基于列式数据库的RDF数据分布式存储

由于RDF数据的爆炸式增长以及关系型数据库自身的瓶颈,使得几年来NOSQL数据库成为人们研究的热点,其中列式数据库尤为突出.在比较了目前流行的RDF存储方案前提下,提出了基于列式数据库的RDF数据分布式存储方案,同时还提出了列式数据库的SPARQL查询方案,最后通过实验对比了基于列式数据库与基于图数据库的RDF分布式数据存储方案的处理性能.     

云计算环境资源监控系统研究

为保障云计算平台的可靠性,基于Ganglia,采用Eucalyptus开源云平台,提出了云计算平台资源监控系统架构.从总体结构、监控信息处理流程等多个方面对监控系统进行了详细阐述,提出了动态更换虚拟机簇头的算法,并对算法与原型系统进行了验证和性能评测.分析与评测结果表明,所提系统能够适用于云计算环境资源信息的实时监测与预警,具备系统开销较低,用户接口友好的特点,能准确反映系统、虚拟机的负载信息,有助于云计算系统服务可靠性的提升.簇头动态更换算法能及时发现失效的簇头并更换为更健壮的虚拟机,保证了监测系统的正常运行.     

电容感应式架空输电线路覆冰厚度传感器的研究

利用同面多点电容感应技术,设计了一种实时测量输电线路覆冰厚度的传感器.传感器基于电容感应技术,通过测量模拟输电导线周围不同位置金属电极的电容值变化,并对电容值进行分析与计算,得出模拟导线上的覆冰厚度.通过对同面多个金属导线电极电场、电容的理论分析、仿真,以及低温下对系统的冰冻试验,验证了传感器在监测输电线路覆冰厚度的可行性.     

覆冰输电线路脱冰跳跃高度理论算法

建立连续档线路脱冰理论分析简化模型,基于导线脱冰跳跃过程中的能量关系、应力弧垂关系、变形关系和平衡关系,给出求解导线最大脱冰跳跃高度的理论计算方法.利用算例与数值模拟方法和Morgan理论算法进行比较,验证了方法的正确性.进而针对典型输电线路的脱冰跳跃问题,利用获得的理论算法、数值模拟方法与现行输电线路设计规程中的脱冰跳跃高度计算公式的计算结果进行比较分析,讨论了该理论算法的实用性.采用所获得的冰跳高度理论算法,可在冰区输电线路的设计中快速确定导线冰跳时的绝缘间隙.     

云计算服务用户持续使用的理论模型

通过梳理IS持续使用理论和服务管理中用户满意度研究的发展脉络,借鉴其成果并结合云计算信息系统的特点,提出了云计算服务用户满意度与持续使用的理论模型,并经过了实证检验.研究结果表明,云计算信息系统的系统质量和服务质量会影响用户的满意度,并且满意度与感知有用性一起影响用户的持续使用意图.使用IT知识存量和组织嵌入作为调节变量,分析了在不同的条件下,服务质量和系统质量等外生因素与用户满意度之间关系的变化.定义了云计算信息系统的系统质量、服务质量、用户感知有用性的测量维度和方法,这对云计算提供商有的放矢的提高系统质量和服务质量,提高云计算用户的有用性感知和满意度,进而促使其持续使用有直接的借鉴意义.     

一种基于大数据处理的异构私有云系统

云计算和大数据已成为IT领域的研究热点,如何将云计算在数据存储和数据处理方面的优势应用于大数据领域具有重要的实际应用价值.开源的云平台OpenStack可方便地从硬件管理方面构建私有云,其存储模块Swift能够支持PB级的大数据存储.开源的云平台Hadoop在数据处理方面具有很强的优势,但在支持超大数据存储方面存在不足.通过对OpenStack中的存储模块Swift和Hadoop中的文件处理模块HDFS的比较分析,提出了将Swift和Hadoop的MapReduce技术结合来构建企业处理大数据的私有云计算系统方案.分析结果显示该方案是可行的,这种异构的私有云系统可以整合不同云计算平台各自的优势进行高效的大数据处理.     

企业云计算应用中的安全风险防范

随着云计算的发展,越来越多的企业将存储和计算需求付诸于云端.但是对云计算的安全问题不容忽视,成为当前的一个研究热点.在企业云计算应用环境中,云计算的安全问题包括信息的存储和处理以及传输的保密性、完整性和可用性,对近年来企业云计算应用中的安全风险相关的研究成果进行总结.     

