P2P检索系统安全研究:基于聚类域的三层体系架构

对等计算作为学术界和工业界的关注焦点,其在实践中的重要应用是信息资源共享,但随之而来的是如何在P2P环境中进行资源查找,实现高效而安全的信息检索问题。该文在分析对等计算体系结构现状之后,提出P2P信息检索系统的新型安全体系架构——基于聚类域的三层体系结构。该结构通过资源描述框架(Resource Description Framework)的元数据描述,将资源节点进行聚类,进而组成聚类域(Clustering Domain),并以此为前提和基础构建从上而下依次为应用层、结构化层和非结构化层的三层体系结构。该结构最大限度地发挥出各层结构的高效、安全优点,并相应控制了局限性和不足。

宽域灰色聚类法在土壤环境质量评价中的应用

利用宽域灰色聚类法评价土壤环境质量,先构造白化函数,引入修正系数,确定污染物权重,再计算聚类系数实现土壤样本的环境质量等级评判与排序。将此方法应用于太原市5个区县,8项指标的土壤质量评价,并与综合指数法、模糊综合评判法相比较,结果表明模糊综合评判法与宽域灰色聚类法对于5个区县的污染级别的评价结果是一致的,而综合指数法对前四个评价点级别均比上述两种方法低一个级别,三种方法的质量排序大致相同,只有模糊综合评价中认为小店区优于万柏林区。

宽域灰色聚类法在减量化用地复垦场地土壤环境质量评价中的应用

利用宽域灰色聚类法,通过构造白化函数、确定污染物权重、引入修正系数、计算聚类系数、归纳聚类结果等步骤,对不同用地历史的10个减量化用地复垦场地的7项重金属指标进行土壤环境质量评价。结果表明,宽域灰色聚类法的聚类结果以及由此进行的土壤环境质量排序与对应场地的历史用地状况关联。宽域灰色聚类法可以用作管控计划内农用地的土壤环境风险、保障农产品安全的一种评价手段。

基于空间域模糊聚类与CV模型的医学图像分割

针对CV模型分割图像时存在的分割精度低及对初始轮廓敏感等问题,提出一种CV模型结合空间域模糊C均值聚类(Spatial fuzzy C-means,SFCM)的图像分割算法(SFCM-CV),用于边界不清晰、存在伪影且含有高噪声的MRI及CT图像分割。在利用空间域模糊C均值聚类算法对图像进行粗分割的基础上,用聚类信息来辅助CV模型设定初始轮廓,迭代演化分割出目标区域。实验结果表明,当迭代次数仅为50时,SFCM-CV算法分割人脑MRI图像的Dice系数为89.17%,比传统CV模型提高了38.9%。可知该算法对医学图像的区分度更高、分割效果更好。

基于激光雷达点云的煤炭快速装车质量检测方法

为了提高煤炭快速定量装车系统智能化无人化水平,实现火车装车质量快速实时检测,避免火车超偏载情况的发生,针对现有非接触式装车质量检测系统的不足,提出了基于激光雷达点云的煤炭快速装车超偏载检测方法。结合煤炭火车装车站装载流程,利用激光雷达三维扫描技术和车号识别技术,构建火车装车质量检测系统。提出一种基于马氏距离的离群点滤波算法,对每个点的邻域进行统计分析,计算其到邻近点的马氏距离,去除火车车厢点云中存在大量的随机噪声,如装车时的粉尘、喷洒的水雾、外界环境的扰动(雨、雪、煤尘)等。提出了一种标签连通域聚类算法,通过点云连通域区域标记和聚类,完成相邻两节车厢之间的分割。提出一种基于主成份分析(PCA)的火车车厢拼接算法,完成车厢点云的三维点云拼接。提出一种基于点云切片的装载物料点云提取方法,通过构建点云局部领域提高计算速度,在车厢长度和宽度方向上分别进行切片,滤除车厢前后、左右车帮点云。最后,提出了火车装车质量主要指标装载高度、装煤量、偏载量的计算方法,通过表面三维重建直观显示检测结果,实现火车装车质量的检测。试验表明,利用基于激光雷达点云的检测方法可以完成火车装车表面的实时扫描建模和装车质量检测,且该方法适用于不同尺寸车型,具有通用性。

