数据库中一个常见错误信息诊断与修正

本文简要的对Oracle数据库一个常见的错误信息ora-04031错误进行了分析与诊断,同时对其形成原因进行了初步探讨.对如何诊断该项错误进行了简单的阐述,并举实例分析了如何解决该错误.     

一种用于白盒测试评估的错误播种方法

软件错误播种方法不仅可以用来评价软件的性能和研究软件错误的特性，而且还可通过播种错误为软件测试方法的评估提供必要的条件。考虑到白盒测试所针对的错误类型是程序代码级错误，为了方便错误播种，将程序代码错误分为计算型错误、域错误和程序接口错误，并针对这3类错误提供了一种改进的基于程序变异的软件错误播种方法。     

一个通用的FOXPRO出错处理和记录程序

FoxPro数据库的函数比较丰富，充分利用它的函数能够得到系统运行时的很多信息。在程序调试阶段，如果能动态地记录下系统运行的错误情况，对于程序员来说是非常重要的。另外，在系统运行过程中，为防止由各种原因引起的错误而破坏系统的完整性，为系统编写一个错误处理程序是必要的。本文就是笔者在开发数据库管理软件时使用的出错处理程序的设计思路和源程序。首先根据检测到的错误的类型做出不同的处理，比如错误信息为IOSJ09是网络上的文件共享错误，对于这类错误，只要等待一段时间再试即可。如果是程序本身的错误，则将出现错误的…     

基于支持向量机的图象插值及错误隐匿策略

如何对在有损网络环境中传输的视频进行错误隐匿是视频传输研究中的基本问题。支持向量机（SVM）是一种新兴的通用学习算法，是国际上机器学习领域新的热点。为了取得比现有方法更好的错误隐匿效果，提出了一种新的基于支持向量机回归估计的错误隐匿策略，首先建立了基于支持向量机回归估计的图像插值算法，并将其引入到错误隐匿问题中，然后用空域插值的方法达到错误隐匿的目的。实验结果表明，与目前采用的各种错误隐匿策略相比较，基于支持向量机的错误隐匿策略在错误隐匿效果和推广性能上都具有一定的优越性。     

ZIP文件损坏的解决办法

大家或许都有过这样的经历，用ＷｉｎＺｉｐ解压一个ＺＩＰ文件时，会出现各种各样的错误提示，如“ＣＲＣ错误”、“数据错误”、“磁盘Ｉ／Ｏ读写错误”等，下面是笔者的解决之道。　　１、“ＣＲＣ错误”：解压时ＷｉｎＺｉｐ提示“ＣＲＣ错误”（如图１），指的是压缩文件在解压时进行的一次校验中出现了错误。发生“ＣＲＣ错误”并不一定是文件的数据已经损坏，只是做成的压缩包有些误码而已。这样的问题，可以通过ＷｉｎＲＡＲ或Ｎｅｔａｎｔｓ等的再次下载（实际只是重新下载损坏的部分）来修复，否则，用普通的解压缩软件是无法解压…     

由应用系统接管FoxPRO的错误捕获和处理

任何一个系统，在设计和运行过程中总不可避免会隐含各种未可预知的错误，尤其在软件设计日趋庞大的今天，隐含错误的几率越来越大。但只要我们能及时捕获到错误以便纠正，或当错误出现后能妥善处理，那么出现错误并不可怕。对错误的捕获和处理是应用系统设计中挺重要的一部分。但是，许多的应用软件中，错误的捕获和处理并不灵活，能提供的错误信息亦不完善，维护较困难。往往还出现这样的情况：当操作员发现一个错误、维护人员赶到现场时，屏幕上的错误信息却早已消失不见而无法维护！如果你是一个XENIX系统管理员，你将总可以在XENIX…     

一种检测与校正JPEG数据传输错误的新方法

无线传输JPEG数据时，由于信道存在噪音，经常会发生偶然性错误或突发性错误，因此错误检测与恢复在无线传输JPEG图像中有着举足轻重的作用。由于JPEG图像对传输错误非常敏感，一个bit位的传输错误就会影响后续位的解码，从而造成图像质量的严重下降。传统的处理JPEG传输错误是采用错误掩藏与编码自同步方法。其中错误隐藏是使用插值的方法来恢复数据，但其会在一定程度上降低传输图像的质量；编码自同步方法则会影响传输数据的通用性。为了更好地检测与校正JPEG数据传输错误，提出了一种新的JPEG编码数据传输错误检测与恢复方法。该方法以图像相关性作为判别依据，使用全搜索的方法来恢复数据。通过对该方法可行性进行的理论分析和仿真的实验表明，该方法可以更好地恢复偶然性传输错误，并可极大提高无线传输JPEG图像的质量。     

