撬装油气分离设备在胜利海六站的试验

撬装油气水分离设备不仅可用于海上平台油井(组)条件下油气水的分离,而且调整处理流程后还可以对污水进一步处理,处理后的水可作为回注水源。通过对高含水产出液的就地分离和回注,可以有效降低集输管线压力,降低边远区块船舶拉油成本。为了开发一井多用工艺技术,低成本和解决边远区块注水水源缺乏的问题,研制了撬装油气水三相分离设备,并在胜利油田完成了室内试验和现场试验,达到了预期的目标

OPGW应用领域技术发展趋势

文章介绍了近年来的部分新技术在OPGW（光纤复合架空地线）中的应用,包括光纤传感技术在OPGW的温度、应力在线监测方面的应用,OPGW融冰新技术,200μm外径新型光纤在OPGW中的应用,大余长光纤OPGW在OPGW中的应用。并对OPGW光缆应用领域技术的发展趋势进行了预测

research of ip flow classification based on heuristic search

基于应用层载荷特征的IP流分类技术的准确性较高,但是,当特征库庞大时遍历匹配特征库需要消耗大量的时间.鉴于此,提出一种将应用层载荷特征和启发式搜索相结合的IP数据流分类方法.通过从各种应用产生的数据包之间提取共同特征并以此共同特征建立启发式规则,根据启发式规则将特征库划分为多个特征子集,在数据包匹配过程中只需要根据启发式规则搜索匹配特定的特征子集,从而大大减少了对无关特征的匹配过程,使待匹配的特征子集具有更强的针对性、使得时间性能得到提高.对于部分应用采用以DNS为引导的方法来对数据包进行分类,该方法部分消除了基于载荷无法对加密数据进行识别的弊端.本文用C语言实现了该算法,并与开源软件17-filter算法进行了对比实验.实验结果表明:在离线状态下,本文提出的方法的分类速度是17-filter分类速度的6-10倍,总体识别准确性达到98％以上.安徽省自然科学基金项目(11040606M131)资

Random Testing with Varied Probability

适应性随机测试是对随机测试低覆盖率和盲目性的一种改进.它的思想是通过尽量地使测试用例均匀地分布在整个测试域范围内,从而提高测试效率.研究显示,相比于常规的随机测试,适应性随机测试能够使用更少的测试用例来发现被测程序的第一个错误.但是,现有的适应性随机测试的实现方案的时间效率不高,在生成测试用例的过程中大量的计算将消耗大量的时间.针对已有适应性随机测试耗时的缺点,提出一种快速的适应性随机测试的实现方法.该方法主要是通过改变输入域空间内不同区域的测试用例生成的概率来实现测试用例的均匀分布.为最大限度地减少时间消耗,该方法每次只计算局部输入域空间内测试用例的生成概率.该方法理论上生成n个测试用例的时间消耗为O(n log n).实验显示,本文提出的方法只需很低的时间消耗就能生成大量的测试用例

程序不变量到断言的自动转换方法研究及其应用

程序不变量可以揭示程序的内部属性和动态执行情况,已经成功应用于软件测试用例的生成与约简。然而,每新增一个用例都要在整个测试用例集合上重新提取程序不变量,时间开销较大。提出一种基于正则表达式的将程序不变量自动转换为对应断言的方法,并利用断言判断新用例是否冗余,仅当新用例非冗余时才提取程序不变量,从而大幅度减小时间开销。将这种基于断言的测试方法应用于回归测试,可以有效约简测试用例集合,识别程序改动所影响的元素,进而发现潜在的程序错误。实验结果表明,与其它测试用例选择方法相比,该方法时间消耗小、测试用例集合约简率高、揭错能力强。安徽省自然科学基金项目(11040606M131

Detecting Program Non-crashing Failures via Assertion and Slicing

针对程序非崩溃错误难以被发现的问题,提出一种基于程序断言与切片技术的程序执行监测方法:根据程序执行过程中断言是否被违反来检测程序异常,并根据断言 违反信息对反馈的程序异常进行自动分类;在程序切片技术的帮助下,对分类后的程序异常进行分析,判断程序是否真的出错并找到程序错误来源.实验结果表明, 该方法可以有效地发现程序执行过程中发生的非崩溃错误,对程序异常进行合理的分类,约简程序代码和不变量,并将程序错误来源定位到具体的汇编指令,从而帮 助程序员快速方便地找到错误来源.To cope with the problem that non-crashing failures can hardly be detected,this paper proposes a novel program execution monitoring method based on program assertion and slicing technique.Program anomaly is detected by judging whether assertions are violated in the program execution process,and detected program anomalies are classified by assertion violation information.In addition,with the assistance of program slicing,programmers can analyze the classified program anomalies,determine whether real failures have happened,and find the source of failure.The experiment results show that the proposed method can effectively detect program non-crashing failures,reasonably classify program anomalies,reduce source code and invariants,and locate the source of failure to specific assembly statements,helping programmers to find the source of failure fast and easily

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies