结合l1/2范数与显著性约束的背景减除

传统背景减除模型在背景静止和前景对象移动较快时提取到的前景效果较好,但当背景变化或前景对象移动缓慢时容易将动态背景误判为前景或检测出的前景有较多空洞。针对传统背景减除模型在动态背景和前景对象移动缓慢条件下存在前景检测精度下降的问题,提出一种基于l_1/2范数与显著性约束的背景减除模型。将观测数据分为低秩背景、运动前景和动态干扰3类,利用l_1/2范数约束运动前景加强前景稀疏性,有效抑制动态背景对前景提取造成的干扰,提高运动前景在动态背景中的检测精度。引入视频每一帧的显著性约束,通过对每一帧图像进行低秩稀疏分解来检测移动缓慢的目标。实验结果表明,该模型对于复杂场景具有较强的适应能力,可有效去除动态背景对前景的干扰,快速检测出移动缓慢的前景对象,相比于l_1/1/2-RPCA背景减除模型的平均查全率、查准率和调和平均值分别提升了9、14和10个百分点。     

Continuum structural topological optimizations with stress constraints based on an active constraint technique

Stress‐related problems have not been given the same attention as the minimum compliance topological optimization problem in the literature. Continuum structural topological optimization with stress constraints is of wide engineering application prospect, in which there still are many problems to solve, such as the stress concentration, an equivalent approximate optimization model and etc. A new and effective topological optimization method of continuum structures with the stress constraints and the objective function being the structural volume has been presented in this paper. To solve the stress concentration issue, an approximate stress gradient evaluation for any element is introduced, and a total aggregation normalized stress gradient constraint is constructed for the optimized structure under the r?th load case. To obtain stable convergent series solutions and enhance the control on the stress level, two p‐norm global stress constraint functions with different indexes are adopted, and some weighting p‐norm global stress constraint functions are introduced for any load case. And an equivalent topological optimization model with reduced stress constraints is constructed,being incorporated with the rational approximation for material properties, an active constraint technique, a trust region scheme, and an effective local stress approach like the qp approach to resolve the stress singularity phenomenon. Hence, a set of stress quadratic explicit approximations are constructed, based on stress sensitivities and the method of moving asymptotes. A set of algorithm for the one level optimization problem with artificial variables and many possible non‐active design variables is proposed by adopting an inequality constrained nonlinear programming method with simple trust regions, based on the primal‐dual theory, in which the non‐smooth expressions of the design variable solutions are reformulated as smoothing functions of the Lagrange multipliers by using a novel smoothing function. Finally, a two‐level optimization design scheme with active constraint technique, i.e. varied constraint limits, is proposed to deal with the aggregation constraints that always are of loose constraint (non active constraint) features in the conventional structural optimization method. A novel structural topological optimization method with stress constraints and its algorithm are formed, and examples are provided to demonstrate that the proposed method is feasible and very effective. © 2016 The Authors. International Journal for Numerical Methods in Engineering published by John Wiley & Sons Ltd.     

多耦合拘束效应对P92钢蠕变裂纹扩展行为的影响

基于断裂力学理论,针对不同面内拘束效应下P92钢高温蠕变裂纹扩展(Creep crack growth,CCG)进行了数据模拟与分析。研究发现不同的拘束效应对CCG行为有着各异的影响,主要分析了试样几何形状、尺寸以及初始裂纹深度等对CCG行为的影响,并且着重研究比较这3种耦合拘束效应对CCG行为的影响程度。基于多种耦合拘束对CCG行为的影响程度做了横向比较,研究发现:试样几何形状对CCG行为的影响程度要大于几何尺寸,试样尺寸和初始裂纹深度对CCG行为的影响程度相似,试样形状比初始裂纹深度的影响程度更大。     

OCL约束验证与实现的方法

为了能在运行时验证OCL约束,提出了约束验证框架.针对OCL-Java代码(即OCL约束对应的可执行Java代码)插入的简单方案和封装方法存在的不足,给出了改进的代码插入方案,其中应用异常处理技术从而能够捕获冲突的约束.为了使OCL-Java代码根据设计的代码模式插入到Java程序中,对Java解析器作了修改.最后,实验结果表明了该方法的可行性.     

