基于宏块重要性测度的重同步方法

重同步是视频传输中一种重要的错误恢复方法。对视频传输过程中的失真进行分析后,提出了基于宏块重要性测度的重同步新方法。通过定义宏块重要性测度,充分利用视频信息的重要性结合码流长度,对压缩视频流进行打包,使得视频包的重要性保持一致。分别采用基于宏块个数、基于码流长度和本方法对不同类型的测试序列进行了视频包丢失实验。结果表明,本文方法平均失真的波动最小,因此在传输过程中,无论哪一个视频包发生丢失,都不会对视频质量带来剧烈的波动,提高了主观视觉效果。     

一种基于数据场的复杂网络聚类算法

针对当前复杂网络研究中聚类的热点问题,提出了一种基于数据场的复杂网络聚类算法,该算法通过一种基于互信息的方法计算出复杂网络中节点的重要性,然后通过数据场中节点的势来划分网络的簇结构。实验证明,该算法在计算时间和精度上具有一定的优势。     

Expertise ranking using activity and contextual link measures

The Internet has transformed from a Web of content to a people-centric Web. People actively use social networking platforms to stay in contact with friends and colleagues. The availability of rich Web-based applications allows people to collaborate and interact online. These connected online societies provide an immense potential for future business models such as crowdsourcing. Based on the idea of crowdsourcing, we developed a framework that enables people to offer their skills and expertise as human-provided services (HPS) which can be discovered and requested on demand. Automated techniques for expertise mining become thus essential in such applications. We introduce a link intensity based ranking model for recommending relevant users in human collaborations. Here we argue that an expertise ranking model must consider the users' availability, activity level, and expected informedness. We present DSARank for estimating the relative importance of persons based on reputation mechanisms in collaboration networks. We test the applicability of our ranking model by using datasets obtained from real human interaction networks including mobile phone and email communications. The results show that DSARank is better suited for recommending users in collaboration networks than traditional degree-based methods.     

汽车起步阻止装置的设计

汽车起步阻止装置,是一种能够主动保护乘客的乘车安全,并能提醒司机注意车门开关信息的装置,能有效避免乘客乘车安全事故的发生。介绍了汽车起步阻止装置的工作原理、组成和安装位置,并指出了起步阻止装置重要组成部件二位三通电磁阀安装位置的重要性。     

Variance reduction by truncated multimodal importance sampling

This paper presents an importance sampling method for system reliability analyses of structures with failure domains defined by linear or appropriately linearized surfaces. Truncated simulation as a new technique is suggested. It provides an advantage of locating all samples in the failure domain and thus increasing computation efficiency. The variance of the estimator is evaluated by an analytically derived upper bound. It is compared with that of the conventional Monte Carlo method by a variance change factor for conservative estimation of the increase in accuracy and efficiency. The upper bound of variance can be used for a priori determination of the required sample size, given an acceptable maximum error associated with a confidence level. Various application examples of both series and parallel systems are included for illustration.     

A matching algorithm for the distribution of human pancreatic islets

The success of human pancreatic islet transplantation in a subset of type 1 diabetic patients has led to an increased demand for this tissue in both clinical and basic research, yet the availability of such preparations is limited and the quality highly variable. Under the current process of islet distribution for basic science experimentation nationwide, specialized laboratories attempt to distribute islets to one or more scientists based on a list of known investigators. This local decision making (LDM) process has been found to be ineffective and suboptimal. To alleviate these problems, a computerized Matching Algorithm for Islet Distribution (MAID) was developed to better match the functional, morphological, and quality characteristics of islet preparations to the criteria desired by basic research laboratories, i.e., requesters. The algorithm searches for an optimal combination of requesters using detailed screening, sorting, and search procedures. When applied to a data set of 68 human islet preparations distributed by the Islet Cell Resource (ICR) Center Consortium, MAID reduced the number of requesters that (a) did not receive any islets, and (b) received mis-matched shipments. These results suggest that MAID is an improved more efficient approach to the centralized distribution of human islets within a consortium setting.     