“错误”让数学课亮起来

孔子说过：“人非圣贤，孰能无过!”不管是在生活中还是学习中。每个人都或多或少地犯过错误，老师如此，学生也如此。认知心理学派认为：错误是学习的必然产物，因此错误是可以被接受的，出错是成长中的学生的权利。学生的知识背景、思维方式、情感体验、表达方式往往和成人截然不同，他们在学习中出现各种各样的错误是十分正常的。     

转型

管理者们经常是带着全心全意的注意力和火热的激情，但所执行的其实是错误的战略，向着错误的方向前进，做着错误的事情——“将军们往往在准备打过去的战争”。     

软件可靠性评估研究

软件错误引起的后果差别很大，有的几乎可以忽略不计，有的可能是灾难性的。目前，软件可靠性模型很少考虑到这一点，我们在实际工作中发现量化这一差别是很有必要的。为了增加人们对错误重要性差别的意识，讨论了在考虑错误级别时，如何应用Jelinski-Moranda模型和Goel-Okumoto,Nelson模型以及Nelson和Bron-Lipow模型。这些讨论和研究说明软件可靠性不仅决定于软件中的错误数，而且决定于软件中的错误级别。     

事故致因理论在信息安全事件中的应用分析

本文建立了信息安全事故致因分析理论和事故致因分析模型,并且对近年来的两起重大的信息安全事件应用事故致因模型进行事故致因分析,相信在对重大信息安全事件科学深入分析的基础上对信息安全监管工作的完善能够起到作用.     

基于GO法融合共因失效的接触网可靠性分析

为分析共因失效对接触网系统可靠性的影响,采用共因失效β因子搭建接触网模型。根据Markov过程理论,导出可修部件的共因失效概率随时间t的近似表达式。对某段电气化铁路接触网系统的特性进行分析,建立其等效失效模型和GO图。根据是否考虑共因失效两种情况,运用MATLAB绘制相应的可靠度变化曲线。对接触网系统的定量分析结果表明:系统在考虑共因失效后,各项可靠性参数值更加贴近实际。实现接触网系统的定性评估,并找出其薄弱环节,为接触网系统的运营维护提供理论依据。     

基于AOV图及因果图的梯形图与语句表互换算法

结合AOV图与因果图提出了一种新的梯形图与语句表双向转换算法。一方面,将梯形图转化为AOV图,并利用AOV图建立因果图,然后遍历因果图的节点生成PLC所能识别的语句表;另一方面,将语句表转化为因果图,基于因果图生成梯形图。经过实际编程证明,该算法简洁,实用性及通用性好,并成功应用于某国产化PLC开发中。     

从结构推断到根因识别——工业过程故障根因诊断研究综述

由于现代工业过程的复杂结构,变量间普遍存在紧密耦合,故障往往在变量间广泛传播,为过程运维带来挑战.针对该问题,工业根因诊断(industrial root cause diagnosis, IRCD)技术应运而生,其从异常变量中确定故障根因,便于针对性故障处理. IRCD包含两个主要步骤:结构推断和根因识别.前者建立变量间的信息传递结构;后者根据传递结构定位根因.然而,现有IRCD综述多侧重于结构推断,未对根因识别步骤进行调研,且未建立起各类IRCD模型与过程特性间的系统关联.为此,从结构推断和根因识别两个层级展开IRCD的研究综述.首先,依据推断准则的异同,归纳4类经典结构推断模型;其次,考虑到过程的高维度、非线性、非平稳性质以及机理知识的效用,对结构推断模型的变种及适用场景进行梳理;随后,对根因识别方法进行归类,包括纯数据驱动、知识与数据融合驱动的范式,涵盖6类典型方法,并分析它们的优势与不足;最后,讨论IRCD技术中存在的挑战,并给出未来研究方向,为后续研究提供参考.     

Diagnosis of plant-wide oscillation through data-driven analysis and process understanding

Disturbances that propagate throughout a plant due to recycle streams, heat integration or other means can have an especially large impact on product quality and running costs. There is thus a motivation for automated detection of a plant-wide disturbance and for determination of the root cause so that the disturbance may be removed. In this article, data-driven techniques are used to diagnose a plant-wide oscillation in an Eastman Chemical Company plant. A numerical non-linearity index derived from routine measurements was able to suggest the root cause. Process understanding possessed by the plant control engineers then enhanced the data-driven analysis, for instance by identifying a proxy measurement for an unmeasured flow through the valve suspected of being the root cause. In situ tests of just one valve confirmed the suspected root cause and the plant-wide oscillation disappeared after repairing the valve. The diagnosis was right first time and the maintenance effort was thus minimized. The success of the study suggests there exists a fruitful direction for future research in the automated linkage of data-driven analysis with information about the structure and connectivity of the process.     