利用块几何约束及视差概率的立体匹配算法

为了解决倾斜表面或曲面的匹配问题,提出了一种基于图像分割块之间的几何约束和视差值的概率分布信息的视差估算方法。在一个全局能量函数中增加了图像分割块之间的几何约束项,通过计算匹配能量得到分割块的最优视差平面。为了确定可信像素和可信分割块,利用了视差的概率分布信息。同时,利用了分割块之间的几何约束和分割块内像素之间的约束来估计不可信像素点的视差值。用包含大视差范围、更多倾斜表面、曲面和弱纹理表面等典型图像对所建议的算法进行测试,实验结果表明,该方法对于存在倾斜表面和曲面的立体视差计算是有效的。     

基于知识点约束的遗传算法组卷策略的研究

基于遗传算法的自动组卷策略采用分段实数编码,具有自适应型的交叉和变异遗传算子,基于知识点约束的分题型的组卷算法思想,为自动组卷算法的进一步发展提供了研究方向.     

基于SS序列集成电路不规则模块布图算法

针对Single-Sequence的集成电路布图,在SS编解码应用对芯片中各单元的摆放进行优化,从而达到芯片面积利用率最大化.重点介绍了利用SS序列解决不规则模块摆放问题,使得SS布图功能更灵活多变.     

Hybrid dynamics of stochastic programs

We provide Stochastic Concurrent Constraint Programming (sCCP), a stochastic process algebra based on CCP, with a semantics in terms of hybrid automata. We associate with each sCCP program both a stochastic and a non-deterministic hybrid automaton. Then, we compare such automata with the standard stochastic semantics (given by a Continuous Time Markov Chain) and the one based on ordinary differential equations, obtained by a fluid-flow approximation technique. We discuss in detail two case studies: Repressilator and the Circadian Clock, with particular regard to the robustness exhibited by the different semantic models and to the effect of discreteness in dynamical evolution of such systems.     

Multi-item inventory model of breakable items with stock-dependent demand under stock and time dependent breakability rate

Items made of glass, ceramic, etc. are normally stored in stacks and get damaged during the storage due to the accumulated stress of heaped stock. These items are known as breakable items. Here a multi-item inventory model of breakable items is developed, where demands of the items are stock dependent, breakability rates increase linearly with stock and nonlinearly with time. Due to non-linearity and complexity of the problem, the model is solved numerically and final decisions are made using Genetic Algorithm (GA). In a particular case, model is solved analytically as well as numerically and results are compared. Models are developed with both crisp and uncertain inventory costs. For uncertain inventory costs both fuzzy and stochastic parameters are considered. A chance constrained approach is followed to deal with simultaneous presence of stochastic and fuzzy parameters. Different numerical examples are used to illustrate the problem for different cases.     

基于约束放松的自适应Web服务选取方法的研究

已有服务选取都是把问题规划为针对某个模型寻求最优解或者次优解的过程,不考虑用户约束对Web服务选取的影响,而在SLA模型中,用户对服务的选取是有约束的,如果约束不满足,选取算法直接结束,要求用户重新给出约束定义,增加了用户与选取过程的交互.为了减少用户的操作次数,本文为用户的约束定义了约束放松模型,对于约束不能满足的情况下,自动地对已有模型进行条件放松,定义了基于分布式约束满足问题求解的服务选取过程,将约束放松模型应用于选取过程,进行自适应的选取.通过与已有的选取算法进行实验比较,本文提出的自适应组合服务选取算法,考虑了用户的约束条件,在选取失败时,基于放松模型进行自动约束条件的更新,进行服务的重新选取,减少了与用户交互次数,有效提高了有约束的组合服务选取的执行效率.