基于CDEGS软件设计的下水库接地网改造

本文利用CDEGS软件,对天荒坪抽水蓄能电站水库区域的土壤及岩石等地质参数进行分析和拟合,并构建土壤的分层模型;然后通过对地网的网格大小、导体的疏密程度、接地网的面积、水平接地体的长度等因素进行经济比较和优化分析,设计出了符合安全标准的水下接地网。结果表明:水下接地网的设计满足设计要求,达到了改善接地网参数性能的目标。     

基于启发式BP算法的软件缺陷预测模型

针对软件缺陷预测时缺陷数据集中存在的类别分布不平衡问题，结合上采样算法SMOTE与Edited Nearest Neighbor (ENN) 数据清洗策略，提出了一种基于启发式BP神经网络算法的软件缺陷预测模型。模型中采用上采样算法SMOTE增加少数类样本以改善项目中的数据不平衡状况，并针对采样后数据噪声问题进行ENN数据清洗，结合基于启发式学习的模拟退火算法改进四层BP神经网络后建立分类预测模型，在AEEEM数据库上使用交叉验证对提出的方案进行性能评估，结果表明所提出的算法能够有效提高模型在预测类不平衡数据时的分类准确度。     

干湿联合冷却塔冷却节水分析软件开发及应用

为实现对干湿联合冷却塔冷却特性、节水特性的综合分析，编制干湿联合冷却塔校核计算流程，基于Visual Studio开发平台，本文开发了干湿联合冷却塔冷却节水特性分析优化软件。通过对某在运干湿联合冷却塔进行冷却节水特性计算，验证了所开发软件计算结果的准确性；在消雾节水计算结果基础上，对标干湿联合消雾冷却塔验收测试规程，生成其成雾频率曲线、耗水量曲线、塔雾指数、冷却特性曲线，综合分析其消雾特性、节水特性及冷却特性，从而确定了干湿联合冷却塔的最佳运行工况。同时运用所开发软件对比分析了某冷却塔设计干段散热面积对其消雾特性、节水特性及冷却特性的耦合影响，并分析了百叶窗开度对干湿联合冷却塔性能的影响，为干湿联合冷却塔的运行优化和设计优化提供了工具性软件支持。     

新型硫酸法C_4烷基化工艺的模拟

提出了一种新型硫酸法C4烷基化生产工艺,丁烯原料在气相状态下进入反应器,与液相异丁烷、浓硫酸混合后,部分丁烯溶于液相,在液相中进行反应;通过控制反应器的压力,部分液相吸收反应热而汽化,使反应温度基本稳定;经气液分离、酸烃分离及产品分馏后,气相丁烯、异丁烷、浓硫酸分别构成循环。采用Aspen Plus过程模拟软件对新工艺过程进行模拟计算的结果表明,在反应器进口压力0.2 MPa、压降4.5 k Pa的条件下,反应器进出口温度均在7.2℃左右,可通过调节反应器压力实现温度的控制;反应器进口液相中烷烯质量比为145∶1,可减少副反应的发生;与传统硫酸法C4烷基化工艺相比,新工艺耗电量可降低30%。     

An industrial case study on variability handling in large enterprise software systems

ContextEnterprise software systems (e.g., enterprise resource planning software) are often deployed in different contexts (e.g., different organizations or different business units or branches of one organization). However, even though organizations, business units or branches have the same or similar business goals, they may differ in how they achieve these goals. Thus, many enterprise software systems are subject to variability and adapted depending on the context in which they are used.ObjectiveOur goal is to provide a snapshot of variability in large scale enterprise software systems. We aim at understanding the types of variability that occur in large industrial enterprise software systems. Furthermore, we aim at identifying how variability is handled in such systems.MethodWe performed an exploratory case study in two large software organizations, involving two large enterprise software systems. Data were collected through interviews and document analysis. Data were analyzed following a grounded theory approach.ResultsWe identified seven types of variability (e.g., functionality, infrastructure) and eight mechanisms to handle variability (e.g., add-ons, code switches).ConclusionsWe provide generic types for classifying variability in enterprise software systems, and reusable mechanisms for handling such variability. Some variability types and handling mechanisms for enterprise software systems found in the real world extend existing concepts and theories. Others confirm findings from previous research literature on variability in software in general and are therefore not specific to enterprise software systems. Our findings also offer a theoretical foundation for describing variability handling in practice. Future work needs to provide more evaluations of the theoretical foundations, and refine variability handling mechanisms into more detailed practices.     

