Nearest neighbor search algorithm based on multiple background grids for fluid simulation

The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.

Damage detection of 3D structures using nearest neighbor search method

An innovative damage identification method using the nearest neighbor search method to assess 3D structures is presented.The frequency response function was employed as the input parameters to detect the severity and place of damage in 3D spaces since it includes the most dynamic characteristics of the structures.Two-dimensional principal component analysis was utilized to reduce the size of the frequency response function data.The nearest neighbor search method was employed to detect the severity and location of damage in different damage scenarios.The accuracy of the approach was verified using measured data from an experimental test;moreover,two asymmetric 3D numerical examples were considered as the numerical study.The superiority of the method was demonstrated through comparison with the results of damage identification by using artificial neural network.Different levels of white Gaussian noise were used for polluting the frequency response function data to investigate the robustness of the methods against noise-polluted data.The results indicate that both methods can efficiently detect the damage properties including its severity and location with high accuracy in the absence of noise,but the nearest neighbor search method is more robust against noisy data than the artificial neural network.

A nearest neighbor search algorithm of high-dimensional data based on sequential NPsim matrix

Problems existin similarity measurement and index tree construction which affect the performance of nearest neighbor search of high-dimensional data. The equidistance problem is solved using NPsim function to calculate similarity. And a sequential NPsim matrix is built to improve indexing performance. To sum up the above innovations,a nearest neighbor search algorithm of high-dimensional data based on sequential NPsim matrix is proposed in comparison with the nearest neighbor search algorithms based on KD-tree or SR-tree on Munsell spectral data set. Experimental results show that the proposed algorithm similarity is better than that of other algorithms and searching speed is more than thousands times of others. In addition,the slow construction speed of sequential NPsim matrix can be increased by using parallel computing.

Nearest neighbor search algorithm for GBD tree spatial data structure

This paper describes the nearest neighbor (NN) search algorithm on the GBD(generalized BD) tree. The GBD tree is a spatial data structure suitable for two-or three-dimensional data and has good performance characteristics with respect to the dynamic data environment. On GIS and CAD systems, the R-tree and its successors have been used. In addition, the NN search algorithm is also proposed in an attempt to obtain good performance from the R-tree. On the other hand, the GBD tree is superior to the R-tree with respect to exact match retrieval, because the GBD tree has auxiliary data that uniquely determines the position of the object in the structure. The proposed NN search algorithm depends on the property of the GBD tree described above. The NN search algorithm on the GBD tree was studied and the performance thereof was evaluated through experiments.

An Adaptive Steganographic Algorithm for Point Geometry Based on Nearest Neighbors Search

In this study, we extend our previous adaptive steganographic algorithm to support point geometry. For the purpose of the vertex decimation process presented in the previous work, the neighboring information between points is necessary. Therefore, a nearest neighbors search scheme, considering the local complexity of the processing point, is used to determinate the neighbors for each point in a point geometry. With the constructed virtual connectivity, the secret message can be embedded successfully after the vertex decimation and data embedding processes. The experimental results show that the proposed algorithm can preserve the advantages of previous work, including higher estimation accuracy, high embedding capacity, acceptable model distortion, and robustness against similarity transformation attacks. Most importantly, this work is the first 3D steganographic algorithm for point geometry with adaptation.

基于点云的发动机叶片损伤体积测量方法

针对当前发动机叶片损伤体积计算困难、误差较大的问题,提出一种基于点云的压气机叶片的损伤体积测量方法。首先,通过结构光扫描仪获取完整点云模型和损伤点云模型,配准分割得到缺损点云。其次,缺损点云经过姿态转换后与主成分轴对比分析、分层、切片、投影得到二维点云轮廓。最后,提出单向双次最近邻点搜索算法对二维点云的轮廓进行有序提取,使用坐标解析法求解投影面的面积,累加各层面积与切片间隔的乘积得到最终的体积。试验结果表明,提出的第一主成分轴方向切片体积计算效果更好,且轮廓提取算法对比凸包提取法、双向最近邻搜索和改进最近邻搜索算法(improved nearest point search,INPS)算法更准确,效率更高,与Geomagic软件结果相比平均相对误差不超过0.3%,证明了算法的高效性和有效性。

