A bidding decision index for construction contractors

Results from applying the model on a sample of contractors, the majority of whom were international and operating in Egypt, reinforces the credibility of the developed methodology, claim the authors.     

Microscopic fatigue processes in a plain-weave glass-fibre composite

This paper focuses on the microscopic damage and progressive failure of a composite reinforced by plain-weave glass cloth under tensile fatigue loading. The fatigue process was divided into three stages like that of multi-directional laminates. It was found that the internal damage at each stage (matrix cracks, debonds in the weft, successive debonds in the warp and ‘metadelaminations’ between warps and wefts) occurred near the cross-over point of the fabric. The modulus decay mechanism was explained by considering the progression of this internal damage. From the end of the first stage to the beginning of the middle stage, a characteristic damage state (CDS) (called a ‘meta-CDS’) was observed. It was found that woven composites have a unit area of damage accumulation (called a ‘unit cell’) and the damage of each unit cell and its distribution control the total fatigue damage of the material.     

Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions

We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.     

遗传算法与文本特征选择方法相结合的研究

对文本特征选择的传统方法进行了深入分析并对各自的优缺点进行了详细的对比;其次,通过利用遗传算法自身的寻优特点,把它与传统特征选择方法相结合,并得出了算法。最后,在对各自算法进行了分析,表明了在性能上有了明显的提高。     

基于SQL的多条件选择智能实现算法

B/S架构的信息系统开发中,一般情况下,根据用户的选择,n个条件的输入组合数是2n个。但是,该文中提出的算法,其组合数仅为n到n(n+1)/2,大大简化了编程。     

Creating change and driving innovation in highly automated and lean organizations: The Temporal Think Tank™ (T3™)

This paper introduces the concept of the temporal think tank™ (T3™), a temporary in-house research center, based on case studies with a highly automated global manufacturer and in various small-sized manufacture (SME's). The T3™ brings individuals from across the organization together in a team setting in which they are charged to look at the future, develop their decision-making skills, and use entrepreneurial thinking to incubate new processes and/or products. After an appropriate time in the temporal think tank™, the individuals are asked to return to their regular positions to implement the best practices and best ideas and doing so spur the organization forward. At intervals, the temporal think tanks™ can be reconvened, or reconstituted with new participants. This approach prepares technology champions for the SME, who can return to the organization with a fresh outlook and renewed energy to keep the enterprise at the peak of efficiency and effectiveness.     

A unifying criterion for unsupervised clustering and feature selection

Exploratory data analysis methods are essential for getting insight into data. Identifying the most important variables and detecting quasi-homogenous groups of data are problems of interest in this context. Solving such problems is a difficult task, mainly due to the unsupervised nature of the underlying learning process. Unsupervised feature selection and unsupervised clustering can be successfully approached as optimization problems by means of global optimization heuristics if an appropriate objective function is considered. This paper introduces an objective function capable of efficiently guiding the search for significant features and simultaneously for the respective optimal partitions. Experiments conducted on complex synthetic data suggest that the function we propose is unbiased with respect to both the number of clusters and the number of features.