盾构运动过程的数值分析

采用模拟盾构推进过程的方法,对土的工程性质和盾构尺寸对盾构姿态的影响进行数值分析.通过分析得出了盾构单位距离角度的变化与地层基床系数和盾构尺寸之间的关系,为盾构姿态的控制提供理论依据,并为建立盾构运动过程的数学模型打下基础.     

基于支持向量机的中长期电力负荷预测研究与应用

采用一种新的机器学习方法——支持向量机,建立了中长期电力负荷预测模型.阐述了支持向量机的基本内容,对影响电力负荷诸多因素的样本集进行了标准化处理和主因素分析(PCA).采用Libsvm训练了数据集,并与灰色预测GM(1,1)模型、多元线性回归模型、模糊ISODATA聚类模型和BP神经网络进行对比.结果表明,此算法有更高的准确性,可为电力负荷预测提供有效依据.     

一种新型虚轴机床的结构设计与位置分析

提出了以四自由度空间并联机构作为主进给机构,辅以双向移动工作台实现多坐标数控加工的一种新型虚拟轴机床的结构设计方案。该虚拟轴机床具有工作空间大、灵巧性好、位置与姿态解耦等优点。计算了主进给机构的自由度,给出了位置反解的方法,推导了位置正确的封闭方程,并给出了数值实例。     

Explaining the High False Positive Rate of the South Oaks Gambling Screen

The South Oaks Gambling Screen (SOGS) has been used extensively in estimating the prevalence of pathological gambling but produces a large number of false positive classifications. Ladouceur et al. (, Journal of Gambling Studies, 16, pp. 1–24) claim that misunderstanding of SOGS items is responsible for the high false positive rate. However, their study is open to a number of methodological criticisms. The current study, where clinical and non-clinical gamblers complete the SOGS with and without clarification, overcomes these problems. Results suggest that clarification does not have a significant overall effect on SOGS scores. This implies that item misunderstanding is not responsible for the false positive rate of the SOGS.     

A single-machine learning effect scheduling problem with release times

In this paper, we investigate a single-machine problem with the learning effect and release times where the objective is to minimize the makespan. A branch-and-bound algorithm incorporating with several dominance properties and lower bounds is developed to derive the optimal solution. A heuristic algorithm is proposed to obtain a near-optimal solution. The computational experiments show that the branch-and-bound algorithm can solve instances up to 36 jobs, and the average error percentage of the proposed heuristic is less than 0.11%.     

陈平在楚汉战争中的作用

陈平在楚汉战争中的作用朱顺玲陈平，阳武户牖（河南原阳县西北）人。秦末大起义时，初投魏玉咎，因说魏王不听，又受人排挤而离去。接着投奔项羽，任都尉。公元前２０５年三月投汉王刘邦，任护军都尉。在公元前２０６年──公元前２０２年的楚汉战争中，刘邦最后打败了项...     

基于PSO和SVM的上市公司财务危机预警模型

提出了一种将经过改进的离散粒子群(PSO)算法和支持向量机(SVM)相结合的算法,以选择最优的指标集并用于财务危机预警。将此算法应用于上市公司的数据,检验模型提前3年的预警效果,最后与常见的主成分分析方法与SVM相结合的模型进行对比,结果证明了PSO-SVM模型的合理性和优越性。     

Total tardiness minimization on unrelated parallel machine scheduling with auxiliary equipment constraints

This research deals with scheduling jobs on unrelated parallel machines with auxiliary equipment constraints. Each job has a due date and requires a single operation. A setup for dies is incurred if there is a switch from processing one type of job to another type. For a die type, the number of dies is limited. Due to the attributes of the machines and the fitness of dies to each, the processing time for a job depends on the machine on which the job is processed, each job being restricted to processing on certain machines. In this paper, an effective heuristic based on threshold-accepting methods, tabu lists, and improvement procedures is proposed to minimize total tardiness. An extensive experiment is conducted to evaluate the computational characteristics of the proposed heuristic. Computational experiences demonstrate that the proposed heuristic is capable of obtaining optimal solutions for small-sized problems, and significantly outperforms an ATCS procedure and a simulated annealing method for problems in larger sizes.     

Efficiency in Repeated Games Revisited: The Role of Private Strategies

Most theoretical or applied research on repeated games with imperfect monitoring has focused on public strategies: strategies that depend solely on the history of publicly observable signals. This paper sheds light on the role of private strategies: strategies that depend not only on public signals, but also on players' own actions in the past. Our main finding is that players can sometimes make better use of information by using private strategies and that efficiency in repeated games can be improved. Our equilibrium private strategy for repeated prisoners' dilemma games consists of two states and has the property that each player's optimal strategy is independent of the other player's state.     

Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.