面向网络化定制的产品族复合建模技术

在分析了网络化定制关键技术的基础上，针对性地对信息映射方法和产品族建进行了研究。提出了多层结构信息映射模型，以及综合类元件和参数化特征建模的双层复合产品族模型。结合本方法和以往网络化制造领域中所取得的成果，实现了较完整的基于Web的个性化定制过程。     

Identification of a minor component of the sex pheromone ofLeucoptera malifoliella (Lepidoptera,lyonetiidae)

A new minor component in the female volatile extract ofLeucoptera malifoliella (Costa) (Lepidoptera, Lyonetiidae) has been identified as 5,9-dimethyloctadecane (2). The amount detected of the minor compound2 ranged from 4 to 8% in comparison with the major component 5,9-dimethylheptadecane (1). Neither compound has been found in the male volatile extract. The identification has been based on its spectroscopic properties and Chromatographic behavior in comparison with an authentic synthetic sample. The synthesis has been carried out through a short route from 2,6-dimethylcyclohexanone (3). In field tests, compound2 appears to act as a Synergist of the major component1 when mixed with the latter in a 1000.1– 5 ratio.     

引汉济渭工程运行调度综合服务平台的应用实践

针对引汉济渭工程运行调度的实际需求和特点,基于云计算技术,采用面向服务的体系结构(SOA)和JAVA EE平台技术架构,研发了基于云服务架构的引汉济渭工程运行调度综合服务平台,建立了基于平台、组件、知识图及可视化工具灵活搭建的引汉济渭工程运行调度业务应用系统构建模式,形成基于平台、基于组件、基于主题、基于可视化的应用模式,解决了传统的工程运行调度系统应用性差、功能单一、可扩展性差及应用模式单一等问题。通过平台提供工程运行调度业务服务,为工程联合调度提供技术支撑。     

单组分阴离子水性聚氨酯胶膜的热质研究

应用热质分析（TG）研究了单组分阴离子水性聚氨酯胶膜的耐热性能，结果表明：n（--NCO）：n（--OH）增大，胶膜的耐热性能增强；聚酯相对分子质量增加，耐高温性能降低；用DEG扩链时的耐高温性能比用DEG／TMP时的好；TDI型水性聚氨酯成膜物的耐高温性能优于IPDI型水性聚氨酯。     

低温共烧陶瓷技术及其应用

低温共烧陶瓷(low temperature co-fired ceramic,LTCC)技术是实现电子元件小型化、片式化的一种理想的封装技术,已成为电子元件集成的主要工艺方式,引起了人们的广泛关注。本文详细叙述了LTCC技术的特点、LTCC材料体系、国内外发展现状以及LTCC技术在电子元件集成中的应用。认为利用LTCC技术来实现电源、有源和无源器件的一体化将是今后信息功能陶瓷发展的一个重要方向。我国应该抓住LTCC技术所面临的前所未有的发展机遇,大力开发具有自主知识产权的LTCC技术,整体提升我国在电子集成领域的技术水平和国际竞争力。     

基于DPSIR和主成分分析的阜康市水资源承载力评价

针对新疆自治区阜康市水资源日益短缺及生态环境逐渐恶化的现实情况,根据DPSIR框架展开水资源承载力因子分析,构建了水资源承载力多因素基础指标体系;应用主成分分析法筛选出主要指标,采用熵权法进行赋权;构建综合评价模型对水资源承载力进行计算。研究表明:筛选出的4个主成分累计贡献率为92.016%,能充分代表多因素基础指标体系;阜康市九年间(2006年-2014年)水资源承载力综合评分值在0.498~0.531之间波动,下降速率缓慢且波动幅度减小,整体上处于开发饱和阶段。社会和经济发展的压力带来的农业用水的增加是导致水资源承载力下降的主要指标,扩大地下水开采和推行的节水灌溉等压采措施则在很大程度上缓解了下降的趋势。     

基于TOPSIS-模糊综合评判的模糊推理模型在开河预报中的应用

冰凌开封河受到较多自然和人为因素的影响,具有较高的不确定性,为了进一步提高冰凌开封河预测的精度,考虑各因素的综合作用成为解决问题的关键。先采用主成分分析法初步确定冰凌开封河历时影响因子的权重,运用模糊推理模型依据影响因子矩阵的相似性进行初步预测,进而采用TOPSIS-模糊综合评判模型对预报因子进行识别,筛选出合理的预报因子进行二次预测。运用实例对基于TOPSIS-模糊综合评判模型冰凌预报因子识别的模糊推理模型的效果进行了检验,同时与冰凌预报模糊优选神经网络BP模型进行对比,结果表明:在TOPSIS-模糊综合评判模型因子进行识别基础上的模糊推理模型预测精度较高、效果较好,既能够有效识别预报因子,又能够较好地提高预报封河、开河历时的精度,为凌汛预测提供了新的途径。     

单组分聚脲材料在寒冷地区某大坝溢流面防护中的应用

水利水电工程泄洪建筑物表面经常遭受高速含沙水流或携带推移质的冲刷,经过一段时间的运行,往往会出现不同程度的磨损及气蚀破坏,采用性能优越的水工混凝土抗冲耐磨材料修复和防护尤为重要。本文通过单组分聚脲材料性能试验揭示了材料的特点,介绍了其在新疆某大坝溢流面防护中的应用情况,其处理技术对类似工程具有良好的借鉴意义。     

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.     

Fault identification for process monitoring using kernel principal component analysis

In this research, we develop a new fault identification method for kernel principal component analysis (kernel PCA). Although it has been proved that kernel PCA is superior to linear PCA for fault detection, the fault identification method theoretically derived from the kernel PCA has not been found anywhere. Using the gradient of kernel function, we define two new statistics which represent the contribution of each variable to the monitoring statistics, Hotelling's T²and squared prediction error (SPE) of kernel PCA, respectively. The proposed statistics which have similar concept to contributions in linear PCA are directly derived from the mathematical formulation of kernel PCA and thus they are straightforward to understand. The main contribution of this work is that we firstly suggest a fault identification method especially applicable to process monitoring using kernel PCA. To demonstrate the performance, the proposed method is applied to two simulated processes, one is a simple nonlinear process and the other is a non-isothermal CSTR process. The simulation results show that the proposed method effectively identifies the source of various types of faults.