Patch-based vehicle logo detection with patch intensity and weight matrix

A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to bad and complex environmental conditions.The bounding-box of the logo is extracted by a thershloding approach.Experimental results show that 93.58% location accuracy is achieved with 1100 images under various environmental conditions,indicating that the proposed method is effective and suitable for the location of vehicle logo in practical applications.

Spatial weight matrix in dimensionality reduction reconstruction for microelectromechanical system-based photoacoustic microscopy

A micro-electromechanical system(MEMS)scanning mirror accelerates the raster scanning of optical-resolution photoacoustic microscopy(OR-PAM).However,the nonlinear tilt angular-voltage characteristic of a MEMS mirror introduces distortion into the maximum back-projection image.Moreover,the size of the airy disk,ultrasonic sensor properties,and thermal effects decrease the resolution.Thus,in this study,we proposed a spatial weight matrix(SWM)with a dimensionality reduction for image reconstruction.The three-layer SWM contains the invariable information of the system,which includes a spatial dependent distortion correction and 3D deconvolution.We employed an ordinal-valued Markov random field and the Harris Stephen algorithm,as well as a modified delay-and-sum method during a time reversal.The results from the experiments and a quantitative analysis demonstrate that images can be effectively reconstructed using an SWM;this is also true for severely distorted images.The index of the mutual information between the reference images and registered images was 70.33 times higher than the initial index,on average.Moreover,the peak signal-to-noise ratio was increased by 17.08%after 3D deconvolution.This accomplishment offers a practical approach to image reconstruction and a promising method to achieve a real-time distortion correction for MEMS-based OR-PAM.

Drawing Weighted Directed Graph from It's Adjacency Matrix

This paper proposes an algorithm for building weighted directed graph, defmes the weighted directed relationship matrix of the graph, and describes algorithm implementation using this matrix. Based on this algorithm, an effective way for building and drawing weighted directed graphs is presented, forming a foundation for visual implementation of the algorithm in the graph theory.

Optimal strategy of searching FPD weights scanning matrix using GA-PSO

This paper discusses a kind of optimal method used for searching flat panel display （FPD） scanning matrix. The method adopts bionic algorithm： genetic algorithm （GA） and particle swarm optimization （PSO） algorithm. The method using single GA is more time-consuming, and the search efficiency is low in later evolution; the PSO algorithm is easily falling into the local optimal solution and appears the premature convergent phenomenon. Hence, a hybrid approach of GAPSO is found to optimize the search for high grayscale weights scanning matrix. Finally in the acceptable time, it finds a weight scanning matrix （WSM） of 256 gray scales with Matlab, whose scanning efficiency reaches 94.73% and the linearity is very good.

Research on consistency measurement and weight estimation approach of hybrid uncertain comparison matrix

The consistency measurement and weight estimation approach of the hybrid uncertain comparison matrix in the analytic hierarchy process （AHP） are studied. First, the decision-making satisfaction membership function is defined based on the decision making＇s allowable error. Then, the weight model based on the maximal satisfactory consistency idea is suggested, and the consistency index is put forward. Moreover, the weight distributing value model is developed to solve the decision making misleading problem since the multioptimization solutions in the former model. Finally, the weights are ranked based on the possibility degree approach to obtain the ultimate order.

Wide-Area Delay-Dependent Adaptive Supervisory Control of Multi-machine Power System Based on Improve Free Weighting Matrix Approach

The paper demonstrates the possibility to enhance the damping of inter-area oscillations using Wide Area Measurement (WAM) based adaptive supervisory controller (ASC) which considers the wide-area signal transmission delays. The paper uses an LMI-based iterative nonlinear optimization algorithm to establish a method of designing state-feedback controllers for power systems with a time-varying delay. This method is based on the delay-dependent stabilization conditions obtained by the improved free weighting matrix (IFWM) approach. In the stabilization conditions, the upper bound of feedback signal’s transmission delays is taken into consideration. Combining theoriesof state feedback control and state observer, the ASC is designed and time-delay output feedback robust controller is realized for power system. The ASC uses the input information from Phase Measurement Units (PMUs) in the system and dispatches supplementary control signals to the available local controllers. The design of the ASC is explained in detail and its performance validated by time domain simulations on a New England test power system (NETPS).

A Note on the Spectral Radius of Weighted Signless Laplacian Matrix

A weighted graph is a graph that has a numeric label associated with each edge, called the weight of edge. In many applications, the edge weights are usually represented by nonnegative integers or square matrices. The weighted signless Laplacian matrix of a weighted graph is defined as the sum of adjacency matrix and degree matrix of same weighted graph. In this paper, a brief overview of the notation and concepts of weighted graphs that will be used throughout this study is given. In Section 2, the weighted signless Laplacian matrix of simple connected weighted graphs is considered, some upper bounds for the spectral radius of the weighted signless Laplacian matrix are obtained and some results on weighted and unweighted graphs are found.

基于Matrix Profile的时间序列分割技术改进

时间序列分割是数据挖掘领域中的一个重要研究方向.目前基于矩阵轮廓(matrix profile,MP)的时间序列分割技术得到了越来越多研究人员的关注,并且取得了不错的研究成果.不过该技术及其衍生算法仍然存在不足:首先,基于矩阵轮廓的快速低代价语义分割算法中对给定活动状态的时间序列分割时,最近邻之间通过弧进行连接,会出现弧跨越非目标活动状态匹配相似子序列问题;其次,现有提取分割点算法在提取分割点时采用给定长度窗口,容易得到与真实值偏差较大的分割点,降低准确性.针对以上问题,提出一种限制弧跨越的时间序列分割算法(limit arc curve cross-FLOSS,LAC-FLOSS),该算法给弧添加权重,形成一种带权弧,并通过设置匹配距离阈值解决弧的跨状态子序列误匹配问题.此外,提出一种改进的提取分割点算法(improved extract regimes,IER),它通过纠正弧跨越(corrected arc crossings,CAC)序列的形状特性,从波谷中提取极值,避免直接使用窗口在非拐点处取到分割点的问题.在公开数据集datasets_seg和MobiAct上面进行对比实验,验证以上两种解决方案的可行性和有效性.

Phosphorylation and biosynthesis of high molecular weight proteins of tumor nuclear matrix

Our previous studies showed a predominance of high molecular weight protein group in tumor nuclear matrices. contrary to normal cells, Proteins of this group are Preferentially phosphorylated. Phosphoproteins of hepatoma nuclear matrix are selectively subjected to rapid proteolysis. By alkali treatment and a monoclonal antibody against phosphotyrosyl residue the presence of two high molecular weight bands of phosphotyrosyl-containing proteins was defected in nuclear matrices of tumor but not of normal liver cells. High molecular weight protein group of tumor nuclear matrices revealed also a rapid turnover and preferential incorporation of labeled amino acids selectively inhibited by chioramphenicol.

关于赋权非正则图的A_(α)特征值和特征向量

设G_(ω)=(G,ω)是一个赋权图,其邻接矩阵和赋权度对角矩阵分别A(G_(ω))和D(G_(ω))。对于α∈[0,1],G_(ω)的A_(α)-矩阵为A_(α)(G_(ω))=αD(G_(ω))+(1-α)A(G_(ω))。对于连通赋权非正则图G_(ω),给出了其关于A_(α)-特征值的一些界,并得到了A_(α)-谱半径对应的特征向量中最大分量与最小分量比值的下界。

基于现场可编程门阵列的矩阵求逆算法设计

由于自适应抗干扰算法在更新最优权值时存在时间延时,导致很难满足动态环境下的权值更新率要求。针对该情况已有学者对如何实现快速采样矩阵求逆算法进行研究,但仍存在只适用于低维矩阵,且权值更新率慢的问题。为解决上述问题,提出了一种基于Cholesky分解的采样矩阵求逆算法实现架构。该实现架构主要包括协方差矩阵计算模块、Cholesky分解模块、计算下三角矩阵L的逆矩阵模块、三角矩阵相乘和权值计算模块。设计采用流水线加状态机实现结构有效地解决了因高阶采样矩阵求逆运算量大产生的权值更新率慢的问题。仿真结果表明,在现场可编程门阵列(field programmable gate array,FPGA)的硬件平台上,对于56阶采样矩阵,在100 MHz工作频率下,一次权值的更新时间仅需要1.2 ms。本文所提的实现架构为自适应抗干扰快速求解权值提供了一种切实可行的解决方案,对存在类似需求的权值求解系统具有一定的参考价值。

