Detection of delamination in laminated composites with limited measurements combining PCA and dynamic QPSO

This paper presents an output only damage diagnostic algorithm based on frequency response functions and the principal components for health monitoring of laminated composite structures. The principal components evaluated from frequency response data, are employed as dynamical invariants to handle the effects of operational/environmental variability on the dynamic response of the structure. Finite element models of a laminated composite beam and plate are used to generate vibration data for healthy and damaged structures. Three numerical examples include a laminated composite beam, cantilever plate made of carbon–epoxy and a laminated composite simply supported plate. Varied levels of delamination of laminated composite plies and matrix cracking at varied locations in the plies are simulated at different spatial locations of the structure. Numerical investigations have been carried out to identify the spatial location of damage using the proposed principal component analysis (PCA) based algorithm. In order to limit the number of sensors on the structure, an optimal sensor placement algorithm based on PCA is employed in the present work and the effectiveness of the proposed algorithm with a limited number of sensors is also investigated. Finally, the inverse problem associated with the detection of delamination and matrix cracking is formulated as an optimization problem and is solved using the newly developed dynamic quantum particle swarm optimization (DQPSO) algorithm. Studies carried out and presented in this paper clearly indicate that the proposed SHM scheme can robustly identify the instant of damage, spatial location, the extent of delamination and matrix cracking even with limited sensor measurements and also with noisy data.     

Automating correctness verification of artifact-centric business process models

ContextThe artifact-centric methodology has emerged as a new paradigm to support business process management over the last few years. This way, business processes are described from the point of view of the artifacts that are manipulated during the process.ObjectiveOne of the research challenges in this area is the verification of the correctness of this kind of business process models where the model is formed of various artifacts that interact among them.MethodIn this paper, we propose a fully automated approach for verifying correctness of artifact-centric business process models, taking into account that the state (lifecycle) and the values of each artifact (numerical data described by pre and postconditions) influence in the values and the state of the others. The lifecycles of the artifacts and the numerical data managed are modeled by using the Constraint Programming paradigm, an Artificial Intelligence technique.ResultsTwo correctness notions for artifact-centric business process models are distinguished (reachability and weak termination), and novel verification algorithms are developed to check them. The algorithms are complete: neither false positives nor false negatives are generated. Moreover, the algorithms offer precise diagnosis of the detected errors, indicating the execution causing the error where the lifecycle gets stuck.ConclusionTo the best of our knowledge, this paper presents the first verification approach for artifact-centric business process models that integrates pre and postconditions, which define the behavior of the services, and numerical data verification when the model is formed of more than one artifact. The approach can detect errors not detectable with other approaches.     

Automatic generation algorithm of expected results for testing of component-based software system

ContextA component-based software (CBS) system is a typical example of a composite component, which is composed of two or more atomic components. In the test of a CBS system, it is necessary to automatically generate expected results because they are compared with the actual results.ObjectiveThis paper proposes an automatic generation algorithm of expected results for the testing of a CBS system.MethodThe algorithm analyzes Input/Output (I/O) relationships of a CBS system to identify inputs that influence its outputs. Then, the algorithm uses test cases of atomic components for each input and automatically generates expected results. To evaluate the proposed approach, we compare the proposed approach with the other I/O relationship based approach via two case studies which are the CBS systems for guide robot. The comparison shows the effectiveness of our approach.ResultsTo verify effectiveness of the proposed approach, we measure the cost of manual generation of expected results for each case study. The costs are from 0.001 to 0.015 in the first case study, and are from 0.590 to 0.998 in the second case study.ConclusionThis proposed approach reduces the time-consuming and error-prone task which manually generates expected results for the testing of a CBS system.     

