激光打孔技术在光伏背板玻璃上的应用

双玻光伏组件以其抗PID性强、防隐裂、防水汽透过、抗蜗牛纹、可靠性优异、轻量化等诸多优点,在晶硅太阳能组件市占比逐步提高。双玻光伏组件用背板玻璃一般需要预留出线孔,光伏背板玻璃的出线孔主要有两种打孔方式:金钢钻上下同步钻孔的模式和激光打孔。激光打孔以其易维护、可异形孔加工、效率高、生产成本低等优势得到各大玻璃厂的认可。通过分析在实际生产中激光打孔出现的打孔缺陷问题,提出了改善措施,有助于工厂的降本增效。     

基于VR技术的智能变电站运行仿真培训系统

为了更加真实、直观地呈现智能变电站的运行整体结构,构建基于VR技术的智能变电站运行仿真培训系统。该系统通过5个模块协同完成智能变电站运行仿真培训,结合实际变电站的设备情况,采用二维纹理映射方法完成变电站设备仿真建模;系统利用虚拟现实交互模块呈现培训场景,参与培训人员能够通过人机交互设备,实现场景漫游和相关培训内容操作。测试结果表明:该系统能够较好地模拟出智能变电站的实际运行情况,参与培训的人员可以身临其境地完成整个变电站的巡视,更真实、直观地呈现智能变电站的整体状态。     

产教融合背景下高职特色专业群“平台+模块”课程体系建设研究

In view of the problems in the construction of characteristic professional groups in higher vocational colleges, the professional group of "intelligent manufacturing" is taken as the research object, and the "platform + module" suitable for the development of the industry will be constructed according to the content of the vocational ability planning courses of the post group. The curriculum system establishes the framework of professional groups, and explores and forms a strategic study to optimize and integrate professional group resources.     

基于LDRA TBrun软件集成测试的研究

Integration testing is to ensure that each functional module is correct and performs well, so that these modules can be as- sembled together to work well. The manual testing method of the tester alone cannot effectively evaluate the tested aviation soft- ware, and the use of advanced automated testing tools can overcome these difficulties. This paper introduces the method and process of integration testing with Testbed suite tool TBrun through a practical test project.     

2018年中国4省脱粒小麦中9种真菌毒素污染情况调查

目的 调查2018年湖北、安徽、江苏和河北4省脱粒小麦中5种镰刀菌毒素和4种黄曲霉毒素（aflatoxins，AFs）的污染情况。方法 从4省采集2018年5~6月收获的脱粒小麦371份，经乙腈-水（84:16，V:V）提取、Mycosep 226多功能净化柱净化后，采用高效液相色谱串联质谱同时测定样品中脱氧雪腐镰刀菌烯醇（deoxynivalenol，DON）、3-乙酰基-DON（3-acetyl-DON，3-Ac-DON）、15-乙酰基-DON（15-acetyl-DON，15-Ac-DON）、雪腐镰刀菌烯醇（nivalenol，NIV）、玉米赤霉烯酮（zearalenone，ZEN）和4种AF, 共9种毒素的含量。 结果 4省脱粒小麦受多种镰刀菌毒素, 尤其是DON的污染较为严重，而AF污染相对较轻。DON的检出率最高,为90.3%，阳性样品中DON平均污染水平为2706.3 μg/kg，45.3%样品DON的含量超过国家限量标准1000 μg/kg；4种AF的检出率和平均污染水平均较低，其中AFB1和AFB2的检出率最高，均为3.2%，2份样品AFB1含量超过国家限量标准5 μg/kg，仅1份样品检出AFG2，所有样品均未检出AFG1。从地区来看，湖北省脱粒小麦中各毒素污染最严重，尤其是DON、其检出率和平均污染水平分别为100%和6314.9 μg/kg；江苏省样品中ZEN的平均污染水平为1971.2 μg/kg，远高于其它省份。Spearman相关性分析表明，5种镰刀菌毒素的污染具有显著相关性，DON与已检出的3种AF及3种AF间的污染相关性均较弱或无显著相关性。 结论 4省脱粒小麦受5种镰刀菌毒素的污染较为严重且具有显著相关性。污染程度为湖北>安徽>江苏>河北。因此，有必要对4省脱粒小麦中多种毒素的协同污染情况进行持续监测。     

Extensive evaluation and classification of low-cost dust sensors in laboratory using a newly developed test method

An extensive evaluation of low-cost dust sensors was performed using an exponentially decaying particle concentration. A total of 264 sensors including 27 sensors with light-emitting diodes (LEDs) and 237 sensors with laser lighting sources were tested. Those tested sensors were classified into 4 groups based on the deviation from the reference data obtained by a reference instrument. The response linearities of all the tested samples for PM₁, PM_2.5, and PM₁₀ were in excellent agreement with the reference instrument, except a few samples. For the measurements of PM₁ and PM_2.5, the lighting source, that is, LED or laser, did not show any significant difference in overall sensor performance. However, LED-based sensors did not perform well for PM₁₀ measurements. The 32, 24, and 16% of all the tested sensors for PM₁, PM_2.5, and PM₁₀ measurement, respectively, are in the category of Class 1 (reference instrument reading ± 20%) requirement. The performance of the low-cost dust sensors for PM₁₀ measurement was relatively less satisfactory.     

基于模糊谱聚类的不确定蛋白质相互作用网络功能模块挖掘

针对谱聚类融合模糊C-means（FCM）聚类的蛋白质相互作用（PPI）网络功能模块挖掘方法准确率不高、执行效率较低和易受假阳性影响的问题，提出一种基于模糊谱聚类的不确定PPI网络功能模块挖掘（FSC-FM）方法。首先，构建一个不确定PPI网络模型，使用边聚集系数给每一条蛋白质交互作用赋予一个存在概率测度，克服假阳性对实验结果的影响；第二，利用基于边聚集系数流行距离（FEC）策略改进谱聚类中的相似度计算，解决谱聚类算法对尺度参数敏感的问题，进而利用谱聚类算法对不确定PPI网络数据进行预处理，降低数据的维数，提高聚类的准确率；第三，设计基于密度的概率中心选取策略（DPCS）解决模糊C-means算法对初始聚类中心和聚类数目敏感的问题，并对预处理后的PPI数据进行FCM聚类，提高聚类的执行效率以及灵敏度；最后，采用改进的边期望稠密度（EED）对挖掘出的蛋白质功能模块进行过滤。在酵母菌DIP数据集上运行各个算法可知，FSC-FM与基于不确定图模型的检测蛋白质复合物（DCU）算法相比，F-measure值提高了27.92%，执行效率提高了27.92%；与在动态蛋白质相互作用网络中识别复合物的方法（CDUN）、演化算法（EA）、医学基因或蛋白质预测算法（MGPPA）相比也有更高的F-measure值和执行效率。实验结果表明，在不确定PPI网络中，FSC-FM适合用于功能模块的挖掘。     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.