磁能驱动微型泵的性能研究

在电磁驱动原理的基础上,设计并研制了一种磁能驱动的微型泵。微型泵的整体尺寸约为Φ11mm×4mm,腔室半径为5mm,深2mm。利用方差分析方法对实验数据进行了检验,验证了前期微型泵研究中获得的性能参数的正确性。为了进一步缩小微型泵体积、简化控制方式和外围电路,针对微型泵的能源部分,开展了单双电源的对比实验研究,并获得了较好的实验结果。     

基于特征选择和最大熵模型的汉语词义消歧

词义消歧是自然语言处理中一类典型的分类问题.在分类中,特征的选择至关重要.通常情况下,特征是由人工选择的,这就要求特征选取者对于待分类的问题本身和分类模型的特点有深刻的认识.分析了汉语词义消岐中特征模板对消歧结果的影响,在此基础上提出一套基于最大熵分类模型的自动特征选择方法,包括针对所有歧义词的统一特征模板选择和针对单个歧义词的独立特征模板优化算法.实验结果表明,使用自动选择的特征,不仅简化了特征模板,而且提高了汉语词义消歧的性能.与SemEval 2007:task #5的最好成绩相比,该方法分别在微平均值MicroAve(micro-average accuracy）)和宏平均值MacroAve(macro-average accuracy）)上提升了3.10%和2.96%.     

基于小波和人工智能技术的车辆无缝定位技术研究

基于自适应神经模糊逻辑推理系统(ANHS),在全球定位系统(GPS)信号阻塞时,为惯性导航系统(INS)提供位置和速度修正量以提高系统的精度和鲁棒性.首先用小波对数据信号进行降噪处理;然后设定INS的位置或速度作为ANHS的输入参数,经训练后输出相应修正量,训练期望值为经小波多分辨率分析得到的位置误差和速度误差.实验表明,无GPS信号时定位精度比同条件下卡尔曼滤波精度提高约40%,因此该方法可为车辆提供可靠有效的导航定位服务.     

Slicing for architectural analysis

Current software development often relies on non-trivial coordination logic for combining autonomous services, eventually running on different platforms. As a rule, however, such a coordination layer is strongly woven within the application at source code level. Therefore, its precise identification becomes a major methodological (and technical) problem and a challenge to any program understanding or refactoring process.The approach introduced in this paper resorts to slicing techniques to extract coordination data from source code. Such data are captured in a specific dependency graph structure from which a coordination model can be recovered either in the form of an Orc specification or as a collection of code fragments corresponding to the identification of typical coordination patterns in the system. Tool support is also discussed.     

Helicopter maintenance error analysis: Beyond the third order of the HFACS-ME

In this paper a statistical analysis of a sample of 58 helicopter maintenance-induced safety occurrences is conducted to study helicopter accidents and incidents’ survivability and the severity distribution of such occurrences. Analysis is also carried out to identify helicopter main and sub-systems mostly exposed to maintenance errors and to determine various types of such errors. Expected inherent relations between rotorcraft components affected and types of associated maintenance errors are investigated. Human factors-based triggers of these accidents and severe incidents are explored. The concept of Specific Failures (SFs) that immediately precede each of such occurrences is introduced for more detailed representation of the last breached individual and organizational safety barriers. Root causes of these safety occurrences were then sought utilizing the Human Factors Analysis and Classification System-Maintenance Extension (HFACS-ME) taxonomy with a refined focus on its third order categories’ list. The rotorcraft characteristics influencing individuals and organizational behaviours within Maintenance, Repair and Overhaul organizations (MROs) are discussed in the light of the root cause investigation results.

Relevance to industry

The study of human reliability within helicopter maintenance industry is waited to emphasise the understanding of causes and propagation mechanisms of maintainers' errors and their consequences on the overall aviation safety. Previous works often investigated maintenance errors and their roles in promoting aviation accidents of fixed-wing aircraft; this research is investigating the case of rotorcraft.     

Modular analysis and modelling of risk scenarios with dependencies

The risk analysis of critical infrastructures such as the electric power supply or telecommunications is complicated by the fact that such infrastructures are mutually dependent. We propose a modular approach to the modelling and analysis of risk scenarios with dependencies. Our approach may be used to deduce the risk level of an overall system from previous risk analyses of its constituent systems. A custom made assumption-guarantee style is put forward as a means to describe risk scenarios with external dependencies. We also define a set of deduction rules facilitating various kinds of reasoning, including the analysis of mutual dependencies between risk scenarios expressed in the assumption-guarantee style.     

Exploring the benefits of the combination of a software architecture analysis and a usability evaluation of a mobile application

Designing easy to use mobile applications is a difficult task. In order to optimize the development of a usable mobile application, it is necessary to consider the mobile usage context for the design and the evaluation of the user-system interaction of a mobile application. In our research we designed a method that aligns the inspection method “Software ArchitecTure analysis of Usability Requirements realizatioN” SATURN and a mobile usability evaluation in the form of a user test. We propose to use mobile context factors and thus requirements as a common basis for both inspection and user test. After conducting both analysis and user test, the results described as usability problems are mapped and discussed. The mobile context factors identified define and describe the usage context of a mobile application. We exemplify and apply our approach in a case study. This allows us to show how our method can be used to identify more usability problems than with each method separately. Additionally, we could confirm the validity and identified the severity of usability problems found by both methods. Our work presents how a combination of both methods allows to address usability issues in a more holistic way. We argue that the increased quantity and quality of results can lead to a reduction of the number of iterations required in early stages of an iterative software development process.     

Edge detection insensitive to changes of illumination in the image

In this paper we present new edge detection algorithms which are motivated by recent developments on edge-adapted reconstruction techniques [F. Aràndiga, A. Cohen, R. Donat, N. Dyn, B. Matei, Approximation of piecewise smooth functions and images by edge-adapted (ENO-EA) nonlinear multiresolution techniques, Appl. Comput. Harmon. Anal. 24 (2) (2008) 225–250]. They are based on comparing local quantities rather than on filtering and thresholding. This comparison process is invariant under certain transformations that model light changes in the image, hence we obtain edge detection algorithms which are insensitive to changes in illumination.     

An EDBoost algorithm towards robust face recognition in JPEG compressed domain

In this paper, we describe a novel multiclass boosting algorithm, EDBoost, to achieve robust face recognition directly in JPEG compressed domain. In comparison with existing boosting algorithms, the proposed EDBoost exploits Euclidean distance (ED) to eliminate non-effective weak classifiers in each iteration of the boosted learning, and hence improves both feature selection and classifier learning by using fewer weak classifiers and producing lower error rates. When applied to face recognition, the EDBoost algorithm is capable of selecting the most discriminative DCT features directly in JPEG compressed domain to achieve high recognition performances. In addition, a new DC replacement scheme is also proposed to reduce the effect of illumination changes. In comparison with the existing techniques, the proposed scheme achieves robust face recognition without losing the important information carried by all DC coefficients. Extensive experiments support the conclusion that the proposed algorithm outperforms all representative existing techniques in terms of boosted learning, multiclass classification, lighting effect reduction and face recognition rates.