确保时态一致性的实时并发控制协议

实时控制系统通常需要维护大量的时态数据对象,这些时态数据对象是现实世界不断变化的物理环境的逻辑表示.应用事务通过对时态数据对象的访问来产生最终的控制活动.在实际应用中,实时控制系统不仅需要确保事务能在规定的期限内完成,也需要保证事务存取的是时态一致性的数据对象.传统的实时并发控制协议强调事务截止期的满足,而忽略了事务对其所存取数据对象有时态一致性需求.本文首先给出了数据和事务时态一致性的形式化定义,在此基础上,提出了一种新的能确保时态一致性的实时并发控制协议:时态一致性高优先级两段锁协议(TCHP-2PL).为了实现更好的实时性能,我们对TCHP-2PL进行了改进,通过引入相似性概念,提出了TCHP-2PL的改进版本:STCHP-2PL.性能测试结果显示,STCHP-2PL在确保时态一致性的同时仍能保证很好的实时性能.     

Estimating Spatial Changes Over Time of Arctic Sea Ice using Hidden 2×2 Tables

Arctic sea ice extent has been of considerable interest to scientists in recent years, mainly due to its decreasing temporal trend over the past 20 years. In this article, we propose a hierarchical spatio‐temporal generalized linear model for binary Arctic sea‐ice‐extent data, where statistical dependencies in the data are modeled through a latent spatio‐temporal linear mixed effects model. By using a fixed number of spatial basis functions, the resulting model achieves both dimension reduction and non‐stationarity for spatial fields at different time points. An EM algorithm is proposed to estimate model parameters, and an empirical–hierarchical‐modeling approach is applied to obtain the predictive distribution of the latent spatio‐temporal process. We illustrate the accuracy of the parameter estimation through a simulation study. The hierarchical model is applied to spatial Arctic sea‐ice‐extent data in the month of September for 20 years in the recent past, where several posterior summaries are obtained to detect the changes of Arctic sea ice cover. In particular, we consider a time series of latent 2 × 2 tables to infer the spatial changes of Arctic sea ice over time.     

Simulation Models for Investigation of Multiuser Scheduling in MIMO Broadcast Channels

Spatial correlation is a result of insufficient antenna spacing among multiple antenna elements, while temporal correlation is caused by Doppler spread. This paper compares the effect of spatial and temporal correlation in order to investigate the performance of multiuser scheduling algorithms in multiple‐input multiple‐output (MIMO) broadcast channels. This comparison includes the effect on the ergodic capacity, on fairness among users, and on the sum‐rate capacity of a multiuser scheduling algorithm utilizing statistical channel state information in spatio‐temporally correlated MIMO broadcast channels. Numerical results demonstrate that temporal correlation is more meaningful than spatial correlation in view of the multiuser scheduling algorithm in MIMO broadcast channels. Indeed, the multiuser scheduling algorithm can reduce the effect of the Doppler spread if it exploits the information of temporal correlation appropriately. However, the effect of spatial correlation can be minimized if the antenna spacing is sufficient in rich scattering MIMO channels regardless of the multiuser scheduling algorithm used.     

A portable functional imaging instrument for psychology research based on near-infrared spectroscopy

To aid psychology researches on the function of the prefrontal cortex, a 16-channel brain functional imaging instrument based on near-infrared spectroscopy is developed. The probe of the instrument, covering a detection area of 15 cm x 4.4 cm of the prefrontal cortex, is made flexible and is easy to fix to the forehead. By employing multi-wavelength LEDs as light sources, using integrated detectors and choosing a USB-interface-based data acquisition device, the whole system is portable and convenient to use, which is good for psychological experiments. The system software is developed using Visual C++ 6.0, which controls the imaging process, measurement data plotting and storage. The maximal temporal resolution is about 100 ms. Noise and long-term drift test are given. The Valsalva maneuver experiment is used to validate the reliability of the instrument for monitoring hemodynamic changes.     

