Indexing the bit-code and distance for fast KNN search in high-dimensional spaces

Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (1D) transformation can break the curse of dimensionality. Based on the two techniques above, a novel high-dimensional index is proposed, called Bit-code and Distance based index (BD). BD is based on a special partitioning strategy which is optimized for high-dimensional data. By the definitions of bit code and transformation function, a high-dimensional vector can be first approximately represented and then transformed into a 1D vector, the key managed by a B+-tree. A new KNN search algorithm is also proposed that exploits the bit code and distance to prune the search space more effectively. Results of extensive experiments using both synthetic and real data demonstrated that BD out- performs the existing index structures for KNN search in high-dimensional spaces.

Novel Distance-Related Matrix Index and QSPR Research for Thermodynamics of Methyl Halides

A novel topological index W F is defined by the matrices X,W and L as W F=XWL.The topological index WF based on the distance-related matrix of molecular graphs is used to code the structural environment of each atom-type in a molecular graph.Good QSPR models have been obtained for standard formation enthalpy of methyl halides.The result indicates that the idea of using multiple matrices to define the distance-related matrix topological index is valid.

Resistance Distance and Kirchhoff Index in Corona and Edge Corona of Two Graphs

Let G_1 and G_2 be vertex-disjoint graphs with n_1 and n_2 vertices,m_1 and m_2 edges,respectively.The corona G_1° G_2 of G_1and G_2 was defined as the graph obtained by taking one copy of G_1and n_1 copies of G_2,and then joining the ith vertex of G_1 to every vertex in the ith copy of G_2.The edge corona G_1° G_2 of G_1 and G_2was defined as the graph obtained by taking one copy of G_1 and m_1copies of G_2,and then joining two end-vertices of the ith edge of G_1to every vertex in the ith copy of G_2.Kirchhoff index of G_1 ° G_2 was obtained in a much simple proof and the resistance distance of G_1 °G_2 is obtained.Furthermore,the Kirchhoff index and the resistance distance of G_1 ° G_2 were obtained.

Cycles, the Degree Distance, and the Wiener Index

The degree distance of a graph G is , where and are the degrees of vertices , and is the distance between them. The Wiener index is defined as . An elegant result (Gutman;Klein, Mihali?,, Plav?i? and Trinajsti?) is known regarding their correlation, that for a tree T with n vertices. In this note, we extend this study for more general graphs that have frequent appearances in the study of these indices. In particular, we develop a formula regarding their correlation, with an error term that is presented with explicit formula as well as sharp bounds for unicyclic graphs and cacti with given parameters.

Resistance Distance and Kirchhoff Index of a Class of Join Graphs

For some complicated graphs obtained by graph operations,it is very difficult to compute resistance distance and Kirchhoff index.Define a new graph operation,and obtain a class of new join graphs:the subdivision-vertex-vertex join G_1* G_2.Then,describe the Laplacian matrix of the graph G_1 * G_2 and use generalized inverse of the Laplacian matrix to get formulas for resistance distance and Kirchhoff index.Through the obtained formulas,the resistance distance of any pairs of vertices and Kirchhoff index of the join graph can be computed.

pgi-distance:一种高效的并行KNN-join处理方法

KNN-join是一种新近才提出的操作,它在数据挖掘中有着广泛的应用.利用KNN-join的"一次一个集合"的性质,一些数据挖掘任务,例如分类、例外挖掘和聚类等,就会更加容易地进行.MuX和Goreder则是两种专为KNN-join设计的算法.为了综合利用这两种方法的优点,一种新的KNN-join并行处理方法——pgi-distance(parallel grid index-distance)——被提了出来.pgi-distance使用双层结构,可以对I/O和CPU进行同时优化;基于距离的索引能够让它更好地适应数据维度和分布的变化.由于采用的是各DBMS厂商广泛支持的B+树索引,这让pgi-distance得以成为一种更为实用的KNN-join处理方法.在合成数据集和真实数据集上的测试也表明pgi-distance是实用的和高效的.

THE MINIMUM SQUARED EUCLIDEAN DISTANCE OF MULTI-h PHASE CODES

The minimum squared Euclidean distance(MSED) of binary multi-h phase codes is presented. The signal segregation degree(SSD) has been put forward to determine MSED of multi-h phase codes. In order to maximize MSED, SSD should be as large as possible. The necessary and sufficient conditions of maximizing SSD are derived. Finally, SSD and the exact formulae for MSED of binary 2-h phase codes are also presented.

A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm

Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.

Graph Similarity Join with K-Hop Tree Indexing

Graph similarity join has become imperative for integrating noisy and inconsistent data from multiple data sources. The edit distance is commonly used to measure the similarity between graphs. To accelerate the similarity join based on graph edit distance, in the paper, we make use of a preprocessing strategy to remove the mismatching graph pairs with significant differences. Then a novel method of building indexes for each graph is proposed by grouping the nodes which can be reached in k hops for each key node with structure conservation, which is the k-hop-tree based indexing method. Experiments on real and synthetic graph databases also confirm that our method can achieve good join quality in graph similarity join. Besides, the join process can be finished in polynomial time.

