Checking determinism of XML Schema content models in optimal time

We consider the determinism checking of XML Schema content models, as required by the W3C Recommendation. We argue that currently applied solutions have flaws and make processors vulnerable to exponential resource needs by pathological schemas, and we help to eliminate this potential vulnerability of XML Schema based systems. XML Schema content models are essentially regular expressions extended with numeric occurrence indicators. A previously published polynomial-time solution to check the determinism of such expressions is improved to run in linear time, and the improved algorithm is implemented and evaluated experimentally. When compared to the corresponding method of a popular production-quality XML Schema processor, the new implementation runs orders of magnitude faster. Enhancing the solution to take further extensions of XML Schema into account without compromising its linear scalability is also discussed.     

A DSS for integrated distribution of empty and full containers

A DSS integrating empty and full containers transshipment operations is presented, addressing the typically unbalanced export/import containers trading problem. The problem is modeled as a network, where nodes represent customers, leasing companies, harbors and warehouses, while arcs represent transportation routes. The underlying mathematical model operates in stages, first prioritizing and adjusting full containers demands considering available empty containers supplies, and then statically optimizing costs. Transportation routes are registered and dynamically controlled, cyclically, for a given time horizon. The DSS is flexible, allowing several parameters to be configured. Experimental examples using randomly generated parameters were conducted to evaluate the effectiveness of the system.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

基于云计算的营销决策支持系统

设计并实施了一种基于云计算的营销决策支持系统,和其他管理决策子系统发生交互,共同构成了完整的现代企业经营决策支持系统.系统的数据库采用分布式设计,使得系统既有独立处理本地数据库的能力,也可读取异地数据库中的数据.系统模型库包含多种营销因素的决策模型,并采用模型组合的思想,将复杂的决策问题通过模型之间的组合来实现.在流程设计上充分考虑人机交互,将用户的经验判断纳入到决策过程中.完成了系统的开发,并用仿真数据进行了系统测试,结果表明系统基本运行稳定,各子模块衔接良好,与其他子系统实现了数据共享.     

Decision support for Cybersecurity risk planning

Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions.     

基于多媒体多智能Agents系统的群体决策支持系统集成化设计研究

分析了Ｄeｓａｎｅｔｉｓ＆Ｇａｌｌｕｐｅ群体决策支持系统（Ｇｒｏｕｐ Ｄｅｃｉｓｉｏｎ Ｓｕｐｐｏｒｔ,简称ＧＤＳＳ）初步设计的通用模式,提出了将多媒体技术、智能Ａｇeｎｔs技术、数据仓库技术Ｉｎｔｅｒｎｅｔ／Ｉｎｔｒａｎｅｔ技术等有机地集成于ＧＤＳＳ的集成化设计方案,并就其关键技术功能结构方案设计进行了研究．     

Gas particle industrial flow simulation using RANSTAD

A turbulent gas particle finite-volume flow simulation of a representative coal classifier is presented. Typical values of the loading ratio permit a one-way coupling analysis. As a case study, the computational fluid dynamics code,ranstad, and the modelling aspects are discussed in some detail. The simulation indicates that small (≈ 30 μm) coal particles pass through the classifier to the furnace but that large (≈ 300 μm) particles are captured and remilled. The computational simulation indicates that the classifier performance can be improved by internal geometric modification. The commitment of the Electricity Commission of New South Wales (Pacific Power) to the exploitation of Computational Engineering for the improvement of all aspects of electricity generation is acknowledged.     

Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process

In the past decade, support vector machines (SVMs) have gained the attention of many researchers. SVMs are non-parametric supervised learning schemes that rely on statistical learning theory which enables learning machines to generalize well to unseen data. SVMs refer to kernel-based methods that have been introduced as a robust approach to classification and regression problems, lately has handled nonlinear identification problems, the so called support vector regression. In SVMs designs for nonlinear identification, a nonlinear model is represented by an expansion in terms of nonlinear mappings of the model input. The nonlinear mappings define a feature space, which may have infinite dimension. In this context, a relevant identification approach is the least squares support vector machines (LS-SVMs). Compared to the other identification method, LS-SVMs possess prominent advantages: its generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of the competing methods, and more importantly, the performance does not depend on the dimensionality of the input data. Consider a constrained optimization problem of quadratic programing with a regularized cost function, the training process of LS-SVM involves the selection of kernel parameters and the regularization parameter of the objective function. A good choice of these parameters is crucial for the performance of the estimator. In this paper, the LS-SVMs design proposed is the combination of LS-SVM and a new chaotic differential evolution optimization approach based on Ikeda map (CDEK). The CDEK is adopted in tuning of regularization parameter and the radial basis function bandwith. Simulations using LS-SVMs on NARX (Nonlinear AutoRegressive with eXogenous inputs) for the identification of a thermal process show the effectiveness and practicality of the proposed CDEK algorithm when compared with the classical DE approach.     

舰船综合保障中数据综合管理研究

介绍了舰船综合保障数据综合管理的有关内容，包括信息数据分类、数据综合管理功能分类以及综合保障数据库的设计思路．根据舰船的特点设计了舰船综合保障数据管理系统的硬件框架、软件体系、开发以度数据库工具．所研究的设计思路已在某船舶综合保障信息管理系统中得到应用,证明该设计模式是科学、可行的     

Characteristics of Metal Droplets in Slag Tapped from the Blast Furnace

Slag samples, hot‐metal samples and hot‐metal temperatures were obtained during tapping of two blast furnaces. Sampling was carried out at different time points during tapping of three separate heats. The size distribution and composition of metal droplets found in the slag were determined using scanning electron microscopy. Only metal droplets above 0.75 μm could be counted and analysed. All droplets were below 8 μm in diameter and the great majority of these droplets were found to be between 0.75 and 2 μm. The size distribution did not differ significantly for different slag samples. Iron was the main droplet component. Electron probe microanalysis showed that the droplets contained small amounts of carbon. The percentage of the area in a studied cross‐section that was covered with metal droplets varied between 0.01 and 0.07%. Calculations based on Stoke's law showed that the distance droplets travel in the slag is in the micron range. Slag samples taken in the beginning of slag tapping contained more droplets than those taken in the middle of slag tapping, an indication that most droplets can be found in the area near the furnace wall. Some droplets were determined to have magnesium enrichment at the external surface.