离散Walsh变换在超声治疗无损测温中的应用

利用高强度聚焦超声对新鲜的离体猪肉进行辐射,并通过B超获取辐射前后的超声图像。采用二维离散Walsh变换法,从辐射前后的超声图像的差图像中分离出低频分量,作为反映温度信息的参数。选取确定系数作为衡量误差的参数,通过曲线拟合,发现二维离散Walsh变换直流分量与温度呈近似线性关系,且多组数据的斜率非常接近,与传统的灰度均值测温法相比,实验结果表明,该方法误差更小,温度分辨率更高,使用正交矩阵变换,符合计算机语言的逻辑,仿真检测时间较短,在实际应用中能为超声无损测温提供准确可靠的依据。     

地下结构震害风险信息融合方法研究

为提高地下结构震害风险判决的科学性和智能化水平,提出一种基于证据理论的地下结构震害风险信息融合方法。在地下结构震害风险智能监测的基础上,根据震害风险融合原理,构建基于智能监测和专家判决的地下结构震害风险融合模型。遵循证据理论的原则和模型规则,构造统一的地下结构震害状态空间和风险概率表达方式,建立风险融合证据框架,确定判决规则,设计风险融合证据模型组件。计算结果表明,该模型能有效提高地下结构震害信息诊断的可信度,充分显示了该融合模型组件的有效性。     

基于RBAC模型的权限管理改进研究与实现

针对传统RBAC模型无法满足业务审批系统要求的权限可分级、能动态管理的新需求,提出一种改进后的基于角色的控制访问模型。该模型能够动态地创造权限、分配权限、撤销权限,达到资源分离、多级权限管理的目的。结合一个实际项目,采用脚本语言JS （JavaScript）和主流的SSH框架,给出该模型的实现过程,增强各类角色对权限的分级、动态管理,该模型的可行性由此得到验证。     

KICA模式匹配的动态过程监控与故障诊断方法

针对复杂化工过程具有的非线性、非高斯性和动态特征,提出了基于核独立成分分析(KICA)的模式匹配方法,用于动态过程监控和诊断。首先,利用滑动窗建立基准集与测试集的KICA模型,提取各自的核独立元:其次,融合余弦函数绝对值度量和距离度量,提出新的不相似度监控指标,识别训练与测试操作期间的相似模式,进行故障检测:最后,基于两类数据的核子空间之间的差异子空间,获得每个过程变量方向与该差异子空间之间的互信息,并定义新的非线性非高斯贡献度指标,进行故障诊断。基于污水处理过程的仿真结果表明,与主成分分析不相似度因子的方法、标准的独立成分分析(ICA)统计指标方法及标准的ICA T~2/SPE指标融合的贡献度方法相比,本文提出的方法具有更好的检测能力与故障诊断效果。     

基于动态权限集的Android强制访问控制模型

针对Android存在的特权提升攻击问题,提出了基于动态权限集的Android强制访问控制模型DP_ManDroid。该模型首先分析强连通分支的权限分布特性,构建动态的权限集划分;然后在信息流与权限集耦合的基础上,抽象权限提升路径;最后提出线性时间的访问控制算法,并通过动态追踪权限集,实现了细粒度的决策控制。与现有 安全模型的对比,以及在原型系统上的仿真结果表明,所提出的安全模型很好地抵御了特权提升攻击,同时降低了时间复杂度。     

基于RBAC模型的权限组件

基于信息化系统的特点及其对权限控制的需要,在.NET框架下,基于RBAC模型设计与实现了一套权限控制组件,并在设计过程中对RBAC模型进行了扩展,引入了权限委托、分级授权,UBAC等特性,并将该组件应用于多个EIS系统的开发过程中,结果表明该组件能够很好的集成于信息化系统中,能够减轻权限开发的复杂度,提高权限管理工作的效率.     

服务器日志挖掘在电力业务系统功能推荐中的应用

提出了一种基于服务器日志挖掘的电力业务系统功能推荐服务,首先从电力业务系统服务器日志中获取用户日志数据,然后对含有"脏"数据的用户日志数据进行预处理,以适应数据挖掘与处理;接着由待处理的数据计算用户访问兴趣度,并基于改进的K均值聚类算法将用户访问兴趣度数据集划分为多个具有相近兴趣度的用户集合,最终为用户提供功能个性化推荐服务.实验结果证明该方法在实现电力业务系统信息推荐方面具有较好的效果.