自动引导车电池SOC估算方法研究

自动引导车(AGV车)工况特殊,电流积分法估算电池剩余容量(SOC)误差较大,而且存在累积误差。为了提高AGV车电池剩余容量估算的准确度,对扩展卡尔曼滤波法估算AGV车电池剩余容量进行了研究,分析了AGV车特殊工况,提出将扩展卡尔曼滤波法的滤波增益改进为动态调整滤波增益,有效提高扩展卡尔曼滤波法的跟踪效果。实验表明使用扩展卡尔曼滤波法估算AGV车电池剩余容量精度较高,采用动态校正的滤波增益提高了估算过程的跟踪效果,解决了AGV车电池剩余容量估算不准确的问题。     

基于相场方法的孔隙尺度油水两相流体流动模拟

基于真实岩心颗粒粒径分布,利用过程法构建疏松砂岩油藏的三维孔隙结构模型,利用相场方法建立两相流体流动数学模型并利用有限元方法进行求解,研究驱替速度、流体性质、润湿性对剩余油分布以及采出程度的影响.结果表明：驱替速度的增大和油水粘度比的减小会导致较大的毛管数,进而有利于采出程度的提高;就润湿性而言,水湿条件下毛管力是水驱油的动力,而在油湿条件下是阻力,因此水湿岩心采出程度更高.同时,从孔隙尺度对油水渗流机理及剩余油分布机理进行揭示,结果表明：由于多孔介质的复杂孔隙结构,流体在流经不同孔隙时呈现不同的流动特征,进而对油水两相流整体的压力分布、流速分布造成重要影响.     

Some new approaches to multivariate probability distributions

We extend and generalize to the multivariate set-up our earlier investigations related to expected remaining life functions and general hazard measures including representations and stability theorems for arbitrary probability distributions in terms of these concepts. (The univariate case is discussed in detail in Kotz and Shanbhag, Advan. Appl. Probab. 12 (1980), 903–921.)     

二类油层高含水开发后期优化补孔措施挖潜剩余油研究

根据大庆油田采油二厂提供的动、静态数据,用数值模拟方法,分析了其南八区西块二类油层的剩余油分布.针对其剩余油的挖潜设计了注水时机、注水强度、注水时机和注水强度优化三套30个补孔方案,优化结果是:注水强度9最好,注水时机含水93%最好,注水强度9和注水时机93%结合优化效果最好.     

隧道衬砌的火灾损伤程度试验与检测方法研究

本文采用不同火灾温度场与不同燃烧时间对隧道衬砌混凝土和构件进行实际 烧损的试验手段,探讨了衬砌在高温作用后力学性能的变化规律与损伤程度;分析了隧道高温受损后,衬砌残余强度与超声波速之间的关系,以及超声法检测技术用于测试隧道火灾损伤的适用性和可靠性。     

磁声发射在钢轨性能无损检测中的应用研究

根据磁声发射（ＭＡＥ）强度与应力的依从关系，对退火钢轨进行了应力作定，并测量了Ｕ７４新轨的残余应力，又根据ＭＡＥ强度随材料相对疲劳度的变化规律，探讨了用ＭＡＥ法估测风轨剩余寿命的可行性。     

Heavy-tailed limits for medium size jobs and comparison scheduling

We study the conditional sojourn time distributions of processor sharing (PS), foreground background processor sharing (FBPS) and shortest remaining processing time first (SRPT) scheduling disciplines on an event where the job size of a customer arriving in stationarity is smaller than exactly k≥0 out of the preceding m≥k arrivals. Then, conditioning on the preceding event, the sojourn time distribution of this newly arriving customer behaves asymptotically the same as if the customer were served in isolation with a server of rate (1−ρ)/(k+1) for PS/FBPS, and (1−ρ) for SRPT, respectively, where ρ is the traffic intensity. Hence, the introduced notion of conditional limits allows us to distinguish the asymptotic performance of the studied schedulers by showing that SRPT exhibits considerably better asymptotic behavior for relatively smaller jobs than PS/FBPS. Inspired by the preceding results, we propose an approximation to the SRPT discipline based on a novel adaptive job grouping mechanism that uses relative size comparison of a newly arriving job to the preceding m arrivals. Specifically, if the newly arriving job is smaller than k and larger than m−k of the previous m jobs, it is routed into class k. Then, the classes of smaller jobs are served with higher priorities using the static priority scheduling. The good performance of this mechanism, even for a small number of classes m+1, is demonstrated using the asymptotic queueing analysis under the heavy-tailed job requirements. We also discuss refinements of the comparison grouping mechanism that improve the accuracy of job classification at the expense of a small additional complexity. This work is supported by NSF Grant 0615126.     

