The generalized Dice similarity measures for Pythagorean fuzzy multiple attribute group decision making

The Pythagorean fuzzy set (PFS) is characterized by two functions expressing the degree of membership and the degree of nonmembership, which square sum of them is equal or less than 1. It was proposed as a generalization of a fuzzy set to deal with indeterminate and inconsistent information. In this study, we shall present some novel Dice similarity measures of PFSs and the generalized Dice similarity measures of PFSs and indicates that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures-based multiple attribute group decision-making models with Pythagorean fuzzy information. Then, we apply the generalized Dice similarity measures between PFSs to multiple attribute group decision making. Finally, an illustrative example is given to demonstrate the efficiency of the similarity measures for selecting the desirable ERP system.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

无失效数据机载多余度EWIS可靠性研究

针对高可靠度机载多余度EWIS各组成部分寿命服从指数分布但参数未知的情况，提出采用无失效数据可靠度分析方法评估EWIS的可靠度水平。通过Monte-Carlo仿真方法对连接形式为“先并联、后串联”EWIS各组成部分寿命进行抽样，利用“最小最大值”方法获得系统寿命的抽样值，用概率纸检验法初步判断EWIS寿命是否服从威布尔分布，再用Pearson拟合优度检验法判断EWIS寿命是否服从威布尔分布。结合无故障飞行时间的样本值与EWIS寿命服从威布尔分布的假设，采用无失效数据分析方法评估EWIS的可靠度水平。研究方法对机载多余度EWIS无失效数据可靠度分析有一定的贡献。     

Compound fault-tolerant attitude control for hypersonic vehicle with reaction control systems in reentry phase

In this paper, a novel compound fault-tolerant attitude control (FTC) scheme is proposed for reentry hypersonic vehicles with aerodynamic surfaces and reaction control systems (RCS) in the presence of parameter uncertainties, external disturbances and aerodynamic surfaces faults. Aerodynamic surfaces work as the primary actuators and RCS serve as auxiliary actuators. When aerodynamic surfaces cannot provide the required attitude control torque due to low dynamic pressure or faults, RCS are activated to assist aerodynamic surfaces to generate the residual torque. A nonlinear disturbance observer-based sliding mode controller is designed to calculate the required attitude control torque which can handle the parametric uncertainties and external disturbances together. The quadratic programming method is applied to obtain the optimal aerodynamic surfaces deflections from the required control torque. An innovative fuzzy rule-based decision-making system is design to solve the RCS control allocation problem, which is conceptually easy to understand and computationally efficiently compared with existing approaches. Based on quantized control theory, the closed-loop control system stability is rigorously analyzed. Simulation results are given to demonstrate the effectiveness and efficiency of developed FTC scheme.     

A platform for P2P agent-based collaborative applications

The operational environment can be a valuable source of information about the behavior of software applications and their usage context. Although a single instance of an application has limited evidence of the range of the possible behaviors and situations that might be experienced in the field, the collective knowledge composed by the evidence gathered by the many instances of a same application running in several diverse user environments (eg, a browser) might be an invaluable source of information. This information can be exploited by applications able to autonomously analyze how they behave in the field and adjust their behavior accordingly. Augmenting applications with the capability to collaborate and directly share information about their behavior is challenging because it requires the definition of a fully decentralized and dependable networked infrastructure whose nodes are the user machines. The nodes of the infrastructure must be collaborative, to share information, and autonomous, to exploit the available information to change their behavior, for instance, to better accommodate the needs of the users to prevent known problems. This paper describes the initial results that we obtained with the design and the development of an infrastructure that can enable the execution of collaborative scenarios in a fully decentralized way. Our idea is to combine the agent-based paradigm, which is well suited to design collaborative and autonomous nodes, and the peer-to-peer paradigm, which is well suited to design distributed and dynamic network infrastructures. To demonstrate our idea, we augmented the popular JADE agent-based platform with a software layer that supports both the creation of a fully decentralized peer-to-peer network of JADE platforms and the execution of services within that network, thus enabling JADE multiagent systems (MASs) to behave as peer-to-peer networks. The resulting platform can be used to study the design of collaborative applications running in the field.     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

Components and Development in Big Data System: A Survey

With the growth of distributed computing systems, the modern Big Data analysis platform products often have diversified characteristics. It is hard for users to make decisions when they are in early contact with Big Data platforms. In this paper, we discussed the design principles and research directions of modern Big Data platforms by presenting research in modern Big Data products. We provided a detailed review and comparison of several state-of-the-art frameworks and concluded into a typical structure with five horizontal and one vertical. According to this structure, this paper presents the components and modern optimization technologies developed for Big Data, which helps to choose the most suitable components and architecture from various Big Data technologies based on requirements.     

二次侧非能动余热排出系统流动不稳定性研究

二次侧非能动余热排出（ASP）系统是压水堆核电厂应对全厂断电事故的重要措施之一。为研究ASP系统的运行特性，设计建造了ASP系统试验装置（ASPTF）。在ASPTF上开展了ASP系统运行稳定性影响试验，并对试验结果进行了理论分析。试验研究与理论分析结果表明：低压低功率下ASP系统中出现流动不稳定性；增加蒸汽管线或回水管线阻力系数可抑制ASP系统出现流动不稳定性；降低加热功率时ASP系统更易出现流动不稳定性；主泵运行状态影响ASP系统的输入功率进而影响系统的流动不稳定性。