The CARE platform for the analysis of behavior model inference techniques

ContextFinite State Machine (FSM) inference from execution traces has received a lot of attention over the past few years. Various approaches have been explored, each holding different properties for the resulting models, but the lack of standard benchmarks limits the ability of comparing the proposed techniques. Evaluation is usually performed on a few case studies, which is useful for assessing the feasibility of the algorithm on particular cases, but fails to demonstrate effectiveness in a broad context. Consequently, understanding the strengths and weaknesses of inference techniques remains a challenging task.ObjectiveThis paper proposes CARE, a general, approach-independent, platform for the intensive evaluation of FSM inference techniques.MethodGrounded in a program specification scheme that provides a good control on the expected program structures, it allows the production of large benchmarks with well identified properties.ResultsThe CARE platform demonstrates the following features: (1) providing a benchmarking mechanism for FSM inference techniques, (2) allowing analysis of existing techniques w.r.t. a class of programs and/or behaviors, and (3) helping users in choosing the best suited approach for their objective. Moreover, our extensive experiments on different FSM inference techniques highlight that they do not behave in the same manner on every class of program. Characterizing different classes of programs thus helps understanding the strengths and weaknesses of the studied techniques.ConclusionExperiments reported in this paper show examples of use cases that demonstrate the ability of the platform to generate large and diverse sets of programs, which allows to carry out meaningful inference techniques analysis. The analysis strategies the CARE platform offers open new opportunities for program behavior learning, particularly in conjunction with model checking techniques. The CARE platform is available at http://care.lip6.fr.     

基于半监督聚类的文档敏感信息推导方法

针对当前多文档聚合推导引起的敏感信息泄露问题存在风险大、隐蔽性高的特点,提出了一种基于半监督聚类的文档敏感信息推导方法。首先,为确保在较小的时间开销下获得高质量的约束信息,设计了一种新颖的二阶约束主动学习算法,它通过选择不确定性最大的样本点来生成信息量最大的约束闭包;然后,在引入约束信息的基础上结合DBSCAN提出一种新的半监督聚类算法,它能够有效解决DBSCAN算法存在的边界模糊问题,提高文档聚类准确性;最后,在半监督聚类结果的基础上,对相似文档进行敏感信息可能性测度。实验表明,半监督聚类算法准确率提升明显,推导方法能够有效推导出敏感信息。     

基于规则引擎的电信CRM系统架构研究

研究了电信CRM系统的功能架构.并针对目前系统架构的不足之处,提出了将规则引擎应用到CRM系统中,并构建了增加业务规则层的多层技术架构模式.使新应用的添加和变更比传统架构模式更加方便,同时能够适应大规模和复杂的应用需求,并有效降低建设和维护成本.     

浅谈JBPM技术在工作流系统的应用

本文主要介绍了工作流新兴技术JBPM的内核,并分析了在流程运转过程中JBPM引擎的工作方式。通过对JBPM技术的剖析,总结出了此技术在工作流系统中应用的优点,并对其发展做出了展望。     

汽车电控发动机故障诊断专家系统在高职教学中的应用

随着电子控制技术在汽车上的广泛应用,电控发动机的检修日益成为汽车维修的重点和难点,同时,发动机电控系统检修也是高职高专汽车类专业重要的专业课之一。对模糊理论和BP神经网络在故障诊断方面的应用进行了探讨,以BP神经网络为主,采用模糊理论作为有益补充设计了电控发动机故障诊断专家系统,给学生创造接近实际的虚拟学习环境,增强学生的学习积极性,取得了良好的教学效果。     

ISG电机在冷起动中的策略仿真

ISG电机的控制系统决定着发动机冷起动性能,是弱混合动力系统中关键部件。它必须在很短的时间内拖动发动机至点火转速,并且要求转速过渡平稳,脉动冲击小。基于永磁同步电机(PMSM)数学模型选定矢量调速策略,采用脉宽调制器(SVPWM),因具有谐波少、响应快的优势,可作为电流调制方法。采用建立S imu link上的发动机阻力矩模型制定发动机起动策略,对冷起动过程进行仿真。仿真结果表明控制策略能够满足发动机快速起动的要求,并且升速过程平稳,受阻力矩变化影响较小,具有较好的抗负载扰动性能。     

运维管理平台工作流引擎设计

论述在运维管理平台设计中针对业务发展对系统功能的不断要求引入工作流引擎.实现方便地创建公文流程,不需要编写任何代码;方便地构建业务流程,并能无缝融合到现有的IT服务流程中;方便流程配置、管理,通过图形化工具来确定流程的定义功能,方便流程的变更.     