图像特征点匹配算法下车辆行驶主动防撞预警

对于车辆行驶过程中的防撞预警,如果无法识别前车的具体行驶状态,可能使系统反应速度较慢,而不能动态变化调整本车行驶策略,导致无法有效规避潜在碰撞的危险。为了提高车辆在行驶过程中对周围环境的感知能力,防止车辆碰撞事故的发生,提出了图像特征点匹配算法下车辆行驶主动防撞预警方法。通过尺度不变特征转换(Scale-Invariant Feature Transform,SIFT)对采集到的前车图像中的特征点展开提取;利用近似最近邻搜索算法完成特征点的匹配,并将匹配点对从像素坐标系转换到图像坐标系中,以完成对前车的定位;基于单帧静态图像测距方法获得车距,并将前车的行驶状态分为静止、减速、匀速或加速三种状态,计算不同状态下的提醒报警距离和危险报警距离,动态调整本车行驶策略。当车距达到提醒报警距离或危险报警距离时,发出报警,以此实现车辆行驶过程中的主动防撞预警。试验结果表明:利用图像特征点匹配算法下车辆行驶主动防撞预警方法对前车车距展开测量,测量结果与实际车距基本一致,准确度误差在5 cm以内,相较于差异化预警方法和车联网数据预警方法而言可以更精准地测量车距;此外,所提方法的风险系数最大值为0.12,远小于差异化预警方法和车联网数据预警方法的风险系数,证实了该方法的车辆定位准确度高、防撞预警性能强。

基于时域波形特征认知的输电线路近端故障辨识与定位

针对现有单端行波故障测距对近端故障存在测距盲区、双端行波故障测距对近端故障测距误差较大,无法满足工程需要的不足,提出基于波形特征认知的近端故障辨识与定位方法。首先,分析了线路故障行波传播规律,以固定分辨率显示波形。发现线路近端故障时,初始行波及其后续波形在长时窗整体宏观观测下呈堆叠缓变特征,而在短时窗局部放大观测下呈周期性变化特征,且周期与故障距离相关。不同线路的近端故障历史样本能统一作为参照基准为测距提供提示。进而提出基于波形密度和突变分布的近端故障辨识方法。最后,对辨识出的近端故障进行周期估计,利用近端故障与线长的无关性以及历史样本突变周期和故障位置已知性,搜索周期最近邻历史样本,并由已知故障距离插值实现故障位置确定。基于大量实测数据进行仿真测试,结果表明所提方法能够显著提升单端行波测距可靠性和成功率。

求解带容量约束车辆路径问题的改进遗传算法

为解决传统遗传算法求解带容量约束的车辆路径问题时收敛速度慢和局部搜索能力差的问题,对传统遗传算法提出一种改进策略。使用基于贪婪策略的启发式交叉算子加强算法接近最优解的能力,加快算法收敛速度,在变异操作中,引入最近邻搜索算子,缩小基因变异范围,使用单点局部插入算子提高算法的局部优化能力。采用精英选择和轮盘赌法结合的选择策略,保持种群多样性以加强算法的全局搜索能力。实例计算测试表明,与传统遗传算法相比,所提算法求解平均偏差降低了70.25%,求解时间减少了87.41%;与ALNS和AGGWOA算法相比,有更高的求解质量和更好的稳定性。

基于自动终止准则改进的kd-tree粒子近邻搜索研究

对于大规模运动模拟问题而言,近邻点的搜索效率将对整体的运算效率产生显著影响。本文基于关联性分析建立kd-tree的最大深度dmax与粒子总数N的自适应关系式,提出了kd-tree自动终止准则,即ATC-kd-tree,同时还考虑了叶子节点大小阈值n_(0)对近邻搜索效率的影响。试验表明,ATC-kd-tree具有更高的近邻搜索效率,相较于不使用自动终止准则的kd-tree搜索效率最高提升46%,且适用性更强,可求解不同N值的近邻搜索问题,解决了粒子总数N发生改变时需要再次率定最大深度dmax的问题。同时,本文还提出了网格搜索法组合坐标下降法的两步参数优化算法GSCD法。通过2维阿米巴虫形状的参数优化试验发现,GSCD法可更为快速地率定ATC-kd-tree的可变参数,其优化效率比网格搜索法最高提升了205%,相较于改进网格搜索法最高提升了90%。研究结果表明,ATC-kd-tree和GSCD法不仅提高了近邻搜索的效率,也为复杂运动中近邻粒子搜索问题提供了一种更为高效的解决方案,能够显著降低计算资源的消耗,进一步提升模拟的精度和效率。

采用定权最近邻搜索的信息集译码算法

伴随式译码问题是基于编码的密码算法核心问题之一,通常用信息集译码(ISD)方式来评估这类算法,而近期信息集译码算法的进展又依赖于该算法中非常重要的步骤—最近邻技术.本文整理了信息集译码算法的发展过程,给出信息集译码算法的复杂度变化情况,分析改进的方向与方案之间的区别.总结出三个主要的改进方向,即框架、搜索方式和搜索树的深度.针对信息集译码算法中的核心内容,研究了最近邻技术的变化.在BM算法的框架基础上提出了采用定权最近邻技术且深度为6的BM-plus-depth6算法,所提算法在最坏码率情况下,全距离译码时间复杂度可以降低至2^(0.0944n),半距离译码时间复杂度可以降低至2^(0.0444n).

