施工中电焊机的安全使用简述

从电焊机自身电气特点及建筑工地工作环境入手,探讨电焊机安全技术的实施方法。分析电焊机安全隐患及事故的分类以及电焊机在使用过程中产生危险的原因。介绍电焊机在安装以及作业过程中的安全措施。     

The concept of smartness in cyber–physical systems and connection to urban environment

The next-generation systems are expected to be largely cyber–physical systems (CPSs) that autonomously control physical processes, through sensors and actuators typically in real-time feedback and cooperative control loops distributed among physical and cyber environments. The rapid technological advancements enhance the smartness of these CPSs, pushing their boundaries of performance and efficiency by embedding new information and communication technologies. However, to what extent CPSs should be smarter so that they do not compromise safety and security of safety critical systems? is an open research question. Towards this goal, the purpose of this study is to establish a grounded theory to analyse what makes these systems smart? and eventually, how to find a balance between smartness and safety risks? In this precinct, this article aims to develop a conceptual framework, define the dimensions and derive the characteristics that make CPSs smart. The proposed approach combines an automated informetric and systematic analysis of literature pertinent to the topic of smartness across anthropology, science, engineering and technology. The analysis of a case study building and the discussions presented herein support the connection between the existing understanding of CPSs and smartness offered by the building design approach in urban environment.     

综合案例设计在“传感器与测试技术”课程教学中的应用

“传感器与测试技术”涉及多学科交叉，是“通专融合、跨界培养”为特征的新工科人才培养中相关专业的重要课程。在非电类专业本科生所学相应基础知识有限的情况下，如何利用有限学时达到最佳教学效果，是本课程面临的一个挑战。本文探讨了教学中对不同类型传感器知识体系的凝练方法，以综合案例的分析对不同学科知识进行交叉应用介绍，希望能够加深学生对理论知识的理解，提高课堂的参与度，锻炼学生综合思考的能力。     

基于Hadoop的电力运维审计系统的设计

国家电网公司信息化程度越来越高，单机运维审计系统产生的数据量日益增多，对海量数据高效率存储分析性能严重下降，系统稳定性降低。为满足国家电网当前对运维审计系统数据存储分析以及系统稳定性的需求，在Hadoop开源架构的基础上，本文提出基于Hadoop集群的海量数据分布式存储方法和基于Heartbeat的心跳检测技术，实现基于Hadoop的电力运维审计系统。〖JP2〗实验测试结果表明，基于Hadoop的电力运维审计系统相比单机系统可用性提高了8.42%，大大提升了存储分析海量数据的性能，具有系统工作稳定和服务不间断等优势。     

Improved low energy adaptive clustering hierarchy and its optimum cluster head selection

ABSTRACT

Wireless sensor networks (WSNs) play a vital role in present-day world, which are being used in different types of applications and occupy an important part in networking domain. The main objective of WSNs is to sense and collect the information from a given area of interest and provide the gathered data to the sink. WSN comprises of number of sensor nodes with batteries of limited energy for communication and computational activities, which are not possible to recharge the batteries after their deployment in the region of interest. Therefore, saving battery energy and utilising the limited power to the optimum level for extending network lifetime became the main factor of WSN. Hence, optimum cluster head (CH) selection will make the network to support longer lifetime and balanced energy consumption during its lifetime. Our proposed protocol selects the optimum CH and found out to be more efficient than the existing low energy adaptive clustering hierarchy. The simulated output shows better network lifetime and in some other performance metric.     

基于网络学习行为聚类分析的在线课堂优化策略研究

利用聚类分析法对网络学习行为分类，从而挖掘不同类型学习者在学习行为和学习效果间的差异。以大学计算机公共课《数据库技术及应用》为例，综合学习者学习行为类别差异、在线课程内容组织以及课程特点等要素为在线课堂进一步发展提供优化策略。     

融合大数据技术和工艺经验的高炉参数优化

 为了更精准地评判高炉运行情况，量化高炉指导方针。根据高炉生产过程的特点，应用大数据技术对高炉生产参数与铁水产量和高炉能耗等指标进行数据驱动分析，提出了一种优化高炉生产参数的新方法。首先，对某钢铁厂高炉各工序历史数据进行了采集、清洗、过滤和整合，建立了高炉数据仓库。然后，将多种聚类算法相结合，完成了对高炉炉况变化的详细划分。运用工艺经验与递归特征消除算法相结合，全面筛选得到能够反映炉况波动的强相关变量。应用统计学方法分析得出Class_a炉况对应的核心参数的最优范围，这对指导现场生产、保持高炉长期稳定顺行具有重要意义。     

Identifying poor metabolic adaptation during early lactation in dairy cows using cluster analysis

Currently, cows with poor metabolic adaptation during early lactation, or poor metabolic adaptation syndrome (PMAS), are often identified based on detection of hyperketonemia. Unfortunately, elevated blood ketones do not manifest consistently with indications of PMAS. Expected indicators of PMAS include elevated liver enzymes and bilirubin, decreased rumen fill, reduced rumen contractions, and a decrease in milk production. Cows with PMAS typically are higher producing, older cows that are earlier in lactation and have greater body condition score at the start of lactation. It was our aim to evaluate commonly used measures of metabolic health (input variables) that were available [i.e., blood β-hydroxybutyrate acid, milk fat:protein ratio, blood nonesterified fatty acids (NEFA)] to characterize PMAS. Bavarian farms (n = 26) with robotic milking systems were enrolled for weekly visits for an average of 6.7 wk. Physical examinations of the cows (5–50 d in milk) were performed by veterinarians during each visit, and blood and milk samples were collected. Resulting data included 790 observations from 312 cows (309 Simmental, 1 Red Holstein, 2 Holstein). Principal component analysis was conducted on the 3 input variables, followed by K-means cluster analysis of the first 2 orthogonal components. The 5 resulting clusters were then ascribed to low, intermediate, or high PMAS classes based on their degree of agreement with expected PMAS indicators and characteristics in comparison with other clusters. Results revealed that PMAS classes were most significantly associated with blood NEFA levels. Next, we evaluated NEFA values that classify observations into appropriate PMAS classes in this data set, which we called separation values. Our resulting NEFA separation values [<0.39 mmol/L (95% confidence limits = 0.360–0.410) to identify low PMAS observations and ≥0.7 mmol/L (95% confidence limits = 0.650–0.775) to identify high PMAS observations] were similar to values determined for Holsteins in conventional milking settings diagnosed with hyperketonemia and clinical symptoms such as anorexia and a reduction in milk yield, as reported in the literature. Future studies evaluating additional clinical and laboratory data, breeds, and milking systems are needed to validate these finding. The aim of future studies would be to build a PMAS prediction model to alert producers of cows needing attention and help evaluate on-farm metabolic health management at the herd level.