基于减函数的多层贝叶斯离散型软件可靠性验证测试方案

随着计算机软件的广泛应用,由于软件失效而导致的航空系统异常越发频繁。因此,通过软件可靠性验证测试(SRDT)来客观评估软件可靠性水平是否满足指标要求,已经引起人们的关注。基于贝叶斯理论的软件可靠性验证测试方案(Bayesian-SRDT)可利用先验信息来降低软件可靠性验证测试工作量。如何构造合适的失效概率先验分布函数,是决定面向离散型软件可靠性验证测试工作量大小的重要因素。已有研究成果表明,使用减函数来构造先验分布函数是一种有效的方法。本论文在减函数理论及贝叶斯理论基础上,提出一种基于减函数的多层贝叶斯离散型软件可靠性验证测试方案(MBDFDSRDT)。首先选取面向失效概率的减函数作为第一层先验分布密度函数,然后选择第一层先验分布密度函数超参数的均匀分布函数来构造其第二层先验分布密度函数,进而给出相应的多层后验分布密度函数。最后,选取若干组失效数据,对本课题提出的MBDFDSRDT方案进行实践应用,并与基于贝叶斯理论的软件可靠性验证测试方以及基于减函数的单层贝叶斯软件可靠性验证测试方案进行对比。应用结果表明,本课题研究成果可显著地降低所需的测试工作量,特别适用于高可靠安全关键软件。     

星载微波辐射计AMSR-E在台风分析中的应用

基于洋面上的微波辐射传输模型,采用多元线性回归算法,建立了海洋-大气参数的反演算式,并对其进行了验证。在此基础上,根据AMSR-E的反演结果,结合微波图像,对2005年“麦莎”和“泰利”两次台风的发生发展过程进行了初步分析和比较。结果表明,反演算式的精度接近于美国雪冰数据中心公布的业务产品,反演结果比较符合实际情况,微波图像可以较好地反映台风发生发展过程中的变化特征。     

大规模网络存储系统中面向可靠性的数据布局算法

如何有效地在大规模的网络存储系统中存放数据是一个具有挑战性的问题.深入研究如何在满足存储的公平、冗余和自适应性的情况下,基于存储设备不同的可靠性能,充分考虑存储数据的重要性进行数据布局.用整数规划的形式描述了面向不同可靠性等级的存储设备进行数据布局的优化问题,并说明了这个问题是NP难的.提出了一种块级别的面向可靠性的数据分级布局算法,保证了布局算法的公平、冗余以及自适应性,并分析了数据布局算法的合理性和可行性.     

A multi-echelon multi-commodity,logistic system design at the Dead Sea Works Ltd

The Dead Sea Works Ltd extracts more than 2 million ton of potassium a year from the Dead Sea and ships it by surface and marine transportation to markets around the world. Product mix and market distribution changes have made it necessary to redesign the surface logistics system. This system consists of four sites with a total of more than 300,000 ton of roofed storage capacity. The purpose of the study was to suggest changes in the storage capacity of each product at each of the sites. We have designed a model that expresses the expected annual loss for the firm and have solved it under potential market scenarios and management and technological constraints. Sensitivity analyses were then carried out for several uncertain cost and reliability parameters. Based on the best system configuration for each scenario, alternative configurations were designed in detail and compared under potential market scenarios. With a clear understanding of the risks and expected benefits involved, the decision makers then selected the best configuration. The annual net benefit from implementing the proposed solution is expected to be 1.7 million dollars.     

基于双区块链及物联网技术的防伪溯源系统

在产品安全日益重要的今天,传统的产品溯源系统依旧存在着中心化存储、数据可信度低、数据易窜改及责任人定位困难等问题。区块链技术凭借其分布式存储、链式结构、不可窜改和高容错性等特点,能在信息的搜集、存储和共享等过程中实现数据溯源功能,并借助物联网技术保证区块链源头数据的真实可靠,以实现对产品信息的实时跟踪。基于此,提出了使用双区块链和物联网技术的产品防伪溯源系统,分析了该溯源系统的安全性并通过仿真实验验证了该系统的可行性,对区块链技术和产品安全领域的进一步结合提供了参考和借鉴。     

