是否同构,最坏情况下其时间复杂度是O(N!),当结点数N比较大时,计算速度非常慢,针对该问题,提出一种通过统计结点间距离和按照距离分层,计算同层结点间的关联边数以及关联结点数来研究图中各结点差异的算法,该算法可以给出两个图的结点间可能的对应关系.如果两个图的结点距离数组及对应结点的层结点关联数组不能一一对应,其时间复杂度仅为O(N4),否则,根据结点间可能的对应关系,避免遍历所有结点序号的交换,计算量可以成倍地下降.     

急救中心选址及其配车问题研究

在投入的急救车总数确定的情况下,按照急救要求,建立了急救中心的最优选址问题的数学模型,给出了一种松弛算法,其不但能够解决建几个急救中心、建在何处的问题,而且可以确定每一个急救中心的责任范围以及各急救中心应配备的急救车数量。与现有的其他算法相比,该算法更加简便,计算效率更高。     

求最优装载的量子算法

随着Grover量子搜索算法的不断发展,它的实际应用价值也在逐渐体现.通过介绍量子并行计算和量子算法的基本思想以及对改进的Grover搜索算法进行研究的基础上,分析给出了一个时间复杂度为O(√N)的求解最优装载问题的量子算法.对于最优装载问题,分别用经典计算机上的贪心算法和量子算法来求解,得出了这两种算法的时间复杂度,从而可以看出量子算法相对于经典算法具有更快的搜索速度.     

Optimal location query based on k nearest neighbours

Optimal location query in road networks is a basic operation in the location intelligence applications. Given a set of clients and servers on a road network, the purpose of optimal location query is to obtain a location for a new server, so that a certain objective function calculated based on the locations of clients and servers is optimal. Existing works assume no labels for servers and that a client only visits the nearest server. These assumptions are not realistic and it renders the existing work not useful in many cases. In this paper, we relax these assumptions and consider the k nearest neighbours (KNN) of clients. We introduce the problem of KNN-based optimal location query (KOLQ) which considers the k nearest servers of clients and labeled servers. We also introduce a variant problem called relocation KOLQ (RKOLQ) which aims at relocating an existing server to an optimal location. Two main analysis algorithms are proposed for these problems. Extensive experiments on the real road networks illustrate the efficiency of our proposed solutions.     

Rotation distance is fixed-parameter tractable

Rotation distance between trees measures the number of simple operations it takes to transform one tree into another. There are no known polynomial-time algorithms for computing rotation distance. In the case of ordered rooted trees, we show that the rotation distance between two ordered trees is fixed-parameter tractable, in the parameter, k, the rotation distance. The proof relies on the kernelization of the initial trees to trees with size bounded by 5k.     

New optimal algorithm of data association for multi-passive-sensor location system

In dense target and false detection scenario of four time difference of arrival (TDOA) for multi-passive-sensor location system, the global optimal data association algo- rithm has to be adopted. In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assign- ment algorithm and a 2-stage association algorithm based on a statistic test. Compared with the traditional optimal algorithm, the new optimal algorithm avoids the complicated operations for finding the target position before we calculate as- sociation cost; hence, much of the procedure time is saved. In the 2-stage asso- ciation algorithm, a large number of false location points are eliminated from can- didate associations in advance. Therefore, the operation is further decreased, and the correct data association probability is improved in varying degrees. Both the complexity analyses and simulation results can verify the effectiveness of the new algorithms.     

基于特征匹配和距离加权的蓝牙定位算法

针对传统iBeacon指纹定位技术中接收信号强度值（RSSI）波动较大、指纹库聚类复杂、存在较大跳变性定位误差等问题,提出一种基于排序特征匹配和距离加权的蓝牙定位算法。在离线阶段,该算法先对RSSI进行加权滑动窗处理,然后根据RSSI向量大小生成排序特征码等值,并与位置坐标等信息组成指纹信息,形成指纹库;在在线定位阶段,根据排序特征向量指纹匹配定位算法和基于距离的最优加权K最邻近法（WKNN）实现室内行人定位。在定位仿真实验中,该算法可以自动根据特征码进行聚类,从而降低了聚类的复杂度,能实现最大误差在0.952 m内的室内行人定位精度。     

