Weighted principal component extraction with genetic algorithms

Pattern recognition techniques have been widely used in a variety of scientific disciplines including computer vision, artificial intelligence, biology, and so forth. Although many methods present satisfactory performances, they still have several weak points, thus leaving a lot of space for further improvements. In this paper, we propose two performance-driven subspace learning methods by extending the principal component analysis (PCA) and the kernel PCA (KPCA). Both methods adopt a common structure where genetic algorithms are employed to pursue optimal subspaces. Because the proposed feature extractors aim at achieving high classification accuracy, enhanced generalization ability can be expected. Extensive experiments are designed to evaluate the effectiveness of the proposed algorithms in real-world problems including object recognition and a number of machine learning tasks. Comparative studies with other state-of-the-art techniques show that the methods in this paper are capable of enhancing generalization ability for pattern recognition systems.     

基于中间件技术的变电站温升在线监测预警系统设计

针对由于各种原因引发变电站发热方面的故障,设计了变电站温升在线监测预警系统,建立了基于中间件技术的变电站温升在线监测预警系统框架模型。采用组件式开发技术设计并实现了变电站温升在线监测预警系统,将系统的构成组件分为界面型组件及数据维护组件。组件式系统能够根据企业需求的变化实现快速重构,提高了信息系统的柔性。     

分布式PACS通信服务中间层构造方法研究

基于目前分布式PACS中存在的问题,提出一个通信服务中间层的构造方法,分别进行了基于设计模式通信框架的构造、DICOM图像互操作和分布式PACS可复用构件的研究.通信服务中间层的构造不仅提高了分布式PACS在网络异构环境中的可移植性、可扩展性以及相关QoS性能,还满足了PACS今后发展中提出的较高的互连互操作性的要求.     

Research and Applications of Semi-solid Processing

Since it was pioneered by Flemings and otherresearchers [11, the semi-solid metal processingtechnique has been widely stUdied in the world.Especially in the past decade, SSM as a newproduct forming technique has been rapidly developed and put into commercial applications[2, 31. In recent years, with the development ofthe ado-industry of China, a lot of researchwork on SSM has been done, including the preparation of slumes, reheating and thixoforming[Al6]; moreover, great achievements have …     

钢铁构件表面上的无机富锌涂层

阐述了无机富锌涂层的研究、发展、特性和应用，指出无机富锌涂层具有杰出的耐环境侵蚀性能、耐化学性、耐溶剂性、耐辐射性、耐热性，在恶劣腐蚀环境下它是钢结构件的最佳底涂层。     

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.     

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。     

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。     

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.