Performance and reliability concern scheme for efficient garbage collection and wear leveling on flash memory-based solid state disk

In the near future, data is expected to double in every two years, which translates to more than one terabyte of data for every person on earth. The difficulty of storing and fetching required data from data centers and servers will consequently increase. As a result, significant attention has been given to the flash memory-based solid state drive (SSD) from consumer electronics companies, which has begun to replace the existing hard disk drive and is highly likely to be used as a storage unit for most of consumer electronics to achieve low power consumption. In contrast to traditional disk, SSD uses semiconductor chips to store data. This structure enjoys original technical characteristics, including low power consumption, shock resistance, and high performance in random access. Such features can overcome the shortcomings of magnetic disks. However, flash memory, the basic unit of SSD, has many distinctive characteristics that cause various challenges. Flash memory does not support updating in the place method. A write operation can be performed only on an empty or erased unit, making the process more time-consuming. Moreover, each storage unit has a limited number of erase cycles, after which the block becomes invalid. This research proposes a new scheme called performance and reliability concern to increase the reliability and performance of SSDs. The eligibility of the proposed scheme is proven through EagleTree simulator. The scheme is also compared with other state-of-the-art techniques in terms of effectiveness and efficiency. Experimental results show that the proposed scheme provides sufficient performance and reliability for SSD.

     

基于维修周期预测的网络摄像机退化分析

网络摄像机作为安防系统中的重要组成部分,其可靠性直接影响着安防系统的性能.现在的研究得到的都是安防设备的整体稳定性或者单一产品的稳定性,无法在保证个体差异性的基础上对整体进行效能评估;鉴于此,提出了基于Winner过程先验数据的可靠性分析法.首先,建立基于Winner过程的设备退化模型并利用PPS抽样方法对其中的设备数据样本获取先验数据,并确定其先验分布;其次,对其设备维修周期的置信区间进行计算,并对退化样本数据建立后验估计,预测安防设备的维修周期.最后通过一个实例表明了该方法的研究意义和使用价值.     

Forecasting analysis by using fuzzy grey regression model for solving limited time series data

The grey model GM(1,1) is a popular forecasting method when using limited time series data and is successfully applied to management and engineering applications. On the other hand, the reliability and validity of the grey model GM(1,1) have never been discussed. First, without considering other causes when using limited time series data, the forecasting of the grey model GM(1,1) is unreliable, and provide insufficient information to a decision maker. Therefore, for the sake of reliability, the fuzzy set theory was hybridized into the grey model GM(1,1). This resulted in the fuzzy grey regression model, which granulates a concept into a set with membership function, thereby obtaining a possible interval extrapolation. Second, for a newly developed product or a newly developed system, the data collected are limited and rather vague with the result that the grey model GM(1,1) is useless for solving its problem with vague or fuzzy-input values. In this paper the fuzzy grey regression model is verified to show its validity in solving crisp-input data and fuzzy-input data with limited time series data. Finally, two examples for the LCD TV demand are illustrated using the proposed models.     

Deterministic fuzzy time series model for forecasting enrollments

The fuzzy time series has recently received increasing attention because of its capability of dealing with vague and incomplete data. There have been a variety of models developed to either improve forecasting accuracy or reduce computation overhead. However, the issues of controlling uncertainty in forecasting, effectively partitioning intervals, and consistently achieving forecasting accuracy with different interval lengths have been rarely investigated. This paper proposes a novel deterministic forecasting model to manage these crucial issues. In addition, an important parameter, the maximum length of subsequence in a fuzzy time series resulting in a certain state, is deterministically quantified. Experimental results using the University of Alabama’s enrollment data demonstrate that the proposed forecasting model outperforms the existing models in terms of accuracy, robustness, and reliability. Moreover, the forecasting model adheres to the consistency principle that a shorter interval length leads to more accurate results.     

一种高性能阵列架构研究

现今的应用程序需要更可靠的数据存储.到目前为止,数据存储的可靠性都是依靠不同的RAID级别来保证数据的可靠性,一般采用5个RAID级别[1,2]中的一种.存储方面的数据已经很明显地增长了,但是磁盘的可靠性并没有多大的改善.所以,为了控制存储的成本,有必要提供多元化的存储.在现有系统中加入新的RAID代码需要大量的开发、测试和调试工作,从成本上来讲是不现实的.因此,提出了一种新的通用RAID架构,此架构是基于异或的纠删码的,并将任意扇区和磁盘故障的组合作为基础,因而具有通用性.     