The Schultz Index and Schultz Polynomial of the Jahangir Graphs <i>J </i>_{5, <i>m </i>}

Let G be simple connected graph with the vertex and edge sets V (G) and E (G), respectively. The Schultz and Modified Schultz indices of a connected graph G are defined as and , where d (u, v) is the distance between vertices u and v?;dv is the degree of vertex v of G. In this paper, computation of the Schultz and Modified Schultz indices of the Jahangir graphs J5,m is proposed.

Reforming of Wiener Index

Based on distance matrixW, the novel topological indexW F is defined by the matricesX, W, L, asW F=WXL. WhereX is a row matrix, which expresses the bonding characteristics between adjacent atoms;L is a column matrix expressing the characteristic of vertexes in molecules;W is a distance matrix, which expresses the chemical environment of the atoms in the molecule. the properties, the chemical environments and the interaction of the vertexes in a molecule are taken into account in this definition. Hence the application of the indexW F to multiple bonds and heteroatomcontaining organic systems or inorganic systems is possible. Good correlation coefficients are achieved not only in the prediction of the standard formation enthalpy of methyl halides, titanium halides, but also the retention index of gas chromatography of the hydrocarbons. Key words distance matrix - Wiener index - QSPR - organic systems - inorganic systems CLC number O 651 Foundation item: Supported by the National Natural Science Foundation of China (20177018) and the Educational Bureau Foundation of Hubei (2003B001)Biography: Huang Yun-ping (1963-), female, Professor, research direction: biophysical chemistry

On the Degree Resistance Distance of Unicyclic Graphs

Let Gbe a connected graph with vertex set V(G). Then the degree resistance distance of Gis defined as DR(G)=∑{ u,v }⊆V(G)(d(u)+d(v))R(u,v), where d(u)is the degree of the vertex u, and R(u,v)is the degree resistance distance between uand vin graph G. A unicyclic graph is a connected graph with a unique cycle. In this paper, we characterize the unique graph with the third-maximum degree resistance distance among all unicyclic graphs with nvertices.

碳排放权交易试点对城市工业低碳转型的影响

文章基于2006—2020年中国254个城市面板数据,采用双向固定效应模型和双重机器学习模型评估碳排放权交易试点对城市工业低碳转型的影响机制。研究发现:碳排放权交易试点能够促进城市工业低碳转型,但是辐射范围存在距离衰减效应;异质性研究显示,初始排放配额采用无偿分配的试点城市拥有更好的转型效果,北京和上海碳试点具有积极辐射作用,而广东、深圳和重庆碳试点则呈现消极辐射影响;空间检验结果显示,碳排放权交易试点对其他地区工业低碳转型会产生消极的空间溢出效应;机制研究结果显示,碳排放权交易试点的运行通过拓宽碳交易覆盖面影响工业低碳转型效果,但碳交易频率的增加并不会直接影响工业低碳转型的发展。

基于氢谱修饰指数的润滑油添加剂抗磨损性能的预测研究

润滑油添加剂对保障机械运行的稳定性和安全性、延长设备的使用寿命、节约能耗具有重要作用,由于通过实验获得数据费时费力,故用定量结构摩擦性能关系研究方法,来获取抗磨损性能数据成为一种简便而高效的方法.为研究酰肼类及磷酸类润滑油添加剂抗磨损性能与结构的关系,定义并建构了新的氢谱修饰指数^(0)H,并计算36个酰肼类及磷酸类分子的电性距离矢量,筛选了M_(2)、M_(3)、M_(21)和M_(59),将这5个结构描述符作为BP神经网络的输入变量,磨损体积量度V s作为神经网络输出变量,用5-3-1的神经网络结构,构建了神经网络法预测模型,模型相关系数0.944,磨损体积量度V s的预测值与文献实验值的平均相对误差为2.61%,优于文献方法结果.结果表明:酰肼类及磷酸类润滑油添加剂抗磨损性能与氢谱修饰指数有良好的非线性关系,影响抗磨损性能的主要因素是分子中原子特性、基团片段的—CH_(3)、—CH_(2)—、>CH—、—O、—N—和—P—等.该研究对指导合成新型抗磨损性能好的化合物分子有理论意义.

舰船红外隐身性作战效能评估研究

舰船红外隐身性测试评估是研制新型水面舰船,提高其战场生存能力和与敌方作战能力的关键技术之一。舰船红外隐身性分析是通过对比舰船采用红外隐身措施前后的红外辐射量的变化情况,分析和判断隐身设计的有效性及隐身措施对红外制导导引头探测效果改变的情况。论文针对应用红外隐身技术的舰船红外隐身性作战效能评估的急需,深入分析了舰船红外辐射的机理与海洋背景下舰船红外辐射特征分布规律,通过外场测试经验及仿真计算模型推演,建立了的红外隐身性考核评估的指标模型,对舰船红外隐身效率及隐身作战效能进行了评估,验证了此红外隐身作战效能评估方法的可行性。