基于容忍因子的近似最近邻混合查询算法

近似最近邻搜索(ANNS)是计算机领域中一种重要的高效相似度搜索技术,可用于在大规模数据集中进行快速信息检索。随着人们对高精度信息检索的需求不断增长,同时使用结构化信息和非结构化信息进行混合查询的方式也得到了广泛应用。然而,基于近邻图的过滤贪心算法在混合查询时可能会因结构化约束条件的影响导致连通性降低,进而损害搜索精度。为此,提出了一种基于容忍因子的过滤贪心算法,通过容忍因子控制不满足结构化约束条件的顶点参与路由,在不改变索引结构的前提下维持原有近邻图的连通性,克服了结构化约束条件对检索精度的负面影响。实验结果证明,新算法可以在不同结构化约束强度下实现ANNS的高精度搜索,同时保持检索效率。该研究解决了基于近邻图的ANNS在混合查询场景中的问题,为大规模数据集的快速混合查询信息检索提供了一种有效的解决方案。

支持K-近邻搜索的区块链泛用型数据隐私保护方法

随着区块链泛用型数据应用场景的不断扩大,其涉及的数据隐私越来越多,数据隐私泄露可能导致个人信用受损,带来财产损失甚至身份盗用等.合理高效地进行用户身份信息及数据隐私保护是确保区块链泛用型数据安全的关键问题.为此,本文提出了支持K-近邻搜索的区块链泛用型数据隐私保护方法,采集区块链泛用型数据,利用k-prototypes算法,聚类区块链泛用型数据,并控制分类属性和数值属性.在此基础上,本文支持K-近邻搜索,建立区块链泛用型数据系统模型,确定区块链泛用型数据敏感区域,实现区块链泛用型数据隐私保护.实验结果表明,本文所提方法具有较好的区块链泛用型数据隐私保护效果,能够有效提高区块链泛用型数据隐私保护安全性,缩短区块链泛用型数据隐私保护时间.

基于分区层次图的海量高维数据学习索引构建方法

学习索引是破解海量高维数据近似最近邻搜索问题的关键。然而,现有学习索引技术结果仅局限于单个分区中,且依赖于近邻图的构建。随着数据维度和规模的增长,索引难以对分区边界数据进行精确判断,并且构建时间复杂度增大,可扩展性难以保障。针对上述问题,提出了基于分区层次图的学习索引方法PBO-HNSW。该方法对分区边界数据进行重新分配,并行构建分布式图索引结构,从而有效应对近似最近邻搜索问题所面临的挑战。实验结果表明,该方法能够在百万级海量高维数据上实现毫秒级的索引构建。当召回率为0.93时,PBO-HNSW方法构建时间仅为基线方法的36.4%。

基于视觉理论的动态点云剔除算法

针对动态场景下构建的点云地图中包含大量动态目标的错误点云问题,提出一种基于视觉理论将三维点云转换视觉图像的动态点云剔除算法。通过对当前帧和包含动态点云的噪声地图做点云的地面分割和高度分割,将点云的深度信息转换成视觉可用的图像信息,利用视觉理论中的背景差分法对当前帧和噪声地图进行深度图像对比,筛选出初始动态点云并计算动态分数;根据动态分数对初始动态点云进行自适应最近邻搜索以剔除动态目标。实验结果表明,所提算法的动态点云剔除率可达94%以上,整体得分为96.34,能有效剔除场景中的动态目标。

Efficient κ-Nearest-Neighbor Search Algorithms for Historical Moving Object Trajectories

Nearest Neighbor （κNN） search is one of the most important operations in spatial and spatio-temporal databases. Although it has received considerable attention in the database literature, there is little prior work on κNN retrieval for moving object trajectories. Motivated by this observation, this paper studies the problem of efficiently processing κNN （κ≥ 1） search on R-tree-like structures storing historical information about moving object trajectories. Two algorithms are developed based on best-first traversal paradigm, called BFPκNN and BFTκNN, which handle the κNN retrieval with respect to the static query point and the moving query trajectory, respectively. Both algorithms minimize the number of node access, that is, they perform a single access only to those qualifying nodes that may contain the final result. Aiming at saving main-memory consumption and reducing CPU cost further, several effective pruning heuristics are also presented. Extensive experiments with synthetic and real datasets confirm that the proposed algorithms in this paper outperform their competitors significantly in both efficiency and scalability.