主动容错云存储系统的可靠性评价模型

除了传统的冗余机制,主动容错技术也被用来提高存储系统的可靠性。然而,当前对主动容错云存储系统可靠性的研究工作很少,而且都局限于硬盘故障服从指数分布的假设前提。针对主动容错磁盘冗余阵列RAID-5和RAID-6云存储系统提出两个可靠性状态转移模型,并基于转移模型设计了蒙特卡洛仿真算法,评价系统在一定运行周期内发生数据丢失事件的期望个数。该算法采用韦布分布函数模拟随时间变化（降低、恒定不变、或升高）的硬盘故障率,准确评价了主动容错机制、硬盘整体故障、故障修复、潜在块故障以及磁盘清洗过程对系统可靠性的影响。所提方法可以帮助系统设计者评估不同容错机制和系统参数对云存储系统可靠性的影响,有助于创建高可靠存储系统。     

融合专家经验和寿命试验数据的产品可靠性评估

专家经验是可靠性工程中常见的一类可靠性数据,通过将其与产品的寿命试验数据融合,可以扩充可靠性信息,为产品可靠性的评估提供新的思路.对此,利用Bayes理论,考虑不同类型和不同形式的专家经验,通过验前矩拟合的方法将其转化为产品寿命分布参数的验前分布.进一步,根据寿命试验数据确定似然函数,推断分布参数的验后分布,可求得数据融合后产品的可靠度和剩余寿命等可靠性评估结果.通过蓄电池算例分析,表明所提出方法的应用及其有效性.     

基于混合伽玛分布的Bayes可靠性评估方法

研究指数寿命产品验前信息的融合问题。为了合理地利用验前信息,同时兼顾产品可靠性试验的不确定性,提出运用混合伽玛分布作为验前分布的贝叶斯方法,并将之推广到多源验前信息和变总体的情况。重点解决多源验前信息融合权重的确定问题。通过实例,分析比较这一方法与经典方法和单纯使用伽玛分布作为验前分布的贝叶斯方法的差异,证明该方法的合理性和可行性。     

主动容错副本存储系统的可靠性分析模型

主动容错机制通过预先发现即将故障的硬盘来提醒系统提前迁移备份危险数据,从而显著提高存储系统的可靠性。针对现有研究无法准确评价主动容错副本存储系统可靠性的问题,提出几种副本存储系统的状态转换模型,然后利用蒙特卡洛仿真算法实现了该模型,从而模拟主动容错副本存储系统的运行,最后统计系统在某个运行时期内发生数据丢失事件的期望次数。采用韦布分布函数模拟设备故障和故障修复事件的时间分布,并定量评价了主动容错机制、节点故障、节点故障修复、硬盘故障以及硬盘故障修复事件对存储系统可靠性的影响。实验结果表明,当预测模型的准确率达到50%时,系统的可靠性可以提高1~3倍;与二副本系统相比,三副本系统对系统参数更敏感。所提模型可以帮助系统管理者比较权衡不同的容错方式以及系统参数下的系统可靠性水平,从而搭建高可靠和高可用的存储系统。     

再入飞行器低误码率数字化遥测系统设计

针对再入飞行器遥测领域要求存储系统容量大、误码率低、恶劣环境下可靠回收等特点,以及传统供配电方式存在的地面设备组成多、操作复杂、可靠性低等不足,提出了一种适用于再入飞行器的低误码率数字化遥测系统设计方法。系统采用大容量冗余备份存储设计、基于ECC纠错编码算法的低误码率设计、抗高冲击回收防护设计等关键技术,存储容量提高到32GB,可连续存储9h数据,存储器误码率降低至10-10,实现了高冲击条件下的可靠回收;采用基于1553B总线的数字化供配电技术、在线故障诊断技术等关键技术,简化了地面测试设备及操作,节约研制成本,提高了飞行器智能化和测试性水平。该系统已成功应用于某再入飞行器,通过了地面炮击试验和飞行试验考核,获取到了完整的全程飞行试验数据。     