A new computational method of a moment-independent uncertainty importance measure

For a risk assessment model, the uncertainty in input parameters is propagated through the model and leads to the uncertainty in the model output. The study of how the uncertainty in the output of a model can be apportioned to the uncertainty in the model inputs is the job of sensitivity analysis. Saltelli [Sensitivity analysis for importance assessment. Risk Analysis 2002;22(3):579-90] pointed out that a good sensitivity indicator should be global, quantitative and model free. Borgonovo [A new uncertainty importance measure. Reliability Engineering and System Safety 2007;92(6):771-84] further extended these three requirements by adding the fourth feature, moment-independence, and proposed a new sensitivity measure, δ_i. It evaluates the influence of the input uncertainty on the entire output distribution without reference to any specific moment of the model output. In this paper, a new computational method of δ_i is proposed. It is conceptually simple and easier to implement. The feasibility of this new method is proved by applying it to two examples.     

International survey on PSA figures of merit

Probabilistic safety assessment (PSA) figures of merit represent one of the most interesting topics in the use of PSA for safety-related applications. This is all the more interesting as the viewpoints and opinions of different experts on the use of importance factors could diverge. Even when the experts agree, the use of these measures is likely to involve a number of difficulties or pitfalls, especially if they are used too “mechanically”, without proper care. EDF considered the topic to be sufficiently important and controversial to justify exploratory studies as part of the scientific and technical monitoring program and to get advice, comments, and critical viewpoints from a number of international PSA experts and practitioners. This report presents a synthesis of the results of this international survey.     

Extreme value distributions for nonlinear transformations of vector Gaussian processes

Approximations are developed for the marginal and joint probability distributions for the extreme values, associated with a vector of non-Gaussian random processes. The component non-Gaussian processes are obtained as nonlinear transformations of a vector of stationary, mutually correlated, Gaussian random processes and are thus, mutually dependent. The multivariate counting process, associated with the number of level crossings by the component non-Gaussian processes, is modelled as a multivariate Poisson point process. An analytical formulation is developed for determining the parameters of the multivariate Poisson process. This, in turn, leads to the joint probability distribution of the extreme values of the non-Gaussian processes, over a given time duration. For problems not amenable for analytical solutions, an algorithm is developed to determine these parameters numerically. The proposed extreme value distributions have applications in time-variant reliability analysis of randomly vibrating structural systems. The method is illustrated through three numerical examples and their accuracy is examined with respect to estimates from full scale Monte Carlo simulations of vector non-Gaussian processes.     

A study using Monte Carlo Simulation for failure probability calculation in Reliability-Based Optimization

In this study, a Reliability-Based Optimization (RBO) methodology that uses Monte Carlo Simulation techniques, is presented. Typically, the First Order Reliability Method (FORM) is used in RBO for failure probability calculation and this is accurate enough for most practical cases. However, for highly nonlinear problems it can provide extremely inaccurate results and may lead to unreliable designs. Monte Carlo Simulation (MCS) is usually more accurate than FORM but very computationally intensive. In the RBO methodology presented in this paper, limit state approximations are used in conjunction with MCS techniques in an approximate MCS-based RBO that facilitates the efficient calculation of the probabilities of failure. A FORM-based RBO is first performed to obtain the initial limit state approximations. A Symmetric Rank-1 (SR1) variable metric algorithm is used to construct and update the quadratic limit state approximations. The approximate MCS-based RBO uses a conditional-expectation-based MCS, that was chosen over indicator-based MCS because of the smoothness of the probability of failure estimates and the availability of analytic sensitivities. The RBO methodology was implemented for an analytic test problem and a higher-dimensional, control-augmented-structure test problem. The results indicate that the SR1 algorithm provides accurate limit state approximations (and therefore accurate estimates of the probabilities of failure) for these test problems. It was also observed that the RBO methodology required two orders of magnitude fewer analysis calls than an approach that used exact limit state evaluations for both test problems.