基于指标修正的AHP-FCEM法长输油气管道风险评价

针对国内在用长输油气管道使用管理过程中存在的高风险、事故多发、风险不可控的现状,在多方位调研的基础上,提出一种综合了层次分析法(AHP)、区间估计分析理论和模糊综合评价法(FCEM)的评价方法。利用层次分析法的优势建立初步评价体系,接着采用区间估计分析理论对“弱权重指标”进行筛选,优化形成包含5个Ⅰ级指标和25个Ⅱ级指标的最终评价体系,最后应用模糊评价法通过两级运算确定评价目标的模糊隶属度,进而得到管道的风险等级。实例验证表明,该评价方法的结果与实际情况相吻合,可为长输油气管道的科学化管理提供技术支撑。

基于极限学习机模型参数优化的光伏功率区间预测技术

提出一种基于极限学习机(ELM)模型参数优化的光伏功率区间预测技术.首先,提出加权欧氏距离作为光伏功率预测区间评估指标,筛选历史样本单元并优化ELM训练集;然后,提出ELM参数混合寻优算法,利用精英保留策略遗传算法与分位数回归优化ELM模型隐层输入及输出权重与偏置参数,并采用训练后的模型预测光伏功率区间;最后,基于光伏电站与气象站历史数据构建实际算例,预测光伏功率区间,并与其他方法得到的结果进行对比.算例结果表明:所提方法在增加区间预测可信度的同时,能较大程度提高区间预测准确度.

基于轨道间最小距离的空间环境指数

空间环境指数是描述空间碎片对空间环境长期影响的量化评估指数。用该指数可以对比分析某空间物体的在轨运行是否对其他物体乃至整体环境产生较大威胁。对现有典型空间碎片环境指数及其建模方法进行分析和比较,并针对过去模型中平均碰撞风险计算方法的不足,提出一种空间环境指数模型,即基于轨道间最小距离(minimum orbital intersection distance,MOID)的空间环境指数(MOID-based space index,MBSI)。该指数综合空间碎片的质量、有效截面积等因素,基于MOID考虑碰撞风险,计算近地轨道(low Earth orbit,LEO)区域中不同空间物体的MBSI指数,并与已有的R_(N)指数、CSI(criticality of spacecraft index,CSI)指数的结果进行比对和分析。MBSI与R_(N)指数、CSI指数符合度超过60%,而MBSI更能体现空间物体寿命期内的危险程度。

密度、距离及分割视角下长三角区域城市韧性逻辑建构与实证分析

风险影响的跨区域性凸显了城市网络在增强城市韧性中的重要性。立足新经济地理学理论中密度-距离-分割三大要素(3D要素),从区域及城区层级上构建包含生态密度、空间距离及社会分割指标的城市韧性综合指数模型,以长三角41市为例,运用空间计量方法研究韧性指数特征、均衡发展状态及3D要素对韧性水平的影响效应。结果显示:①区域中城市韧性水平总体偏低,在空间上呈现以“苏锡常”为核心向外梯度递减的圈层式+轴线型韧性发展格局;韧性等级与区位条件、规划衔接以及内外均衡发展状态高度相关。②随着城市韧性水平的提升,城区层级上的抵御恢复力及区域层级上的适应转型力均衡趋势增强。高水平韧性城市中两者趋向均衡发展,中低水平韧性城市中两者存在失衡;③不同层级的3D要素对韧性水平的影响存在差异性,区域层级上的主导要素为距离,而城区层级上则为密度,二者交互后的影响更加显著。

基于多指标成分定量、化学计量学、EW-TOPSIS分析的白术厚朴合剂质量评价研究

目的以10种成分为指标,建立白术厚朴合剂HPLC定量分析方法,联合化学计量学及熵权优劣解距离法(EW-TOPSIS)对其质量进行综合评价。方法以75%甲醇提取液为样品,采用Agilent TC-C18色谱柱,乙腈-0.1%磷酸为流动相梯度洗脱,HPLC法同时测定12批白术厚朴合剂中10个化合物(白术内酯Ⅲ、白术内酯Ⅰ、肉桂醇、肉桂酸、桂皮醛、和厚朴酚、厚朴酚、6-姜辣素、8-姜酚和10-姜酚)含量,运用化学计量学和EW-TOPSIS法对含量数据进行分析。结果经方法学验证,各指标均符合《中华人民共和国药典》要求:10种成分在各自质量浓度范围内线性关系良好,相关系数>0.999;精密度、稳定性、重复性、加样回收率RSD均<2.0%。化学计量学分析显示,前2个主成分代表10个成分92.24%的信息,12批样品聚为3类,桂皮醛、厚朴酚、和厚朴酚、白术内酯Ⅲ、6-姜辣素和白术内酯Ⅰ对白术厚朴合剂的质量影响较大。EW-TOPSIS法结果显示,12批白术厚朴合剂的最优解欧氏贴近度均值为0.2385~0.6662,各批次间质量差异较大。结论建立的多指标成分定量方法操作便捷、结果准确,可用于白术厚朴合剂的质量控制,化学计量学及EW-TOPSIS分析可实现对白术厚朴合剂质量的综合评价,为白术厚朴合剂质量标准提升提供参考。