国债远期利率的量子场理论模型构建

随着我国利率市场化改革的全面推进和利率衍生品数量的增加,如何对远期利率进行精确与合理建模,就显得十分重要和紧迫.本文利用金融物理学中可有效纳入日历时间和到期时间两个维度上的国债远期利率之间不完全相关性的量子场论方法,对2011年1月4日到2016年12月30日的国债瞬时远期利率的实际市场演化进行建模,并将其结果与传统金融只能考虑日历时间方向上的相关性的主流两因子Heath-Jarrow-Morton (HJM)模型的实证结果进行比较.研究结果表明,考虑心理感知剩余时间变量后的量子场理论模型,提供了对实际的国债远期利率的92.67%的拟合优度,优于经典的最优两因子HJM模型69.02%的拟合精度.此外,分别将估计所得的最优参数代入最优量子场理论模型和两因子HJM模型下的远期利率更新方程,对2017年1月3日到2017年12月30日的100个期限的瞬时远期利率的250个瞬时远期利率的期限结构进行回测检验,从平均瞬时远期利率、均方根误差和Theil不等系数三个方面的结果均显示出量子场理论模型对国债远期利率建模的优越性.这些结果对将量子场理论引入到以国债为标的各种金融产品的定价和相关的利率风险管理、银行和金融公司的量化分析以及固定收益证券领域的实践者们均具有重要意义.     

The Remaining Service Time upon Reaching a High Level in M/G/1 Queues

The distribution of the remaining service time upon reaching some target level in an M/G/1 queue is of theoretical as well as practical interest. In general, this distribution depends on the initial level as well as on the target level, say, B. Two initial levels are of particular interest, namely, level 1 (i.e., upon arrival to an empty system) and level B–1 (i.e., upon departure at the target level).In this paper, we consider a busy cycle and show that the remaining service time distribution, upon reaching a high level B due to an arrival, converges to a limiting distribution for B. We determine this asymptotic distribution upon the first hit (i.e., starting with an arrival to an empty system) and upon subsequent hits (i.e., starting with a departure at the target) into a high target level B. The form of the limiting (asymptotic) distribution of the remaining service time depends on whether the system is stable or not. The asymptotic analysis in this paper also enables us to obtain good analytical approximations of interesting quantities associated with rare events, such as overflow probabilities.     

Queues with Equally Heavy Sojourn Time and Service Requirement Distributions

For the G/G/1 queue with First-Come First-Served, it is well known that the tail of the sojourn time distribution is heavier than the tail of the service requirement distribution when the latter has a regularly varying tail. In contrast, for the M/G/1 queue with Processor Sharing, Zwart and Boxma [26] showed that under the same assumptions on the service requirement distribution, the two tails are equally heavy. By means of a probabilistic analysis we provide a new insightful proof of this result, allowing for the slightly weaker assumption of service requirement distributions with a tail of intermediate regular variation. The new approach allows us to also establish the tail equivalence for two other service disciplines: Foreground–Background Processor Sharing and Shortest Remaining Processing Time. The method can also be applied to more complicated models, for which no explicit formulas exist for (transforms of) the sojourn time distribution. One such model is the M/G/1 Processor Sharing queue with service that is subject to random interruptions. The latter model is of particular interest for the performance analysis of communication networks.