仙游抽水蓄能电站尾水系统充排水试验

为检查工程质量,查找输水系统可能存在的问题并消除隐患,保障电站安全运行,福建仙游抽水蓄能电站在投产发电前,要对输水发电系统进行充水试验与放空排水试验,在试验过程中监测输水系统的应力、应变与渗漏水情况。本文以尾水系统为例,介绍了试验所应具备的工程条件、工作程序及技术要求、试验成果分析和存在问题处理等。试验表明,仙游抽水蓄能电站尾水系统设计合理,施工质量优良,结构可靠,可供同类工程参考与借鉴。     

机载光电雷达它机试飞技术研究

机载光电雷达受平台及大气环境影响较大,而它机试飞能有效规避研制风险,是产品研制的必经阶段。本文介绍了机载光电雷达它机试飞试验系统组成、试验流程和数据处理等内容。经工程验证,实施过程满足任务需求,对其他光电系统的它机及本机试飞具有参考价值。     

Supramolecular Glyco‐nanoparticles Toward Immunological Applications

Glyco‐mimicking nanoparticles (glyco‐NPs) with Förster resonance energy transfer (FRET) donor and acceptor groups formed via dynamic covalent bond of benzoboroxole and sugar from two complementary polymers are prepared. The glyco‐NPs are proved to be quite stable under physiological conditions but sensitive to pH. So the glyco‐NPs can be internalized by dendritic cells with integrity and nontoxicity and then dissociate within the acidic organelles. This particle dissociation is directly observed and visualized in vitro, for the first time via the FRET measurements and fluorescent microscopy. This feature makes controlled release of drug or protein by glyco‐NPs possible, i.e., when model antigen Ovalbumin is loaded in the glyco‐NPs, the released Ovalbumin in dendritic cells stimulates T cells more efficiently than the free Ovalbumin itself as a result of the enhanced antigen processing and presentation. Thus, the results enlighten a bright future of the glyco‐NPs in immunotherapy.     

The World robot summit disaster robotics category – achievements of the 2018 preliminary competition

The World Robot Summit is a robot Olympics and aims to be held in a different country every four years from 2020. The concept of the Plant Disaster Prevention challenge is daily inspections, checks, and emergency response in industrial plants, and in this competition, robots must carry out these types of missions in a mock-up plant. The concept of the Tunnel Disaster Response and Recovery challenge is emergency response to tunnel disasters, and is a simulation competition whereby teams compete to show their ability to deal with disasters, by collecting information and removing debris. The Standard Disaster Robotics challenge assesses, in the form of a contest, the standard performance levels of a robot that are necessary for disaster prevention and emergency response. The World Robot Summit Preliminary Competition was held at Tokyo Big Sight in October 2018, and 36 teams participated in the Disaster Robotics Category. UGVs and UAVs contended the merits of new technology for solving complex problems, using core technologies such as mobility, sensing, recognition, performing operations, human interface, autonomous intelligence etc., as well as system integration and implementation of strategies for completing missions, gaining high-level results.     

基于学生群体心理测试系统的设计

心理测试的根本目的是为了让人们在测试过程中，产生某些特定行为，即个体对测试题目的反应，并根据这些行为反应来推论其相应的心理特性。在一定程度上帮助我们了解人类的心理活动，并从中得到帮助。因此，本文对基于学生群体心理测试系统的设计进行深入研究。     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

大牛地气田异常修正等时试井资料求取产能方法

大牛地气田为低渗-致密气田,因存在启动压力梯度等因素,修正等时试井曲线按常规方法处理出现异常,无法准确获取气井的产能方程.采用LIT法和异常曲线校正法对气田异常试井资料进行了重新处理,最终获得气井产能方程,为同类气藏产能试井可能出现的异常资料处理解释探索了新的途径.     

地层测试的压力"下滑恢复"响应分析

在油气井的试油测试资料中，压力恢复曲线有时会出现下滑现象，常被认为是施工失误所致。本文以实际资料为例，分析了“下滑恢复”的压力响应特征和影响因素，认为只在低渗透地层有可能产生“下滑恢复”现象，并提出了避免“下滑恢复”的施工建议。     

Effective Location Based Services with Dynamic Data Management in Mobile Environments

With the proliferation of mobile computing technologies, location based services have been identified as one of the most promising target application. We classify mobile information service domains based on feature characteristics of the information sources and different patterns of mobile information access. By carefully examining the service requirements, we identify the dynamic data management problem that must be addressed for effective location based services in mobile environments. We then devise a general architecture and cost model for servicing both location independent and location dependent data. Based on the architecture and cost model, we propose a set of dynamic data management strategies that employs judicious caching, proactive server pushing and neighborhood replication to reduce service cost and improve response time under changing user mobility and access patterns. Detail behavior analysis helps us in precisely capturing when and how to apply these strategies. Simulation results suggest that different strategies are effective for different types of data in response to different patterns of movement and information access. Shiow-yang Wu is an associate professor of the Department of Computer Science and Information Engineering at National Dong Hwa University, Hualien, Taiwan, R.O.C. He received the BS and MS degrees in computer engineering from National Chiao Tung University, Hsinchu, Taiwan, ROC, and the PhD degree in computer science from the University of Texas at Austin in 1984, 1986, and 1995, respectively. His research interests include data/knowledge bases, mobile computing, distributed processing, intelligence information systems, and electronic commerce. Kun-Ta Wu was born in Taipei, Taiwan, R.O.C., in 1976. He received the B.S. degree in computer science from Soochow University, Taipei, Taiwan, R.O.C., in 1999 and the M.S. degree in computer science and information engineering from National Dong Hwa University, Hualien, Taiwan, R.O.C., in 2001. Currently, he is an Assistant Researcher in the Domestic Division at Science and Technology Information Center, National Science Council, R.O.C., as a member of Information Gathering and Analysis Group of National Information and Communication Security Taskforce. His research interests include mobile computing, wireless network and information security.     

横泉水库大坝土料碾压试验

在取得土料场土料物理力学性质参数的情况下，遵循碾压试验基本原理、方法及参数组合，进行了现场碾压试验，通过试验成果的分析，得出了大坝碾压试验结论，为大坝土方填筑提供了技术依据。