基于簇的移动Ad hoc网多层分布式入侵检测

动Ad hoc网络的独特网络特性导致其安全性特别脆弱，所以为其提供高安全的入侵检测系统势在必行。通过考虑在移动Ad hoc网络中入侵检测系统的分布式和协同工作的需要，提出了一种基于簇的多层分布式入侵检测技术，并给出模型。此模型采用统计学方法的异常检测技术结合数据挖据技术和簇技术对入侵进行检测．有效提高了移动Ad hoc网络的安全性和对分布式攻击的协同检测能力，并降低了网络的通信负荷。     

卫星系统新技术--应用机遇及发展策略考虑(上)

结合市场导向下的技术驱动重要性，重点论述现代卫星系统新技术及其在中国的应用机遇和发展策略。     

The Performance of Independent Active SMEs in French Competitiveness Clusters

This article analyses the evolution of independent members of French independent competitiveness clusters. It compares small and medium-sized enterprises (SMEs) that participated in collaborative research projects, funded by French competitiveness clusters, against similar control companies that were not part of such projects and not funded by the policy, over the period 2005–2013. French competitiveness clusters seek to enhance SMEs’ size, performance, innovation and employment; the current study therefore assesses their outcomes in terms of capital invested and equity returns. The findings from a difference-in-differences analysis reveal that sales, employment, R&D investments, and value added all increase in relative terms, but financial returns exhibit significant relative decreases for the smallest companies, whereas larger firms record strong increases. The inability of the smallest SMEs to reap gains from their cluster membership should be a matter of great concern to both companies and policy-makers.     

永不落幕的城市舞台 2010年上海世博会世博文化中心的设计与建造过程

2010年上海世博文化中心设计与建造实现了大型文化娱乐聚集区的综合定位目标,设计建造过程在超短工期状况下完成多项突破。其公众文化娱乐性现已初露锋芒,已有成果预期了世博后期长效动力的潜质。该项目无论是宏观定位还是设计建造过程,均显现其张力特点。     

A methodology for predicting regional freight traffic in developing countries

The prime concern of transportation planners in developing countries is how to collect and transfer data into models as simple and as effective as possible and obtain solutions in the shortest time.

In this study, a methodology which utilizes multivariate statistical analysis techniques for travel estimation is presented. The two simple and convenient techniques namely Cluster Analysis and Principal Component Analysis, are used for the evaluation of available data. The traffic demand is expressed as a function of principal components which are determined independently as a small set of variables to represent total system variability. A stepwise regression analysis is carried out to derive the traffic demand‐principal component relationship. The proposed methodology is then applied to Southeastern Anatolia Regional Development Project in Turkey.     

An improvement of cluster variation method entropy functional for bcc alloys

The entropy functional in the tetrahedron approximation of the cluster variation method (CVM) for the body-centered cubic structure has been modified to yield accurate consolute temperature for a phase separating system. The improvement is achieved by a change in the multiplicity of the basic tetrahedron cluster and approximately accounts for truncation errors in the entropy functional of CVM owing to finite size of the basic cluster. We demonstrate that the phase diagram, thermodynamic properties and short range order parameters show improved agreement with more accurate results, without involving additional computational burden. Hence, the modified entropy functional could be used as a standard for CVM.     

Multivariate analyses of major and trace elements in 19 species of herbs consumed in Yunnan,China

This study aimed to analyze the concentrations of 10 elements in 19 species of herbs related to medical and edible purpose in Yunnan, China. Microwave-assisted acid digestion was used for all of the dried herbs and element contents were determined by inductively coupled plasma atomic emission spectrometry. The accuracy of this method was validated by analyzing GBW07605 certified reference material. The results indicated that the distributions of element contents were varied over a wide range in the specimens tested. The decreasing sequence of average element content expressed as dry weight was presented as follows: calcium (1740–22,246 µg/g dry weight), magnesium (634–6367 µg/g dry weight), iron (52.8–5707 µg/g dry weight), barium (9.19–465 µg/g dry weight), zinc (10.7–82.1 µg/g dry weight), strontium (8.25–69.8 µg/g dry weight), copper (4.10–36.6 µg/g dry weight), chromium (0.26–13.3 µg/g dry weight), nickel (0.57–14.7 µg/g dry weight), and cadmium (0.11–2.66 µg/g dry weight). The element contents of samples were different depending on several species. However, the accumulations of toxic elements (cadmium, chromium, and nickel) were above the international safety standards limit in most samples. Principal component analysis generated three principal components that explained 77% of the total variance in the data. Similar samples may get together by cluster analysis and could correspond to the result of principal component analysis.     

Cluster structure of SnO2/SnCo composites as anodes for lithium ion batteries

SnO₂/CoSn composites have been prepared by hydrolysis accompanied by carbothermal reduction. With the addition of cobalt, SnO₂/CoSn nanoparticles self-assemble to form nano-micro-structure cluster even microparticles. The nano-micro-structure cluster not only shortens lithium-ion diffusion and electron transportation route, but also cohere together to form compact defense. This preserves the integrity of the structure and the capacity from fading during cycling. It exhibits the improved charge capacity of 627.3 mA h/g in the first cycle and 421.9 mA h/g after 50 cycles at 100 mA/g. It paves the way for practical applications of tin-based anodes.     

Decision methodology of end-milling conditions using data-mining

The purpose of the present study is to apply data-mining methods to support the decision of reasonable cutting conditions. Although an enormous amount of information is listed in a catalog, it is not possible to know all of it. Seen from the viewpoint of the user, this enormous amount of information becomes a hindrance. For example, even if an expert worker does not look at a catalog, in end-mill processing, he can decide the appropriate processing condition efficiently from experience; however, this type of situation creates difficult problems for an unskilled worker or a skilled worker with little experience. The recommended cutting condition for every type of material is listed in a catalog together with the appropriate tool, but it takes much time and labor to search and examine the catalog to find the right tool, and this process is inefficient. The main subject of our research was to support the processing condition of the end-mill for each precision tool efficiently based on end-mill clusters. Our research applied the techniques of data mining, in particular, non-hierarchy clustering and hierarchy clustering, to catalog data. With these techniques, we applied multiple regression analysis and reached the following main conclusions. As a first step, we paid attention to the shape element of catalog data. In addition to using conventional mining processes, we grouped end-mills from the viewpoint of tool shape, which meant the ratio of dimensions, visually by applying the K-means method. We applied variable cluster analysis next to each cluster and extracted an predictor variable to represent each cluster, and we performed multiple regression analysis and derived a cutting condition decision formula. The cutting condition decision formula provided high accuracy. The accuracy was higher than the results achieved through mining of all data. A more highly precise processing condition decision formula was derived by doing mining again, excluding the peculiar data clusters such as small diameter end-mill. We understood what was effective for cutting condition decision to be factors related to blade length and the ratio of the full length, factors which have not been singled out through background knowledge or expert knowledge, but were noticed as an effect of catalog mining.