基于OGRE和ODE的碰撞检测在巷道漫游系统中的研究

为了提高交互漫游系统中动态人物与静态场景对象之间的碰撞检测性能,提出了将动态人物采用包围盒的方法,与地面、巷道等静态物体采用三角网格面的方法相结合的思想,并将其应用于OGRE和ODE环境的巷道漫游系统中。结果表明,此方法提升了碰撞检测性能,满足了虚拟现实中实时性的要求。     

基于分布式中间件ICE的应用架构研究

在分布式系统中,分布式平台之间的差异增加了开发的复杂性.基于一种轻量级的分布式中间件ICE,提出了在分布式异构环境中的一般架构模型.该模型可以降低模块之间的耦合度,解决服务器负载过大的问题.将该模型应用到网络安全管理系统中,解决了网络安全管理系统中平台差异问题和服务器负载均衡问题.     

Lyapunov-based feedback design and experimental verification of IC engine speed control

In this paper, the speed control problem of internal combustion engines is investigated based on mean-value engine models. The dynamics of internal combustion engines is a complicated nonlinear system, and usually, it is difficult to know the exact values of the physical parameters. First, a Lyapunov-based design method is shown without requiring the full information of the physical parameters. Then, to improve transient performance, the design method is extended to several cases under different operation conditions. Numerical simulation results are presented for comparing the proposed design methods. Finally, experiments are conducted on an engine test bench and the results demonstrate the validity of the proposed design methods. Recommended by Editorial Board member Myotaeg Lim under the direction of Editor Hyun Seok Yang. The authors are grateful to Kai Zheng for his assistance of the model identification experiments. Jiangyan Zhang received the B.E. and M.E. degrees in Electrical Engineering, Yanshan University, China, in 2005 and 2008, respectively. Now, she is a Ph.D. candidate with the Department of Engineering and Applied Sciences, Sophia University, Tokyo, Japan. Her current research interests include nonlinear system control theory and applications to powertrain system control. Tielong Shen received the Ph.D. degree in Mechanical Engineering from Sophia University, Tokyo, Japan, March, 1992. From April 1992, he has been a faculty member of the Chair of Control Engineering in Department of Mechanical Engineering, Sophia University, where he currently serves as professor of the Department of Engineering and Applied Science. His research interests include control theory and application in mechanical systems, power systems, and automotive powertrain. Currently, he is an Associate Editor for the IEEE Control System Society Conference Editorial Board, and is serving as Associate Editor of Journal of Control Theory and Applications, and the Regional Editor Asia-Pacific for International Journal of Modeling, Identification and Control etc. Junichi Kako received the B.E. degree from Nagoya Institute of Technology, Nagoya, Japan. He joined Toyota Motor Corporation, Tokyo, Japan in 1989. He worked on various aspects of automotive powertrain control. From 1989 to 1994, he took part in the team for the development of Laboratory Automation (LA) system, Engineering Office Automation (EOD) system, and embedded system of powertrain control. During 1995–2001, he focused on the engine control systems in Powertrain Management Engineering Division. In 2002, he was with Future Project Division in which he was responsible for the R&D of model-based engine control system. Currently, he is developing engine control systems in the Powertrain Management Engineering Division, Toyota Motor Corporation. Shozo Yoshida received the M.S. degree in Engineering from Kyoto University, Kyoto, Japan. He joined Toyota Motor Corporation, Tokyo, Japan in 2000. From 2000 to 2004, he was with Future Project Division and worked on physical combustion modeling for Model-based Control Development. Since 2005, he has been with the Powertrain Management Engineering Division Toyota Motor Corporation, and is a member of the R&D of Model-based Engine Calibration.