与通信兼容的毫米波室内定位中角功率谱视距传输簇的识别方法

该文提出一种在室内无线定位中识别毫米波视距传输(LoS)的方法,在室内毫米波无线通信的波束训练框架下,实现了信道角功率谱(PAS)中视距角度簇的识别。该方法通过分水岭算法,将角功率谱聚类成簇,进而采用最大似然比和人工神经网络分析角度簇内5个不同信道指标,即空间对称性、冲激响应和传递函数的信号峰度、平均过度延迟和均方根(RMS)延迟扩展。仿真结果中可观察到视距和非视距(NLOS)角功率谱簇之间差异明显,并以实验验证了该识别方法的有效性。

一种新型的P2P信息检索系统体系结构

本文在分析对等计算体系结构现状之后,提出了一种的新型P2P信息检索系统体系架构——基于聚类域的三层体系结构。该结构通过资源描述框架的元数据描述,将资源节点进行聚类,进而组成聚类域,并以此为前提和基础构建从上而下依次为应用层、结构化层和非结构化层的三层体系结构。该结构最大限度地发挥出各层结构的高效、安全优点,并相应控制了局限性和不足。

青海可可西里地区湖泊水系环境水化学

在综合科学考察的基础上,经过大量的水质化学数据的测试,阐明了可可西里地区湖泊的水化学基本特征。包括分析了流域盆地内天然降水、水系及湖泊的水质展布趋向,运用离子域随机图式聚类阐明了湖水物质来源条件,还对典型湖泊的水温、分异、相变等进行了介绍和讨论;采用难溶盐法对本区水体进行水化学分类,阐明了湖泊水化学类型的地域性及其与区域地质环境条件的关系。

A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique

Determination of homogenous precipitation-based regions is a very important task in effective management of water resources. The present study tried to propose an effective precipitation-based regionalization methodology by conjugating both temporal pre-processing and spatial clustering approaches in a way to take advantage of multiscale properties of precipitation time series. Annual precipitation data of 51 years(1960-2010) for 31 rain gauges(RGs) were collected and used in proposed clustering approaches. Discreet wavelet transform(DWT) was used to capture the time-frequency attributes of the time series and multiscale regionalization was performed by using k-means and Self Organizing Maps(SOM) clustering techniques. Daubechies function(db) was selected as mother wavelet to decompose the precipitation time series. Also, proper boundary extensions and decomposition level were applied. Different combinations of the approximation(A) and detail(D) coefficients were used to determine the input dataset as a basis of spatial clustering. The proposed model's efficiency in spatial clustering stage was verified using three different indexes namely, Silhouette Coefficient(SC), Dunn index and Davis Bouldin index(DB). Results approved superior performance of k-means technique in comparison to SOM. It was also deduced that DWT-based regionalization methodology showed improvements in comparison to historical-based models. Cross mutual information was used to investigate the RGs of cluster 3's homogeneousness in DWT-k-means approach. Results of non-linear correlation approach verified homogeneity of cluster 3. Verifications based on mean annual precipitation values of rain gauges in each cluster also approved the capability of multiscale approach in precipitation regionalization.

Characteristics of Clustering Debris Flows in Wenchuan Earthquake Zone

Clustering debris-flow events, namely many debris flows simultaneously triggered by a regional rainstorm in a large-scale mountainous area,occurred in four regions of Wenchuan earthquake stricken areas in 2008 and 2010. The characteristics of the clustering debris flows are examined with regard to triggering rainfall, formation process, and relationship with the earthquake by field survey and remote sensing interpretation. It is found that the clustering events occurred nearly at the same time with the local peak rainstorms, and the rainfall intensity-duration bottom limit line for clustering debris flows is higher than the worldwide line. It means that more rainfall is needed for the occurrence of the clustering debris flows. Four kinds of major formation processes for these debris flows are summarized: tributary-dominated, mainstreamdominated, transformation from slope failures, and mobilization or liquefaction of landslide. The four regions has a spatial correlation with the strongquake-influenced zone with the peak ground acceleration = 0.2 g and the seismic intensity > X.

Application of different clustering approaches to hydroclimatological catchment regionalization in mountainous regions, a case study in Utah State

