智能空间——智能建筑的延伸

智能建筑在全球范围广泛兴起,其主要实现形式是集成现有的各种智能化子系统。普适计算的发展推动信息技术革命,同时也给建筑带来深刻变化,智能空间就是普适计算与建筑结合的产物,其特点包括空间的伸缩性、信息技术的广泛应用、信息空间与物理空间的融合,同时也是智能建筑的延伸(从静态到动态的延伸、从有形到无形的延伸、从被动到主动的延伸)。     

高层建筑结构侧移刚度矩阵计算方法的探讨

本文发展了高层建筑结构侧移刚度矩阵计算的几种方法,通过算例,对几种方法的计算误差、计算机耗时、占用计算机内存等进行了讨论.     

基于普及计算基础上的智能建筑未来展望

论述了智能建筑的发展概况，介绍了智能建筑的发展趋势，即普及计算与建筑密切结合，会促成自主管理和控制智能建筑的产生，并对可能出现的问题进行探讨。     

军用飞机PHM技术进展分析及问题研究

在研究故障预测与健康管理(PHM)技术体系构建、数据采集与运用、系统状态监测与故障预测、验证评估等发展现状的基础上,结合新阶段装备PHM工程应用现状,从装备测试性设计与验证、多源故障信息交互与管理、寿命预测与维修支持、验证评估及标准制定等方面分析军用飞机PHM技术面临的瓶颈问题,并指出应重点开展包括测试性试验技术、智能信息处理与融合技术、航空装备与信息标准化管理技术、数据挖掘与平台构建技术等方面的研究,最后对我国PHM技术的发展给出思考与建议。     

A Heuristic Combinatorial Optimization Algorithm for Load-Leveling and Peak Demand Reduction using Energy Storage Systems

A method for applying combinatorial optimization algorithms to Energy Storage System (ESS) scheduling is presented in this paper. Scheduling is essential for the integration of ESS in electrical networks at grid level or at consumer level to achieve the objectives of integration such as constraint management or energy cost reduction and for efficient storage dispatch. It also shows that for a time-of-use (ToU) tariff scheme based on the shape of the demand profile with higher prices tied to peak periods, effective load-leveling, and peak demand reduction always leads to energy cost reduction. While other methods usually require more information such as generation cost curves or ToU tariffs to schedule ESS, the proposed method uses only demand profile information and ESS parameters to achieve load-leveling and peak demand reduction and also considers the entire optimization time horizon. This is done by combining heuristic bin packing and subset sum algorithms with specific modifications to the standard forms and through transformations. A case study is presented in which the algorithm is used to schedule household ESS with repurposed electric vehicle batteries and the results are compared to a demand response scheme on the same setup.     

云虚拟机资源负载均衡的任务调度研究

针对云计算中现有任务调度模型为追求最短完成时间,而没有从物理资源的负载均衡角度考虑, 提出了基于任务预先分类的调度策略(PCSS)。该策略通过综合考虑任务的最少执行时间和截止时间来设置优先级,根据优先级别将任务批量分配给合适的物理机,实现了任务的最短完成时间和物理机的负载均衡。最后通过Cloudsim 仿真实验分析和比较,该策略能很好地执行用户任务并体现出良好的负载均衡。     

电子计价秤测量结果的不确定度评定

本文主要就使用电子计价秤进行称重计量得到的测量结果的不确定度做些研究与分析,介绍了相关技术法规、测量原理及数学模型,重点论述了测量结果不确定度的来源和评定问题。     

基于云计算的模拟飞行训练体系架构设计

为满足我军模拟飞行训练不断提升的训练需求,设计一种基于云计算的模拟飞行训练体系架构。根据云 计算特点,提出面向服务的飞行仿真云概念,依托超高性能网络,构建模拟飞行训练体系。采用传统仿真技术,应 用分布计算、分布存储、分布缓存、数据同步、空间一致等手段对其进行设计,并对体系的优势进行比较分析。应 用结果验证了该体系可实现模拟飞行训练资源的高可用率、模拟器各分系统的可靠计算和数据存储。     

Context‐aware Android applications through transportation mode detection techniques

In this paper, we study the problem of how to detect the current transportation mode of the user from the smartphone sensors data, because this issue is considered crucial for the deployment of a multitude of mobility‐aware systems, ranging from trace collectors to health monitoring and urban sensing systems. Although some feasibility studies have been performed in the literature, most of the proposed systems rely on the utilization of the GPS and on computational expensive algorithms that do not take into account the limited resources of mobile phones. On the opposite, this paper focuses on the design and implementation of a feasible and efficient detection system that takes into account both the issues of accuracy of classification and of energy consumption. To this purpose, we propose the utilization of embedded sensor data (accelerometer/gyroscope) with a novel meta‐classifier based on a cascading technique, and we show that our combined approach can provide similar performance than a GPS‐based classifier, but introducing also the possibility to control the computational load based on requested confidence. We describe the implementation of the proposed system into an Android framework that can be leveraged by third‐part mobile applications to access context‐aware information in a transparent way. Copyright © 2016 John Wiley & Sons, Ltd.     

IP2DM: integrated privacy‐preserving data management architecture for smart grid V2G networks

With the development of battery vehicles, vehicle‐to‐grid (V2G) networks are becoming more and more important in smart grid. Although battery vehicles are environmentally friendly and flexible to use two‐way communication and two‐way electricity flow, they also raise privacy‐preservation challenges, such as location and movement privacy. On the one hand, utility companies have to monitor the grid and analyze user data to control the power production, distribution, scheduling, and billing process, while typical users need to access their data later online. On the other hand, users are not willing to provide their personal data because they do not trust the system security of the utility companies where their data stored, and it may potentially expose their privacy. Therefore, in this paper, we study data management of V2G networks in smart grid with privacy‐preservation to benefit both the customers and the utility companies. Both data aggregation and data publication of V2G networks are protected in the proposed architecture. To check its security, we analyze this architecture in several typical V2G networks attacks. We conduct several experiments to show that the proposed architecture is effective and efficient, and it can enhance user privacy protection while providing enough information for utility companies to analyze and monitor the grid. Copyright © 2016 John Wiley & Sons, Ltd.