Reward optimization of a repairable system

This paper analyzes a system subject to repairable and non-repairable failures. Non-repairable failures lead to replacement of the system. Repairable failures, first lead to repair but they lead to replacement after a fixed number of repairs. Operating and repair times follow phase type distributions (PH-distributions) and the pattern of the operating times is modelled by a geometric process. In this context, the problem is to find the optimal number of repairs, which maximizes the long-run average reward per unit time. To this end, the optimal number is determined and it is obtained by efficient numerical procedures.     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

A study of replacement rules for a parallel fleet replacement problem based on user preference utilization pattern and alternative fuel considerations

Parallel fleet replacement problems deal with determining an optimal replacement schedule that results in a minimal total cost of owning and operating a fleet within a finite planning horizon. In this paper, the fleet consists of service vehicles, varying in age and cumulative mileage, commonly owned by government agencies or private business organizations. The paper focuses on three main aspects of the problem, including the effects of some widely used replacement rules, consideration of alternative fuel vehicles for replacement, and a new “user preference” utilization pattern. The vehicle replacement rules under study include purchase-new-vehicles-only, no-splitting-in-selling, one-purchase-choice, older-vehicles-selling, and all-or-none rules. The initial fleet consists of gasoline vehicles, while alternative-fuel vehicles using either compressed natural gas (CNG) or liquefied petroleum gas (LPG) are included as challengers in every replacement decision period. The user preference utilization pattern denotes a pattern in which yearly usage of vehicles declines as the vehicles grow older or have higher cumulative mileage. Integer programming formulation of the defined problem that incorporates these new factors is provided. Small numerical examples under various scenarios and a large problem instance with model parameters estimated from actual data are solved to optimality to gain some insights about the replacement rules and other considered factors.     

Deletion, replacement and mean-shift for diagnostics in linear mixed models

Deletion, replacement and mean-shift model are three approaches frequently used to detect influential observations and outliers. For general linear model with known covariance matrix, it is known that these three approaches lead to the same update formulae for the estimates of the regression coefficients. However if the covariance matrix is indexed by some unknown parameters which also need to be estimated, the situation is unclear. In this paper, we show under a common subclass of linear mixed models that the three approaches are no longer equivalent. For maximum likelihood estimation, replacement is equivalent to mean-shift model but both are not equivalent to case deletion. For restricted maximum likelihood estimation, mean-shift model is equivalent to case deletion but both are not equivalent to replacement. We also demonstrate with real data that misuse of replacement and mean-shift model in place of case deletion can lead to incorrect results.     

位置相关信息服务移动终端缓存替换研究

随着无线网络的快速发展,相应的无线应用服务越来越多,位置相关信息服务(LDISs)就是其中典型的例子。无线网络受信道带宽以及终端计算能力等因素的限制,缓存技术减少了网络开销并且支持在网络断接下使用数据的优点而成为研究热点。本文根据无线应用环境的特点,结合位置相关信息数据的特征,运用粗糙集(RoughSet,RS)这一强有力的数学工具,给出一种更加准确、实用的移动缓存替换策略。     

国内在役核电厂首例电气贯穿件更换改造

核电厂电气贯穿件作为安全壳上的关键设备,承担着核岛内外各种电力和信号传输以及保证安全壳压力边界完整性的重要功能。通过秦山核电厂一期工程30万千瓦机组第18次大修期间国产在役DDG-1型电气贯穿件更换改造项目的实施,分析了秦山核电厂一期工程在役电气贯穿件设备现状和改造的必要性;针对在役核电厂更换改造工期短和贯穿件密封性能验证难等问题,通过优化检验工序、制作专用检漏工装的方法,缩短了贯穿件改造的工期并验证了贯穿件密封性能。     

流媒体对象的缓存管理策略

基于流媒体服务的代理技术是流媒体研究领域中的重要课题.随着流媒体技术在Internet和无线网络环境中的高速发展,对流媒体代理服务器的研究也正在逐步深入.本文主要讨论通过代理技术改善媒体的服务质量,降低媒体的传输延迟以及减轻网络负载.在Internet环境下,对流媒体代理服务器的研究集中于流媒体的访问特性、缓存替换算法,构建和实现一个流媒体代理服务器是对流媒体代理技术研究的基础.     

一种面向固态硬盘的Cache置换算法

固体硬盘的出现改变了现有存储子系统的框架,也逐渐成为了嵌入式应用的主流存储设备.但是其自身的写机制由于需要先擦后写,成为了影响SSD性能的瓶颈.针对此问题,出于减少写操作次数这一思路提出了LRU-AB算法,力图在基于Cache数据的访问频度上改进SSD置换算法.同时对现有算法进行了分析.     

CFCs和HCFCs的替代

由R134a、R600a、R290和R600a／R290混合物替代制冷剂R12已为国际公认，其他CFCS的替代已基本趋于统一；HCFCS已出现最有希望的14种替代物，其中R407c、R410a备受关注。制冷剂的替代出现了多元化、区域化趋势。