空间计量经济学前沿理论、方法与应用研究综述

数字经济时代区域要素流动愈发明显,经济活动的空间组织和经济地理正发生着剧烈的、持续的和趋势性的变化,对空间经济理论与实证研究提出了重大需求,在此背景下空间计量经济学模型的应用价值也越来越为重要。为深化空间计量经济学模型的理论认识,促进其在经济管理学领域实证研究中的规范应用,文章在充分收集文献资料的基础上,着眼于空间计量经济学前沿理论、方法与应用,从空间计量经济学理论及技术、模型体系及拓展、国内应用现状、不足及反思等方面进行了较为系统的文献分析与总结,并展望了其发展前景和方向。未来国内空间计量经济学应在技术理论研究、模型应用体系,及方向上进行不断推进,以提升空间计量经济学理论与应用研究水平。

基于一致性的赋权方法在课程过程性评价中的应用

针对课程平时成绩的权重比例,构建采用客观赋权法确定权重的动态代数模型.基于平时成绩的各项得分,分析项目之间的一致度,应用矩阵理论对权重进行一致性检验,给出一致性最强的权重比例.结合实例计算最优权重.

饮用水中高分子量消毒副产物的检测识别方法

消毒是饮用水处理过程中的重要步骤,但普遍采用的氯系消毒剂在杀灭细菌病毒的同时,能产生大量具有“三致”效应的卤代消毒副产物(DBPs),严重威胁了饮用水的化学安全.研究人员已在饮用水中陆续检测并识别出700多种DBPs,但这些已知DBPs均为分子量小于800 Da的低分子量DBPs,目前对高分子量DBPs的认识还较为有限.本研究建立了基于基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)的高分子量DBPs检测方法,发现在基质为2,5-二羟基苯甲酸,阳离子化试剂为三氟乙酸钠,使用三明治法点靶,反射-正离子模式,90%激光强度时,信噪比和信号重现性达到最优,信噪比之和达到了136.2,变异系数(CV)则为4.77%.利用上述方法,在模拟饮用水中检测到5种新的高分子量DBPs.在此基础上,通过同位素模式分析、TOF/TOF串联质谱和数据库验证,建立了针对未知高分子量DBPs的分子式/结构式识别方法,确定新的高分子量DBPs为寡糖羧酸类物质.

基于Weighted-slope One的用户聚类推荐算法研究

针对传统协同过滤推荐算法存在的数据稀疏性以及实时性差的问题,提出一种基于Weighted-slope One的用户聚类推荐算法。该算法首先利用Weighted-slope One算法的思想对初始的用户-评分矩阵进行有效填充,降低数据的稀疏性;然后,结合初始聚类中心优化改进的K-means方法对用户进行聚类,生成相似用户集合,以缩小目标用户搜索最近邻的范围;最后,结合目标用户所属的聚类,利用基于用户的协同过滤算法搜索最近邻居,为目标用户推荐对应的产品。仿真实验结果表明,改进算法可以显著降低数据的稀疏度,同时提升推荐的准确性和实时性。

结合注意力机制的多重引导点云配准网络

针对点云配准过程中仅仅利用点云特征寻求对应关系使得离群点多、配准精度不高的问题进行研究,提出了一种使用点云之间匹配点概率矩阵和点云空间信息特征矩阵共同搜寻对应关系,并且相互配合确定对应点权重的点云配准网络——AMGNet。首先使用点云特征提取网络获得两片待配准点云的高维特征;然后采用Transformer对独立特征进行上下文信息融合,之后利用关键点提取模块选取出特征更强的点,使用SoftBBS方法获得点云匹配点概率矩阵后,结合点云空间特征矩阵搜索到最终的对应关系,同时,权重分配也使用了双重矩阵共同决定的策略;最后使用奇异值分解获得需要的刚性变换矩阵。在ModelNet40,7Scenes等人工合成数据集和真实场景数据集上进行了多次实验。结果表明,在ModelNet40目标未知实验中的旋转矩阵和平移向量的均方误差分别降低至0.025和0.0046。AMGNet配准精度较高,抗干扰能力强,泛化能力强。