With respect to the different hydrological responses of catchments, even the adjacent ones, in mountainous regions, there are a great number of motivations for classifying them into homogeneous clusters. These motivations include prediction in ungauged basins(PUB), model parameterization, understanding the potential impact of environmental changes, transferring information from gauged catchments to the ungauged ones. The present study investigated the similarity of catchments through the hydro-climatological pure time-series of a 14-year period from 2001 to 2015. Data sets encompass more than 13,000 month-station streamflow, rainfall, and temperature data obtained from 27 catchments in Utah State as one of the eight mountainous states of the USA. The identification, analysis, and interpretation of homogeneous catchments were investigated by applying the four approaches ofclustering, K-means, Ward, and SOM(Self-Organized Map) and a newly proposed Wavelet-Entropy-based(WE-SOM) clustering method. By using two clustering evaluation criteria, 3, 5, and 6 clusters were determined as the best numbers of clusters, depending on the method employed, where each cluster represents different hydro-climatological behaviors. Despite the absence of geographic characteristics in input data matrix, the results indicated a regionalization in agreement with topographic characteristics. Considering the dependency of the hydrological behavior of catchments on the physiographic field aspects and characteristics, WE-SOM method demonstrated a more acceptable performance, compared to the other three conventional clustering methods, by providing more clusters. WE-SOM appears to be a promising approach in catchment clustering. It preserves the topological structure of data which can, as a result, be proofed in a greater number of clusters by dividing data into higher numbers of distinct clusters withsimilar altitudes of catchments in each cluster. The results showed the aptitude of wavelets to quantify the time-based variability of temperature, rainfall and streamflow, in the way contributing to the regionalization of diverse catchments.

Terahertz spectrum clustering of traditional Chinese medicine based on first derivative characteristics

In order to deal with the unclear absorption peak caused by the absorption peak overlap of traditional Chinese medicine(TCM)and other mixtures,a method of three unsupervised clustering algorithms as K-means,K-medoids and Fuzzy C-means(FCM)combined with the first derivative characteristics of terahertz absorption spectrum,is proposed to perform the terahertz spectra clustering of Sanchi and other three kinds of TCM compared with their easily-confused products(ECPs).These three unsupervised clustering methods complement the scope of the supervised learning classification method.The first derivative of the spectrum could amplify the difference in the absorption coefficient with different substances,so that the obvious absorption peak can be revealed.Experiments shows that these three clustering algorithms can achieve good results by combining the origin absorption coefficient with its first-order derivative as the characteristic data,and among which K-means does the best with the accuracy of95.32%.Compared with pure absorption coefficient data clustering,the accuracy in this study has been significantly improved,especially for the non-absorption-peak TCM classification.And the accuracy of K-means algorithm is improved by5.38%.Besides,clustering algorithms in this study have strong anti-interference ability to the error data.

Thymoquinone and Poloxin are slow-irreversible inhibitors to human Polo-like kinase 1 Polo-box domain

Objective： To provide a kinetic model（s） and reveal the mechanism of thymoquinone and Poloxin blocking an emerging anti-cancer target, human Polo-like kinase 1 （hPlkl） Polo-box domain （PBD）. Methods： The binding kinetics was determined by using a fluorescence polarization based assay. The putative mechanism was examined with a competition test. Results： Thymoquinone follows a one-step binding with an association rate constant （k1） of 6.635× 10^3 L.mol^-1 min^-1.Poloxin fit a two-step binding with a dissociation constant （Ki） of 118 μmol/L for the intermediate complex and its isomerization rate （k4） of 0.131 5 minJ to form an irreversible adduct. No significant dissociation was observed for either ligand up to 13 h. The inhibitors responded insignificantly to the presence of Michael donors as hPIkl-PBD competitors. Conclusion： Thymoquinone and Poloxin are slow-tight ligands to the hPlkl-PBD with kinetic models distinct from each other. Michael addition as the mechanism is excluded.

NASSN:a NAS-based storage network

With the digital information and application requirement on the Internet increasing fleetly nowadays,it is urgent to work out a network storage system with a large capacity,a high availability and scalability.To solve the above-mentioned issues,a NAS-based storage network(for short NASSN)has been designed.Firstly,the NASSN integrates multi-NAS,iNAS(an iSCSI-based NAS)and enterprise SAN with the help of storage virtualization,which can provide a greater capacity and better scalability.Secondly,the NASSN can provide high availability with the help of server and storage subsystem redundancy technologies.Thirdly,the NASSN simultaneously serves for both the file I/O and the block I/O with the help of an iSCSI module,which has the advantages of NAS and SAN.Finally,the NASSN can provide higher I/O speed by a high network-attached channel which implements the direct data transfer between the storage device and client.In the experiments,the NASSN has ultra-high-throughput for both of the file I/O requests and the block I/O requests.