单汽泡沸腾过程数值模拟的研究

利用Comsol Multiphysics软件中的Level Set方法对单汽泡沸腾过程进行了模拟,建立了微细结构网格,给出了边界条件,求解了质量、动量、能量和Level Set方程,得到了正确的模拟结果。分析了单汽泡沸腾过程中相含率、压力场、速度场和温度场随时间的变化规律;考察了接触角和壁面效应对汽泡脱离直径和汽泡生长周期的影响。模拟结果表明,当接触角小于12°时,汽泡脱离直径维持不变;当接触角大于12°时,接触角越大,汽泡脱离直径越大。汽泡生长周期随接触角的增大而延长。当加热装置的直径小于汽泡脱离直径时,汽泡受壁面效应的影响不易脱离;当加热装置的直径大于汽泡脱离直径时,汽泡脱离直径不随加热装置直径的增大而变化;汽泡生长周期随加热装置直径的增大而缩短。     

A data‐centric framework for debugging highly parallel applications

Contemporary parallel debuggers allow users to control more than one processing thread while supporting the same examination and visualisation operations of that of sequential debuggers. This approach restricts the use of parallel debuggers when it comes to large scale scientific applications run across hundreds of thousands compute cores. First, manually observing the runtime data to detect error becomes impractical because the data is too big. Second, performing expensive but useful debugging operations becomes infeasible as the computational codes become more complex, involving larger data structures, and as the machines become larger. This study explores the idea of a data‐centric debugging approach, which could be used to make parallel debuggers more powerful. It discusses the use of ad hoc debug‐time assertions that allow a user to reason about the state of a parallel computation. These assertions support the verification and validation of program state at runtime as a whole rather than focusing on that of only a single process state. Furthermore, the debugger's performance can be improved by exploiting the underlying parallel platform because the available compute cores can execute parallel debugging functions, while a program is idling at a breakpoint. We demonstrate the system with several case studies and evaluate the performance of the tool on a 20 000 cores Cray XE6. Copyright © 2013 John Wiley & Sons, Ltd.     

软件功能安全验证活动的应用

软件功能安全验证活动对提高安全关键系统的安全性具有重要作用,它贯穿于软件安全生命周期的各个阶段。通过对各阶段输出文档的审查和分析,可尽早发现软件问题,降低软件修复成本。完整的问题闭环处理流程可有效解决软件验证活动中发现的问题,给出各方均满意的处理结果。     

Interventions for long-term software security: Creating a lightweight program of assurance techniques for developers

Though some software development teams are highly effective at delivering security, others either do not care or do not have access to security experts to teach them how. Unfortunately, these latter teams are still responsible for the security of the systems they build: systems that are ever more important to ever more people. We propose that a series of lightweight interventions, six hours of facilitated workshops delivered over three months, can improve a team's motivation to consider security and awareness of assurance techniques, changing its security culture even when no security experts are involved. The interventions were developed after an Appreciative Inquiry and Grounded Theory survey of security professionals to find out what approaches work best. We tested the interventions in a participatory action research field study where we delivered the workshops to three software development organizations and evaluated their effectiveness through interviews beforehand, immediately afterwards, and after twelve months. We found that the interventions can be effective with teams with limited or no security experience and that improvement is long-lasting. This approach and the learning points arising from the work here have the potential to be applied in many development teams, improving the security of software worldwide.     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.