一种实时数据库的分布式存储方法

随着网络技术迅速发展,各实时系统产生的数据量呈指数级增长,各业务应用对海量数据的管理和应用的实时性提出了越来越高的要求,现有的单机实时数据库技术已无法满足需求,将分布式思想引入实时数据库领域,主要从数据分布方式、数据冗余备份、数据一致性等方面做了研究,并提出一种实时数据系统中的数据分布式存储的设计.该设计可以为当前实时数据库扩展性及可靠性方面提供技术支撑.     

Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment

Reliability prediction plays an important role in product lifecycle management. It has been used to assess various reliability indices (such as reliability, availability and mean time to failure) before a new product is physically built and/or put into use. In this article, a novel approach is proposed to facilitate reliability prediction for evolutionary products during their early design stages. Due to the lack of sufficient data in the conceptual design phase, reliability prediction is not a straightforward task. Taking account of the information from existing similar products and knowledge from domain experts, a neural network-based fuzzy synthetic assessment (FSA) approach is proposed to predict the reliability indices that a new evolutionary product could achieve. The proposed approach takes advantage of the capability of the back-propagation neural network in terms of constructing highly non-linear functional relationship and combines both the data sets from existing similar products and subjective knowledge from domain experts. It is able to reach a more accurate prediction than the conventional FSA method reported in the literature. The effectiveness and advantages of the proposed method are demonstrated via a case study of the fuel injection pump and a comparative study.     

Confidence-based reliability assessment considering limited numbers of both input and output test data

Simulation-based methods can be used for accurate uncertainty quantification and prediction of the reliability of a physical system under the following assumptions: (1) accurate input distribution models and (2) accurate simulation models (including accurate surrogate models if utilized). However, in practical engineering applications, often only limited numbers of input test data are available for modeling input distribution models. Thus, estimated input distribution models are uncertain. In addition, the simulation model could be biased due to assumptions and idealizations used in the modeling process. Furthermore, only a limited number of physical output test data is available in the practical engineering applications. As a result, target output distributions, against which the simulation model can be validated, are uncertain and the corresponding reliabilities become uncertain as well. To assess the conservative reliability of the product properly under the uncertainties due to limited numbers of both input and output test data and a biased simulation model, a confidence-based reliability assessment method is developed in this paper. In the developed method, a hierarchical Bayesian model is formulated to obtain the uncertainty distribution of reliability. Then, we can specify a target confidence level. The reliability value at the target confidence level using the uncertainty distribution of reliability is the confidence-based reliability, which is the confidence-based estimation of the true reliability. It has been numerically demonstrated that the proposed method can predict the reliability of a physical system that satisfies the user-specified target confidence level, using limited numbers of input and output test data.     

大数据技术在智慧机场领域的应用研究

随着信息技术的发展,大数据技术在公共服务中的交通、医疗、教育、预测服务等领域得到广泛应用.本文从大数据存储、挖掘、分析和应用等几个角度重点介绍了机场大数据分析平台的构建方法,在机场转型升级方面提供了信息整合及数据分析模式的创新,以大数据技术在智慧机场领域的应用为研究内容,通过多个案例讨论大数据技术在智慧机场中的应用,从...     

寄生存储冗余机制与实现技术

互联网在数据包收发过程中具有短暂的数据存储能力.由于网络上数据包的传榆时间非零,这样便导致数据包在传输的过程中能够保持一定的在线时间,从而形成事实上的数据存储.这种数据存储行为可用于构建一种极其独特的网络存储新模式--寄生存储.针对寄生存储模型,给出数据冗余机制,并深入分析基于纠删码的数据冗余存储模型,给出数据分布模型与数据恢复策略.分析结果表明,这种冗余方法根据宿主的健康程度及能力实现数据分布的差异化管理,既减少了系统对网络状况的影响又提高了系统的可靠性,适用于在广域网上进行免费、大容量及短期的数据存储;远程寄生宿主并没有意识到用户的数据存储行为;对网络性能的影响几乎可以忽略不计.