Feature extraction using orthogonal discriminant local tangent space alignment

A novel algorithm called orthogonal discriminant local tangent space alignment (O-DLTSA) is proposed for supervised feature extraction. Derived from local tangent space alignment (LTSA), O-DLTSA not only inherits the advantages of LTSA which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. The experimental results of applying O-DLTSA to standard face databases demonstrate the effectiveness of the proposed method.     

Stochastic TCP friendliness: Expanding the design space of TCP-friendly traffic control protocols

While the current definition of TCP friendliness has enabled a wide variety of traffic control protocols other than TCP, it still considerably restricts the design space of TCP-friendly traffic control protocols. For example, some multimedia streaming applications prefer a smooth sending rate on a time scale of tens of seconds, however, a UDP flow maintaining such a long smoothness time scale is naturally not TCP friendly by the current definition. In this paper, we propose an innovative method to relatively compare the degrees of TCP friendliness of different traffic control protocols, and use it to define a new class of TCP friendliness definitions, called stochastic TCP friendliness (STF). STF greatly expands the design space of TCP-friendly traffic control protocols, while still effectively maintaining the desired fairness of the Internet. To demonstrate the expanded design space, we also develop a new congestion control protocol, called TCP-friendly CBR-Like Rate Control (TFCBR), for multimedia streaming applications which do not require a high sending rate but prefer a smooth sending rate on a time scale of tens of seconds. TFCBR is stochastically TCP friendly, however, it is not TCP friendly by the current definition.     

Financial distress prediction using support vector machines: Ensemble vs. individual

Financial distress prediction (FDP) is of great importance to both inner and outside parts of companies. Though lots of literatures have given comprehensive analysis on single classifier FDP method, ensemble method for FDP just emerged in recent years and needs to be further studied. Support vector machine (SVM) shows promising performance in FDP when compared with other single classifier methods. The contribution of this paper is to propose a new FDP method based on SVM ensemble, whose candidate single classifiers are trained by SVM algorithms with different kernel functions on different feature subsets of one initial dataset. SVM kernels such as linear, polynomial, RBF and sigmoid, and the filter feature selection/extraction methods of stepwise multi discriminant analysis (MDA), stepwise logistic regression (logit), and principal component analysis (PCA) are applied. The algorithm for selecting SVM ensemble's base classifiers from candidate ones is designed by considering both individual performance and diversity analysis. Weighted majority voting based on base classifiers’ cross validation accuracy on training dataset is used as the combination mechanism. Experimental results indicate that SVM ensemble is significantly superior to individual SVM classifier when the number of base classifiers in SVM ensemble is properly set. Besides, it also shows that RBF SVM based on features selected by stepwise MDA is a good choice for FDP when individual SVM classifier is applied.     

Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis

General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.     

An adaptive hybrid surrogate model

The determination of complex underlying relationships between system parameters from simulated and/or recorded data requires advanced interpolating functions, also known as surrogates. The development of surrogates for such complex relationships often requires the modeling of high dimensional and non-smooth functions using limited information. To this end, the hybrid surrogate modeling paradigm, where different surrogate models are combined, offers an effective solution. In this paper, we develop a new high fidelity surrogate modeling technique that we call the Adaptive Hybrid Functions (AHF). The AHF formulates a reliable Crowding Distance-Based Trust Region (CD-TR), and adaptively combines the favorable characteristics of different surrogate models. The weight of each contributing surrogate model is determined based on the local measure of accuracy for that surrogate model in the pertinent trust region. Such an approach is intended to exploit the advantages of each component surrogate. This approach seeks to simultaneously capture the global trend of the function as well as the local deviations. In this paper, the AHF combines four component surrogate models: (i) the Quadratic Response Surface Model (QRSM), (ii) the Radial Basis Functions (RBF), (iii) the Extended Radial Basis Functions (E-RBF), and (iv) the Kriging model. The AHF is applied to standard test problems and to a complex engineering design problem. Subsequent evaluations of the Root Mean Squared Error (RMSE) and the Maximum Absolute Error (MAE) illustrate the promising potential of this hybrid surrogate modeling approach.     

基于CMOS工艺的无线图像传感器网络综述

无线传感器网络是无线通讯技术、网络技术、传感器技术交叉融合的新兴产物。无线传感器网络具备小型化、智能化、网络化和能够无线通讯等特点,能够满足人们在生产生活、科学研究、军事、医疗等众多领域对于实时信息获取的需求。由于人类获取信息的80%以上是通过视觉获取的,因此,无线图像传感器网络作为人类获取视觉图像信息的重要工具在无线传感器网络中占有重要的地位。无线图像传感器网络要想实现小型化、智能化、集成化等要求,采用与标准CMOS工艺兼容的CMOS图像传感器技术将是最为理想的选择。随着CMOS图像传感器工艺和设计水平的进步,CMOS图像传感器在成像质量、生产成本、小型化、智能化等方面显示出巨大的优势,同时由于与VLSI电路兼容的天然特性,基于CMOS图像传感器的无线图像传感器网络拥有巨大的发展潜力和广阔的市场前景。基于CMOS的无线图像传感器网络目前呈现多元化的发展趋势,高分辨率、高速、低功耗、智能化等方向是基于CMOS的无线图像传感器网络未来发展的方向。     

从简约视角分析当代书籍设计的趋势

简约理念作为一种设计思想,旨在用尽可能少的元素表现事物的内涵,简约理念下的书籍在设计上追求视觉的"简洁",在情感上追求丰富,这对于当代社会下的环境保护、节约能源有着重要的意义。通过对简约理念的阐释,分析当代书籍设计形式上存在的问题,并提出"简约主义"风格在书籍设计中运用的可行性。     

Analysis of reasons why gas sensors influence long-term stability of flammable gas alarms and produce false alarm

概述了影响报警产品性能的两大原因-电路和气体传感器,其中气体传感器部分是影响质量的关键.以半导体传感器为例,分析了可燃气体报警器在正常使用环境下经常发生误报警及使用一段时间后不报警的原因,并采取了相应的措施—通过对传感器增加干扰气体过滤帽把主要影响报警器误报的乙醇等气体滤除掉,并尽可能远离含硅物质以保证传感器的长期稳定性.根据对不同质量传感器在各种气体环境下的试验数据及特性曲线分析,论述了传感器对报警产品的可靠性及长期稳定性的影响,并针对报警器的误报问题提出相应的解决方法.     

双离合器式自动变速器换档优化控制研究

研究汽车换挡过程中的摩擦问题,降低摩擦损耗。针对双离合器式自动变速器换档过程,压力突变很大,造成车辆的输出转矩发生剧烈波动,硬件摩擦损耗很大的问题,提出一种双离合器式自动变速器换档优化控制策略,通过建立双离合器物理动力学模型和构建换档规律模型,运用优化的压力控制PID算法,控制在汽车换挡过程中的压力变化,使得压力能够达到一个最优的控制状态,减少硬件摩擦。实验结果表明,换档优化控制策略能够大幅降低换挡过程的摩擦消耗,取得了不错的效果。     

栅栏覆盖最大化生存周期调度算法

栅栏覆盖是近年来无线传感器网络的研究热点之一,如何延长生存周期是无线传感器网络研究的一个重要问题。针对无线传感器网络的栅栏覆盖应用,设计了两种最大化网络生存周期的调度算法:集中式的Greedy调度算法和分布式的DBCS调度算法。仿真实验表明:两种算法显著地延长了网络的生存周期;在较稀疏的网络中,DBCS算法与Greedy算法性能接近,分布式的DBCS算法适合应用于大规模传感器网络。