使用程序静态分析技术识别Web程序中的用例结构

本文利用程序静态分析技术对Web程序中的用例识别进行了深入研究。对Web页面的用户行为进行了深入分析并建模,从行为模型中提取出描述整个系统中用户行为的事件流图,结合采用现有的从目的成功语句出发、以必经结点为基础的用例识别方法,从各个Web页面中提取的独立控制流图,形成全局控制流图,进行全局用例识别。与以前已有工作相比,本文考虑了Web程序的特点,尤其是前端程序如何实现用户行为,并在前端程序中进行精确的用例识别。本文采用目前使用较为广泛的JSP程序进行了实验,结果表明该方法能够有效识别JSP程序中的用例结构。     

基于概率支持向量回归的产品设计时间预测模型

为使产品设计时间预测既克服小样本、异方差噪声问题,又提供除预测值以外的其他有用信息,建立概率支持向量回归(PSVR)模型。首先,在异方差回归模型基础上设计概率约束条件,以使预测值以较大概率位于真实值的某邻域,结合具有参数不敏感损失函数的支持向量回归确定优化目标,提出PSVR。然后,将最大完工时间知识嵌入进PSVR的约束条件,用以确定真实值邻域的宽度,将交叉验证与遗传算法相结合以确定PSVR的相关参数。最后,以注塑模具设计的实例进行分析,结果表明基于PSVR的时间预测方法可同时提供有效的预测值和预测区间。     

信号交叉口影响区路边停车的交通流延误模型

为了提高路边停车带的利用率和减小其对路段交通流的影响,以城市一幅路为研究对象,从分析路边停车带的车辆驶出、汇入时对道路动态交通流的影响入手,考虑路边停放车辆及信号控制的共同作用,运用稳态理论分析车辆在信号交叉口的延误时间,建立了停车带第i个泊位车辆驶出影响下,单位时间内机动车流在信号交叉口的延误-Di与停车带离开停车线的距离L0、路段交通流量以及信号周期时长C和红灯时间r的关系模型.以南京市天津路路边停车带为例,重点分析了距离交叉口停车线最近停车位的车辆驶出引起的延误时间-D1随L0、信号周期时长C、红灯时间r变化的规律,结果表明,随着交叉口饱和流率的不同,停车带与交叉口的距离可分别在60m和90m区间设定,而交叉口信号周期长度和红灯时间比例均对延误结果存在显著影响.     

采用离散元法分析路面结构的疲劳特征

为了在考虑疲劳损伤和恢复性能的基础上,获取路面结构在重复荷载作用下的疲劳特征,采用离散元方法建立了路面结构模型,并分析了在正弦重复荷载作用下路面结构沥青层底部的受力特性.由于路面荷载的随机性,分别以荷载大小、荷载间歇时间、荷载作用次序为考察对象,分析其对路面结构沥青层底受力特征的影响.研究结果表明:随着周期荷载作用次数的增大,沥青层底部拉应力快速增大至某一基础值,并在此基础值附近呈周期性波动,拉应变则在以一定幅值波动的同时,呈现整体水平先急剧增大后缓慢增大的规律;延长荷载作用的间歇时间,虽然一定程度上增大了拉应力和拉应变的波动幅值,但却显著降低了沥青层底拉应力和拉应变的整体水平;不同的荷载作用次序在沥青层底引起的应力应变状态不同,从小至大序列的荷载对沥青层造成的损伤明显小于从大至小序列的荷载.     

网络媒介影响下建筑相关问题初探

自工业革命以来形成的社会生活方式,在信息数字技术的影响下发生着悄无声息的变化.从原子到比特,数字化生存是必然的趋势.网络媒介的出现使得人们在交流和机会等方面获得平等的权利.随之产生的人们的生活方式、行为的改变已经为艺术领域带来了新的变化.通过对艺术创作的考察,为建筑领域的相关问题提供了参照、借鉴.在建筑创作中,＂图＂与＂底＂的关系正在模糊化,信息交流方式更多样化并具有一定的阶段性,动态的模数将导致更具个性化的设计,空间界面更为柔化,设计成果的生成也具有了更多的不可预见性.     

Renewal strategies for old industrial areas in the post-industrial age—Take “Zurich-West” in Switzerland as an example

This paper introduces and analyses the renewal planning of “Zurich-West”, an old industrial area. It points out that systematic and comprehensive target orientation, extensive collaboration in argumentation and decision-making, and the design ideas of coexistence and integration are the key to its success. Considering the status quo of China’s old industrial district remodeling, the paper offers some suggestions. Supported by the National Natural Science Foundation of China (Grant No. 50878045)     

Profiling selected paths with loops

Path profiling records the frequency of each path in an executed program.To accomplish profiling,probes are instrumented in the program and executed as the program runs.So,the number of probes has a significant impact on the efficiency of a profiling technique.By profiling only the interesting paths,existing techniques try to improve the profiling efficiency by reducing the number of probes and optimize path encodings for efficient storage.However,they lack accuracy,waste time on running uninteresting paths,and can mainly deal with acyclic paths.In this article,a novel technique called Profiling Selected Paths(PSP)is introduced to profile selected paths,which enables custom selection for both acyclic and cyclic paths and increases the execution efficiency by early termination on uninteresting paths.Theoretical analysis and experimental evaluation indicate that PSP performs better than existing techniques.     

Neural network-based adaptive decentralized learning control for interconnected systems with input constraints

In this paper, the neural network-based adaptive decentralized learning control is investigated for nonlinear interconnected systems with input constraints. Because the decentralized control of interconnected systems is related to the optimal control of each isolated subsystem, the decentralized control strategy can be established by a series of optimal control policies. A novel policy iteration algorithm is presented to solve the Hamilton–Jacobi–Bellman equation related to the optimal control problem. This algorithm is implemented under the actor-critic structure where both neural networks are simultaneously updated to approximate the optimal control policy and the optimal cost function, respectively. The additional stabilizing term is introduced and an improved weight updating law is derived, which relaxes the requirement of initial admissible control policy. Besides, the input constraints of interconnected systems are taken into account and the Hamilton–Jacobi–Bellman equation is solved in the presence of input constraints. The interconnected system states and the weight approximation errors of two neural networks are proven to be uniformly ultimately bounded by utilizing Lyapunov theory. Finally, the effectiveness of the proposed decentralized learning control method is verified by simulation results.     

Compositional metric learning for multi-label classification

Multi-label classification aims to assign a set of proper labels for each instance, where distance metric learning can help improve the generalization ability of instance-based multi-label classification models. Existing multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature space. In this paper, a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label space. On one hand, compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on component bases. On the other hand, compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label spaces. Due to the compositional nature of employed distance metric, the resulting problem admits quadratic programming formulation with linear optimization complexity w.r.t. the number of training examples.We also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional metric. Extensive experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.