基于分布估计算法的配电网故障区段定位

传统的人工智能算法在配电网馈线故障定位中的应用广泛,存在初始种群规模大,迭代次数多以及易陷入局部最优等缺陷。提出一种基于分布式估计算法的配电网故障区段定位方法,该方法将故障区段向量作为正确解,通过建立解空间内个体分布的概率模型,对模型采样,逐步提高最优故障区段向量在解空间内出现的概率。仿真结果表明将分布估计算法应用于多源开环条件下的配电网故障区段定位有着较快的故障定位速度和良好的容错性。     

基于遗传算法与密度及距离计算的聚类方法

为解决聚类中心选择困难和数据点密度计算泛化能力弱的问题,提出一种基于遗传算法与密度及距离计算的聚类方法.该算法通过指数方法计算数据点密度,降低参数对算法性能的影响;用遗传算法搜索最优密度和距离阈值,同时引入惩罚因子,克服算法搜索域偏移从而提高收敛速度,寻找最优聚类中心,并用归属方法完成聚类.通过4组人工数据集和4组UCI数据集实验证明,该方法在RI指数、聚类精度、聚类纯度、召回率等4个聚类评价指标上都达到与K-means算法、快速搜索聚类算法和Max_Min_SD算法相当或更好的效果,算法是有效的.     

Development of acoustic source localization with adaptive neural network using distance mating-based red deer algorithm

Multichannel, audio processing approaches are widely examined in human–computer interaction, autonomous robots, audio surveillance, and teleconferencing systems. The numerous applications are linked to the speech technology and acoustic analysis area. Much attention is received to the active speakers and spatial localization of acoustic sources on the acoustic sensor arrays. Baseline approaches provide negotiable performance in a real-world comprised of far-field/near-field monitoring, reverberant and noisy environments, and also the outdoor/indoor scenarios. A practical system to detect defects in complex structures is the time difference mapping (TDM) technique. The significant scope of the research is to search the location using the minimum distance point in the time difference database to be apart from the verification point. In the case of the improved “time difference mapping (I-TDM)” technique and traditional “time difference mapping (T-TDM)” technique, the denser grids and vast database permit increased accuracy. In the database, if the location points are not present, then the accurate localization of the I-TDM and T-TDM techniques is damaged. Hence, to handle these problems, this article plans to develop acoustic source localization according to the deep learning strategy. The audio dataset is gathered from the benchmark source called the SSLR dataset and is initially subjected to preprocessing, which involves artifact removal and smoothing for effective processing. Further, the adaptive convolutional neural network (CNN)-based feature set creation is performed. Here, the adaptive CNN is accomplished by the improved optimization algorithm called distance mating-based red deer algorithm (DM-RDA). With this trained feature set, the acoustic source localization is done by the weight updated deep neural network, in which the same DM-RDA is used for optimizing the training weight. The simulation outcome proves that the designed model produced enhanced performance compared to other traditional source localization estimators.     

基于最优跳距和LevyPSO算法的DV_Hop定位算法*

针对DV_Hop(Distance Vector-Hop)算法中定位精度较低的问题,提出一种基于最优跳距与LevyPSO算法的无线传感器网络定位算法,即OLPDV_Hop(Optimal Jump Distance and Levy Particle Swarm Optimization DV_Hop).首先,通过单跳平均误差修正平均跳距,然后利用接收多锚节点的平均跳距估算节点间距离,使估算距离得以优化,最后利用LevyPSO算法替代最小二乘法求得未知节点位置,LevyPSO算法利用Levy飞行改变粒子移动方向以防陷入局部最优,并通过贪婪的更新评价策略产生最优解,最终得到全局最优.仿真结果表明,OLPDV_Hop 算法较 DV_Hop 算法、IPSODV_Hop(Improved Particle Swarm Optimization DV_Hop)算法和BDV_Hop(Based on DV_Hop)算法在定位精度上有明显改善.     

二元传感器网络目标定位与跟踪研究

传感器的通信带宽和能量资源有限使传统的位置测量传感器的应用受到了限制,对此,选择利用二元传感器网络进行相应的研究．基于二元传感器传送信息量少的特性,提出一种新的二元传感器网络目标定位与跟踪算法．仿真结果表明,该算法在传感器个数和探测半径较大、采样周期较短的情况下较质心算法具有更好的性能．