基于马尔科夫过程的储备系统稳态性能分析

由于储备系统组成部件在存储期间的失效概率各不相同,当部件状态趋于稳定时,各个状态对系统性能的影响也存在差异。为了识别关键部件及其状态对系统性能的影响程度,本文以重要度为主要指标,应用马尔科夫过程研究储备系统在稳态时的性能变化模式。首先基于综合重要度研究系统性能的变化规律,并结合冷储备系统和温储备系统的状态转移矩阵推导出马尔科夫过程中稳态值的计算方法;其次基于稳态综合重要度获得系统稳态时的性能变化模式;最后以双臂机器人为例,分析部件处于不同状态时对系统性能的影响模式,比较了不同部件综合重要度的变化,验证了提出方法的有效性。     

输电系统的部分可观Petri网故障诊断器设计

采用部分可观Petri网的故障诊断方法来解决变电站输电系统中不可观事件和不可观运行状态的故障诊断问题.首先,将系统可观测序列分解为长度为1的基础观测序列,应用线性不等式矩阵计算与基础观测序列相符的点火序列集;然后,基于整数线性规划问题,利用向前向后函数拓宽诊断区间,同时应用参数K限定故障诊断序列长度,通过分析系统可观事件和系统部分可观状态,给出故障诊断结果.最后,构造变电站输电系统的部分可观Petri网模型,应用提出的故障诊断算法对输电系统进行诊断,诊断结果准确给出了故障发生与否及故障发生位置.算法适用于在线故障诊断,计算复杂性线性相关于观序列长度.     

一种新的非线性系统传感器漂移性能退化实时监测方法

随着传感器使用时间的增加,其测量值会发生漂移,且漂移程度会随时间的增加而增大.针对非线性系统中的传感器漂移性退化现象,基于改进的粒子滤波算法,提出了传感器漂移性能退化实时监测方法.该方法通过实时估计漂移量来实现对系统状态的估计,能够检测并跟踪动态系统中传感器性能退化过程,为系统安全运行提供保障.基于三容水箱系统的仿真验证了该方法的有效性.     

Proximity relations in the fuzzy relational database model

The fuzzy relational model of Buckles and Petry is a rigorous scheme for incorporating non-ideal or fuzzy information in a relational database. In addition to providing a consistent scheme for representing fuzzy information in the relational structure, the model possesses two critical properties that hold for classical relational databases. These properties are that no two tuples have identical interpretations and each relational operation has a unique result.The fuzzy relational model relies on similarity relations for each scalar domain in the fuzzy database. These relations are reflexive, symmetric, and max-min transitive. In addition to introducing fuzziness into the relational model, each similarity relation induces equivalence classes in its domain. It is the existence of these equivalence classes that provides the model with the important properties possessed by classical relational databases.In this paper, we extend the fuzzy relational database model of Buckles and Petry to deal with proximity relations for scalar domains. Since reflexivity and symmetry are the only constraints placed on proximity relations, they generalize the notion of similarity relations. We show that it is possible to induce equivalence classes from proximity relations; thus, the ‘nice’ properties of the fuzzy relational model of Buckles and Petry are preserved. Furthermore, the removal of the max-min transitivity restriction also provides database users with more freedom to express their value structures.     

Fuzzy relational neural network

In this paper a fuzzy neural network based on a fuzzy relational “IF-THEN” reasoning scheme is designed. To define the structure of the model different t-norms and t-conorms are proposed. The fuzzification and the defuzzification phases are then added to the model so that we can consider the model like a controller. A learning algorithm to tune the parameters that is based on a back-propagation algorithm and a recursive pseudoinverse matrix technique is introduced. Different experiments on synthetic and benchmark data are made. Several results using the UCI repository of Machine learning database are showed for classification and approximation tasks. The model is also compared with some other methods known in literature.     

Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure

A new method for predicting failures of a partially observable system is presented. System deterioration is modeled as a hidden, 3-state continuous time homogeneous Markov process. States 0 and 1, which are not observable, represent good and warning conditions, respectively. Only the failure state 2 is assumed to be observable. The system is subject to condition monitoring at equidistant, discrete time epochs. The vector observation process is stochastically related to the system state. The objective is to develop a method for optimally predicting impending system failures. Model parameters are estimated using EM algorithm and a cost-optimal Bayesian fault prediction scheme is proposed. The method is illustrated using real data obtained from spectrometric analysis of oil samples collected at regular time epochs from transmission units of heavy hauler trucks used in mining industry. A comparison with other methods is given, which illustrates effectiveness of our approach.     