溴化镧探测器γ射线能量展宽及解谱研究

溴化镧(LaBr_(3):Ce)探测器是一种性能优良的无机闪烁体探测器,准确的LaBr_(3):Ce探测器γ响应参数以及响应能谱是开展应用工作的基础。通过对实验中实测γ能谱的分析,可以得到溴化镧谱仪的半高宽-能量关系,在尝试使用MCNP中GEB卡还原此关系时,发现可将程序中自带的半高宽-能量函数简化,简化后的函数关系仍能较好地反映实验测量信息。在此基础上,采用MCNP程序模拟了0.1~5 MeV的单能γ射线在Φ50 mm×10 mm溴化镧探测器中的响应输出,构建了其γ响应函数矩阵。通过能谱计算得到展宽系数后,编写MATLAB计算程序对构建的响应矩阵进行高斯展宽。利用展宽后的矩阵通过加权最小二乘法对实验测得的复杂能谱进行解谱,并将解谱结果与实验测量数据进行比较验证。结果表明:展宽后的响应函数与实验得到的响应函数符合一致,通过数值解谱可以很好地给出实验测量中不同γ源的能量以及相对产额等信息。

空间权重选择的模拟研究——基于贝叶斯方法

文章在空间杜宾模型下讨论贝叶斯后验模型概率方法对空间权重矩阵的选择效果,并利用蒙特卡洛模拟验证了该方法的有效性。结果表明:贝叶斯后验模型概率方法对权重判别的准确性较高,且随样本量和时间的增加而提高,正确判别率与空间相关系数的关系呈现“U”型特征;在对具有较强相关性的权重选择中,其判别能力下降,但效果仍优于ML法;在对有不同属性的静态空间权重的判别中,贝叶斯后验模型概率可快速达到100%;对于动态和静态空间权重的选择问题,该方法在存在较大正向空间相关性的情况下具有良好功效。

多重关联下中国省际政府债务风险传染研究

经济发展压力下的地方政府财政缺口和资金需求与日俱增,债务规模不断扩大,随之而来的债务风险问题引起了社会各界的广泛关注。利用2010-2020年中国省级面板数据,基于地理、财政和经济社会关联构建具有不同空间权重矩阵凸组合的动态空间面板模型,在多重关联视角下探究了中国省际政府债务风险的空间传染效应及其效应分解,进一步分析了债务风险传染效应的影响因素及其经济后果。通过构建多个空间权重矩阵的凸组合,可以更全面和准确地刻画地方政府之间可能存在的多重空间互动关系,并在统一研究框架下评估不同风险传染网络的相对重要性。研究发现:中国省际政府债务风险在基于地理、财政和经济社会关联的三类风险传染网络中均存在显著的空间传染效应,且基于财政关联的风险传染网络最为重要;基于最优加权空间权重矩阵进行效应分解,发现多数情况下长期效应明显大于短期效应,直接效应比间接效应更为显著,各省份所接收和输出的债务风险存在显著的非对称性。在此基础上,进一步研究发现,经济越繁荣、地方政府间引资竞争越激烈,中国省际政府债务风险传染效应越弱;其他省份的债务风险会对本省经济发展产生显著的抑制作用。最后,从多角度对防范化解地方政府债务风险和实施科学有效的管控政策提供了针对性建议。

模糊层次分析法的WEB开发课程实践教学评价

现有的实践课程教学效果差,学生实践能力不佳,为此,文中提出了基于模糊层次分析法的WEB开发课程实践教学评价方法。通过模糊层次分析方法获取评价判断矩阵,计算不同层次因素中依据评价专家设定评价指标,计算隶属度权重值。根据判断矩阵构建隶属度函数,建立评价集合的关系矩阵,得到评价矩阵特征值,计算单因素评价结果。通过实例验证WEB开发课程实践教学评价方法的有效性,对三个不同层次的权重值进行量化分析,由加权综合得分可知,文中方法的最终评价分数为72.5,该结果表明其教学评价方法具有一定的实用性。