Complexity results for structure-based causality

We give a precise picture of the computational complexity of causal relationships in Pearl's structural models, where we focus on causality between variables, event causality, and probabilistic causality. As for causality between variables, we consider the notions of causal irrelevance, cause, cause in a context, direct cause, and indirect cause. As for event causality, we analyze the complexity of the notions of necessary and possible cause, and of the sophisticated notions of weak and actual cause by Halpern and Pearl. In the course of this, we also prove an open conjecture by Halpern and Pearl, and establish other semantic results. We then analyze the complexity of the probabilistic notions of probabilistic causal irrelevance, likely causes of events, and occurrences of events despite other events. Moreover, we consider decision and optimization problems involving counterfactual formulas. To our knowledge, no complexity aspects of causal relationships in the structural-model approach have been considered so far, and our results shed light on this issue.     

基于序列标注模型的情绪原因识别方法

情绪原因识别是情绪分析中的一个重要研究任务。该任务旨在自动分析出导致某一情绪发生的原因描述。该文将情绪原因识别任务建模为序列标注模型,即将情绪词相关的子句当成序列,进而整体标注出哪些属于原因子句。具体实现中,我们使用条件随机场(CRF)模型进行求解,并结合了基本词特征、词性特征、距离特征、上下文特征及语言学特征等多种特征进行原因识别。实验结果表明,所采用的这些特征对于原因识别都有一定帮助,特别是上下文特征。此外,我们发现在使用类似特征集合的情况下,序列标注模型能够获得比分类模型更好的识别效果。     

Resistance characterization for weak open defects

Strong open defects can cause a circuit to malfunction, but even weak open defects can cause it to function poorly. Detecting weak opens is thus an important, but challenging, task. Characterizing weak opens can help researchers assess the need for delay fault tests     

Leveraging on causal knowledge for enhancing the root cause analysis of equipment spot inspection failures

Causal correlation data over the equipment spot-inspection operation and maintenance (O&M) records and fault investigation sheets potentially reflect the state related to the causal effect of equipment failures. Various factors influence equipment failures, making it difficult to effectively analyze the main cause of the problems. Mining and leveraging these causal data from the equipment spot inspection records will undoubtedly significantly improve the root cause analysis of the fault in the O&M system. Hence, this paper introduces causal knowledge in equipment fault O&M for the first time and proposes to exploit causal knowledge for enhancing root cause analysis of equipment spot inspection failures. Specifically, an equipment fault O&M knowledge graph with causal knowledge called CausalKG is constructed to provide knowledge support for the causal analysis of faults. That is, CausalKG consists of spot-inspection knowledge graph (SIKG) and causal relationship knowledge (CRK) in equipment fault O&M. Further, a CausalKG-ALBERT knowledge reasoning model is designed. The model transforms CausalKG into network embeddings based on relational graph convolutional networks. In turn, it combines the Q&A mechanism of the language model ALBERT to mine the root cause knowledge of equipment failures. The case study confirms that incorporating the CRK is more effective than directly using the SIKG for causality reasoning; The model can fully use causal relationship knowledge to enhance the reliability of root cause analysis. This method is valuable to help engineers strengthen their causal analysis capabilities in preventive equipment maintenance.     

Prioritizing Test Cases for Memory Leaks in Android Applications

Mobile applications usually can only access limited amount of memory. Improper use of the memory can cause memory leaks, which may lead to performance slowdowns or even cause applications to be unexpectedly killed. Although a large body of research has been devoted into the memory leak diagnosing techniques after leaks have been discovered, it is still challenging to find out the memory leak phenomena at first. Testing is the most widely used technique for failure discovery. However, traditional testing techniques are not directed for the discovery of memory leaks. They may spend lots of time on testing unlikely leaking executions and therefore can be inefficient. To address the problem, we propose a novel approach to prioritize test cases according to their likelihood to cause memory leaks in a given test suite. It firstly builds a prediction model to determine whether each test can potentially lead to memory leaks based on machine learning on selected code features. Then, for each input test case, we partly run it to get its code features and predict its likelihood to cause leaks. The most suspicious test cases will be suggested to run at first in order to reveal memory leak faults as soon as possible. Experimental evaluation on several Android applications shows that our approach is effective.