Security and efficiency data sharing scheme for cloud storage

With the adoption and diffusion of data sharing paradigm in cloud storage, there have been increasing demands and concerns for shared data security. Ciphertext Policy Attribute-Based Encryption (CP-ABE) is becoming a promising cryptographic solution to the security problem of shared data in cloud storage. However due to key escrow, backward security and inefficiency problems, existing CP-ABE schemes cannot be directly applied to cloud storage system. In this paper, an effective and secure access control scheme for shared data is proposed to solve those problems. The proposed scheme refines the security of existing CP-ABE based schemes. Specifically, key escrow and conclusion problem are addressed by dividing key generation center into several distributed semi-trusted parts. Moreover, secrecy revocation algorithm is proposed to address not only back secrecy but efficient problem in existing CP-ABE based scheme. Furthermore, security and performance analyses indicate that the proposed scheme is both secure and efficient for cloud storage.     

基于ANDROID的LTE冰水情自动监测客户端设计

在冰水情信息监测工作中,数据实时远程传送和现场远程实时监控、告警是能否准确实现预测、报警的关键因素.随着通信技术的发展,LTE(第四代移动通信技术)系统已大规模商用,在基于LTE网络架构的冰水情灾害自动监测设备中,利用大带宽高传输速率的优势,视频、图像数据的实时传送成为现实.在应用中,远程智能手机客户端的开发成为一个重要问题.基于Android(安卓手机操作系统)系统的LTE冰水情自动监测客户端具备较强的稳定性、可靠性、可移植性,能够在很好的在满足用户移动性的同时,实现远程监控、告警功能.能够为LTE冰水情灾害自动监测设备远程用户端提供一个有效的解决方案.     

Enhancing the generality of fuzzy relational models for control

A promising area of research in fuzzy control is the model-based fuzzy controller. At the heart of this approach is a fuzzy relational model of the process to be controlled. Since this model is identified directly from process input-output data it is likely that ‘holes’ will be present in the identified relational model. These holes are real problems when the model is incorporated into a model-based controller since the model will be unable to make any predictions whatsoever if the system drifts into an unknown region. The present work deals with the completeness of the fuzzy relational model which forms the core of the controller. This work proposes a scheme of post-processing to ‘fiil in’ the fuzzy relational model once it has been built and thereby improve its applicability for on-line control. A comparative study of the post-processed model and conventional relational model is presented for Box-Jenkins data identification system and a real-time, highly non-linear application of pH control identification.     

Sliding mode control based on multi-node transmission hybrid scheduling for Markovian jump systems with constrained bandwidth

The sliding mode control (SMC) problem is investigated for Markovian jump systems (MJSs) under constrained communication bandwidth. A multi-node hybrid transmission strategy composed of an event-triggered protocol and the weight try-once-discard (WTOD) protocol is introduced into the sensor-to-controller (S/C) channel. Its key feature is that by using two dynamic thresholds, the number of the transmitted components may be dynamically regulated, not just the one with the largest difference as in the conventional WTOD protocol. That may greatly increase the flexibility of transmission under limited bandwidth, meanwhile, it is also beneficial to balance system performance and network burden. Then, a compensating strategy is proposed via the previous transmitted signals, and a scheduling signal-dependent sliding mode controller is designed. By using mode-dependent Lyapunov function, both the stochastic stability and the reachability are analyzed under different transmission cases, respectively. Moreover, an optimization problem on convergent domain is formulated and the binary-encoded genetic algorithm (GA) is utilized to search a desirable sliding gain. Finally, the proposed multi-node hybrid scheduling-based SMC scheme is illustrated via simulation results.     

Towards optimal workload-aware XML to relational schema mapping

Storing XML documents in relational databases has drawn much attention in recent years because it can leverage existing investments in relational database technologies. Different algorithms have been proposed to map XML DTD/Schema to relational schema in order to store XML data in relational databases. However, most work defines mapping rules based on heuristics without considering application characteristics, hence fails to produce efficient relational schema for various applications. In this paper, we propose a workload-aware approach to generate relational schema from XML data and user specified workload. Our approach adopts the genetic algorithm to find optimal mappings. An elegant encoding method and related operations are proposed to manipulate mappings using bit strings. Various techniques for optimization can be applied to the XML to relational mapping problem based on this representation. We implemented the proposed algorithm and our experiment results showed that our algorithm was more robust and produced better mappings than existing work.