Analysis on Regional Difference of Development of Elderly Care Real Estate in Gannan Area

The continuous expansion of the scale of the elderly population and the continuous acceleration of elderly population ageing have had a profound impact on China＇s economy, society and family, especially for the development of China＇s pension indusby, in this paper, taking Ganzhou City as an example, the population size, population ageing rate, urbanization rate and residential area were analyzed using Z-Scores standardized data processing model from three aspects： the population development level, the real estate market maturity degree and the service development level. The proportion of the tertiary industry and other indicators were used to explore the suitability of the development of eldedy care real estate, and cluster analysis was used to classify the sample areas according to the suitability. The results show that the 18 counties and cities in Ganzhou City could be divided into three categories, areas with poor suitability, areas with development potential, key development areas. Finally, suggestions were put forward that the target market of the development of old-age real estate could be comprehensively analyzed by combining the population development trend of the region with its thoughts and concepts, social and economic development, the maturity of the real estate market and the development of the service industry to ensure the sustainable operation of the old-age real estate.

A Cluster-Based Method for Marine Sensitive Object Extraction and Representation

Within the context of global change, marine sensitive factors or Marine Essential Climate Variables have been defined by many projects, and their sensitive spatial regions and time phases play significant roles in regional sea-air interactions and better understanding of their dynamic process. In this paper, we propose a cluster-based method for marine sensitive region extraction and representation. This method includes a kernel expansion algorithm for extracting marine sensitive regions, and a field-object triple form, integration of object-oriented and field-based model, for representing marine sensitive objects. Firstly, this method recognizes ENSO-related spatial patterns using empirical orthogonal decomposition of long term marine sensitive factors and correlation analysis with multiple ENSO index. The cluster kernel, defined by statistics of spatial patterns, is initialized to carry out spatial expansion and cluster mergence with spatial neighborhoods recursively, then all the related lattices with similar behavior are merged into marine sensitive regions. After this, the Field-object triple form of < O, A, F > is used to represent the marine sensitive objects, both with the discrete object with a precise extend and boundary, and the continuous field with variations dependent on spatial locations. Finally, the marine sensitive objects about sea surface temperature are extracted, represented and analyzed as a case of study, which proves the effectiveness and the efficiency of the proposed method.

Distribution and Provenance of Detrital Minerals in Southern Coast of Shandong Peninsula

Detrital minerals of 137 offshore and 22 river sediment samples collected from Qingdao coastal areas have been analyzed. Four mineral assemblage provinces can be classified by Q-mode cluster analysis. Factor analysis identifies two major factors that account for the total variability in most common minerals: 1) based on the relationship of quartz, hornblende, actinolite, micas, and authigenic pyrite, 41.55% of the variability is related to sediment sources; 2) based on the relationship of epidote, garnet, sphere, and ilmenite, 23.21% can be related to strong hydrodynamic conditions that control transport and sedimentation. By comparing mineral compositions of river waters in the study area, the following four mineral provenances can be identified. The Qingdao-Laoshan nearshore area has a quartz-feldspar-epidote-hornblende-limenite-limonite-sphene assemblage, which is largely attributed to relict sediment and coastal erosion. The Jimo-Haiyang nearshore area has a quartz-feldspar-hornblende-epidote-limonite-mica-actinolite assemblage, derived largely from the Wulong River and Rushan River, and is also affected by the Huanghe River, while the Qianliyan Island area in the deeper offshore area separated by a mud belt has a similar assemblage. The Haiyang-Rushan nearshore area has a quartz-feldspar-hornblende-epidote-micas-limonite assemblage, indicating multiple sources from the Rushan River, the Wulong River, the Huanghe River, and coastal erosion. The central area, located in an eddy center, has a mica-authigenic pyrite-hornblende-quartz-feldspar assemblage, indicating multiple sources dominated by Huanghe River distal sediments.

Watershed classification by remote sensing indices： A fuzzy c-means clustering approach

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.

基于点云簇组合特征的激光雷达地面分割方法

针对三维激光雷达在地面分割过程中存在分割不足和过分割的问题,提出一种基于点云簇组合特征的激光雷达地面分割方法。首先将三维点云投影到扇形栅格中进行连通域聚类,将梯度相差较小的栅格聚为一类。然后根据路面点云符合平面和直线几何特征的特点,对每一簇进行特征值计算以挑选路面栅格簇的候选簇,接着对其进行径向方向上的梯度检查以剔除误判栅格。最后使用三次B样条曲线进行平滑拟合,实现地面点与非地面点的分割。在不同路面状况的场景中对所提方法进行验证。实验结果表明,所提方法在含有多障碍物路面的准确率为97.50%,计算时间为27ms,说明所提方法的地面提取准确率更高,路面适应性更强。