RDF partitioning for scalable SPARQL query processing

The volume of RDF data increases dramatically within recent years, while cloud computing platforms like Hadoop are supposed to be a good choice for processing queries over huge data sets for their wonderful scalability. Previous work on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect system performance. Specifically, a good partitioning solution would greatly reduce or even totally avoid cross-node joins, and significantly cut down the cost in query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture, where Map/Reduce takes charge of the computing tasks, and RDF query engines like RDF-3X store the data and execute join operations. According to the analysis of query workloads, this work proposes a novel algorithm for automatically partitioning RDF data and an approximate solution to physically place the partitions in order to reduce data redundancy. It also discusses how to make a good trade-off between query evaluation efficiency and data redundancy. All of these proposed approaches have been evaluated by extensive experiments over large RDF data sets.     

Analyzing Method of Temporal Brain Activation in fMRI Measurements

In this paper, we propose a method to acquire the temporal changes of activations by combining a slide of the analysis time window and t‐value. An advantage of this method is that it can acquire rough changes of the activated areas even with data having low time resolution. Nine normal subjects participated in the study of the visual oddball paradigm, which consisted of a random series of 30 targets and 30 control stimuli. Here, the subjects were instructed to push a button as the go‐task when a target stimulus occurred. We investigated the activated area in three types of analysis time sections: from stimulus onset to 5 s after the stimulus (time section A; TS‐A); from 5 to 10 s (TS‐B); and from 10 to 15 s (TS‐C). We also investigated the temporal change of the t‐value between go and no‐go responses in blood oxygenation level dependent (BOLD) signals. In this study, we picked up the following eight representative areas corresponding with previous reports: left primary motor area and left somatosensory motor area, supramarginal gyri (SMG), right superior parietal lobule, right inferior frontal gyrus, cerebellum (cerebellar tonsil), and right medial frontal gyrus. Among them, the left primary motor area and left somatosensory motor area showed prominent activation (t > 10) in TS‐A and TS‐B, respectively. Activity at the left supramarginal gyrus decreased gradually with time, and right insula showed constant activation through all time sections. We could see various patterns on temporal activation at different positions in the brain. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

电动汽车充电负荷时空分布及其对配电网的影响

电动汽车充电负荷因受诸多因素影响而充满随机性与复杂性,随着电动汽车的规模化应用,势必会给配电网带来一系列问题,这也成为目前国内外学者和相关科研机构的研究热点。基于电动汽车充电运行数据,选取充电起始时间和充电持续时间作为影响充电负荷时间分布的关键因素,结合用户充电行为的不确定性分析,建立电动汽车充电负荷的时间分布模型。以此为根据,对充电负荷区域进行划分,建立电动汽车充电负荷的时空分布模型。通过算例分析,研究电动汽车充电负荷对配电网运行中的电压质量、网络损耗及日负荷曲线的影响。     

基于时序概率超图模型的视频多语义标注

在基于语义的视频检索系统中,为了弥补视频底层特征与高层用户需求之间的差异,提出了时序概率超图模型。它将时间序列因素融入到模型的构建中,在此基础上提出了一种基于时序概率超图模型的视频多语义标注框架（TPH-VMLAF）。该框架结合视频时间相关性,通过使用基于时序概率超图的镜头多标签半监督分类学习算法对视频镜头进行多语义标注。标注过程中同时解决了已标注视频数据不足和多语义标注的问题。实验结果表明,该框架提高了标注的精确度,表现出了良好的性能。     

P2P TV在线用户的时空分布研究

针对P2P TV在线用户的匿名性和高度动态性,设计和部署了P2P TV爬行器--TVCrawler对主流的大规模P2P IPTV系统进行主动测量,对比研究了不同P2P TV系统中在线人数的时间演化和地理分布规律。首先,对在线用户数量的在时间上的演化规律进行了直观分析,并通过多尺度熵方法研究了在线用户人数时间序列的复杂性;其次,研究了在线用户在地理空间分布上的分布特点和规律,并对用户的地理分布进行了可视化展现;最后还研究了各省级行政区内的频道在线用户与当地经济发展水平的关系,发现二者之间存在较为显著的线性递减关系。     

基于时序逻辑的可信取证工作流模型研究

在计算机取证领域,许多取证技术在实际的取证过程中已经得到了成功有效的应用。但对所得到的电子证据的有效性或可信性方面还存在明显的问题,文章在此背景下提出了电子证据的可信取证理念,并对相关问题进行了研究。结合工作流技术及时序逻辑知识构建基于时序逻辑的可信取证工作流模型。研究了构建该取证工作流体系所需用到的关键技术、取证工作流模型的构建过程和原理,对于可信取证进一步发展具有一定帮助。