时间Petri网与CAGA算法相结合的并行测试研究

在并行测试的研究中对于任务的测试次序一直是个研究重点及难题。用时间Petri网建立关于并行测试系统的模型,并首次用元胞遗传算法(CAGA)对模型中的变迁序列进行搜索,快速得到最好的测试序列。通过仿真结果表明,该算法展示了很好的收敛效果,并且与已有的研究成果相比更具优势。     

基于改进离散粒子群禁忌算法的并行测试任务调度

针对并行测试任务调度问题特点,提出了一种解决并行测试任务调度的离散粒子群禁忌算法,设计了符合并行测试任务调度特点的粒子初始化编码方式、粒子位置转移公式以及禁忌搜索邻域。通过与禁忌搜索相结合,避免了离散粒子群算法早熟问题。通过实验以及算法对比,表明提出的算法是快速有效的,能够很好地解决并行测试任务调度问题,求得测试总时间最短的任务调度序列。     

无理数字典码的测试数据压缩方案

提出了一种无理数字典码的测试数据压缩方案,能有效地压缩芯片测试数据。方案利用无理数序列建立字典,编码时只需进行相容性检查,无需填充无关位,简化了操作步骤;同时,选择局部压缩率最大的一组数据作为最终编码结果,保证压缩效果可以达到局部最优。对ISCAS 89标准电路Mintest集的实验结果显示,该方案的压缩效果优于传统压缩方法,如Golomb码、FDR码、混合定变长码等。     

飞机碱性电瓶充放电参数测量仪的设计与研究

电瓶是民航飞机可靠供电的最后保证。飞机制造厂家提高了对电瓶的性能和容量检测要求,传统的电瓶充放电装置已经无法满足新的要求。利用单片机技术研制的飞机电瓶参数测试仪,实现了对电瓶各种参数的实时侧量和分析,为飞机电瓶维护和检测提供依据。     

基于Hg~(2+)诱导DNA双链形成的汞离子检测试纸条研制

Hg2+能特异性地与2个胸腺嘧啶碱基(T)共价结合,介导T-T错配形成稳定的T-Hg2+-T结构。基于DNA修饰的纳米金探针研制了一种可检测水溶液中Hg2+浓度的层析试纸条。试纸条包含1条控制线和2条测试线,检测结果在5 min内可见,裸眼可见检测灵敏度为100 nmol/L。金标条阅读仪分析结果在10 nmol/L~10μmol/L范围内具有良好的线性关系,且对常见二价重金属离子Cu2+,Ni2+,Mg2+,Zn2+,Pb2+具有很好的选择性。该试纸条灵敏特异、快捷简单、操作方便、成本低廉,在环境监测和食品安全的现场快速检测中具有很好的应用前景。     

MEMS气体流量传感器的简易流量测试装置

为了完成所研制的MEMS气体流量传感器样品的流量测试与标定,设计制作了一种由标准流量发生器和传感器信号读出与数据采集电路组成的简易流量测试装置。标准流量发生器由注射器和可更换的砝码组成,利用不同的砝码配重,在注射器出气口产生合适的恒定气体流速。通过理论分析和Ansys有限元数值仿真,验证了简易标准流量发生器的可行性。传感器信号读出与数据采集电路基于内建多路A/D转换器的单片机实现,具有传感器加热电阻器的恒温控制、流量信号的数字检测和显示的功能。采用该简易流量测试装置对自行研制的MEMS气体流量传感器进行了流量测试与标定,获得了待测器件的标定参数、传感器流量测量的绝对误差和相对误差。     

一种基于SBL和APIT的混合定位算法

由于子区域分割粒度的限制,基于阶次序列的定位算法(Sequence-based localization,SBL)存在边缘区域节点定位误差较大和不能保证平均定位误差性能的问题。针对这些问题,提出了一种基于SBL和APIT的混合定位算法,利用APIT信标三角形切割SBL算法子区域,减小子区域面积,实现了分割粒度的细化。通过预先进行系统训练,优化了混合算法的加权系数,进一步提升了算法的定位精度。仿真结果表明,相比于原算法,所提出的混合算法有效地提升了边界区域节点的定位精度,其平均定位误差降低了17.9%,使基于阶次序列的定位算法的定位精度得到了有效改善。     

用磷石膏生产建筑材料的新进展

针对湿法磷酸副产的磷石膏在建筑材料中的应用及其国内外进展，介绍了用磷石膏制高强度砌块的中间试验流程，并指出在磷石膏中搀入其他材料，采用特殊复合工艺，可生产出新型功能性建筑材料，如石膏基导电材料和石膏基磁性材料等这不仅拓展了石膏的用途，又可获得巨大的经济效益。     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.     

What you get is what you see: revisiting the evaluator effect in usability tests

Usability evaluation is essential to user-centred design; yet, evaluators who analyse the same usability test sessions have been found to identify substantially different sets of usability problems. We revisit this evaluator effect by having 19 experienced usability professionals analyse video-recorded test sessions with five users. Nine participants analysed moderated sessions; 10 participants analysed unmoderated sessions. For the moderated sessions, participants reported an average of 33% of the problems reported by all nine of these participants and 50% of the subset of problems reported as critical or serious by at least one participant. For the unmoderated sessions, the percentages were 32% and 40%. Thus, the evaluator effect was similar for moderated and unmoderated sessions, and it was substantial for the full set of problems and still present for the most severe problems. In addition, participants disagreed in their severity ratings. As much as 24% (moderated) and 30% (unmoderated) of the problems reported by multiple participants were rated as critical by one participant and minor by another. The majority of the participants perceived an evaluator effect when merging their individual findings into group evaluations. We discuss reasons for the evaluator effect and recommend ways of managing it.