Composite Distance Transformation for Indexing and κ-Nearest-Neighbor Searching in High-Dimensional Spaces

Due to the famous dimensionality curse problem, search in a high-dimensional space is considered as a ＂hard＂ problem. In this paper, a novel composite distance transformation method, which is called CDT, is proposed to support a fast κ-nearest-neighbor （κ-NN） search in high-dimensional spaces. In CDT, all （n） data points are first grouped into some clusters by a κ-Means clustering algorithm. Then a composite distance key of each data point is computed. Finally, these index keys of such n data points are inserted by a partition-based B^＋-tree. Thus, given a query point, its κ-NN search in high-dimensional spaces is transformed into the search in the single dimensional space with the aid of CDT index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of the proposed scheme. Our results show that this method outperforms the state-of-the-art high-dimensional search techniques, such as the X-Tree, VA-file, iDistance and NB-Tree.

Efficient Metric All-k-Nearest-Neighbor Search on Datasets Without Any Index

An all-k-nearest-neighbor （AkNN） query finds k nearest neighbors for each query object. This problem arises naturally in many areas, such as GIS （geographic information system）, multimedia retrieval, and recommender systems. To support various data types and flexible distance metrics involved in real applications, we study AkNN retrieval in metric spaces, namely, metric AkNN （MAkNN） search. Consider that the underlying indexes on the query set and the object set may not exist, which is natural in many scenarios. For example, the query set and the object set could be the results of other queries, and thus, the underlying indexes cannot be built in advance. To support MAkNN search on datasets without any underlying index, we propose an efficient disk-based algorithm, termed as Partition-Based MAkNN Algorithm （PMA）, which follows a partition-search framework and employs a series of pruning rules for accelerating the search. In addition, we extend our techniques to tackle an interesting variant of MAkNN queries, i.e., metric self-AkNN （MSAkNN） search, where the query set is identical to the object set. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness of our pruning rules and the efficiency of the proposed algorithms, compared with state-of-the-art MAkNN and MSAkNN algorithms.

基于均衡聚类索引的近似最近邻检索方法

大数据时代,深度学习通过将复杂对象表示为高维特征向量,并使用向量之间的距离度量来衡量样本的相似性,在推荐系统、用户画像、数据中台管理等场景中得到了广泛的应用.但是,随着数据规模的不断增加,海量特征数据的相似向量检索面临着检索模型占用内容大、特征检索算法召回率较低的严重挑战.如何在保证检索精度的前提下,设计紧凑型索引图结构,降低特征检索的内存消耗,对于提升大数据系统的近邻检索效率具有重要的作用.因此,本文提出了一种均衡感知的快速K均值近邻聚类的特征数据分桶及其图结构紧凑型索引用于海量数据近邻检索.首先,设计了均衡感知的快速K-均值聚类算法,通过在图索引构建过程中海量特征数据的均衡分桶,将高维向量压缩成轻量级紧凑型图索引结构,随后通过量化操作进一步压缩高维向量样本,提升其在候选集上的最近邻检索速度.在基准数据集上实验验证结果表明,本文提出的方法能够在保证较高检测召回率的同时,有效加快索引构建速度,可以用于支持高维特征数据的高效最近邻检索.

基于字典分级和属性加权的密文排序检索方案

可搜索加密支持用户在不解密原始数据的前提下对加密数据执行检索操作.现有的多关键词排序可搜索加密方案,其索引和陷门构建的时间成本通常依赖于由全局关键词字典张成的向量空间.为了减少用户端的计算开销和通信成本,进一步提升数据使用者对检索结果的满意度,提出了一种支持细粒度访问控制的多关键词密文排序检索方案.该方案首先设计基于互信息的字典剥离机制差异化全局字典中的关键词,得到两个信息量不同的附属子字典,进一步在低维子字典空间上生成索引和陷门;其次,引入文档访问策略中属性的权重,将其作为排序标准之一,使数据使用者获得更为相关的结果;最后,检索时利用筛选向量对数据进行初次过滤并借助属性匹配完成二次剔除,从而避免检索过程中不必要的计算.