Digital hysteresis sliding mode control for interleaved DC–DC converters

This paper presents a digital constant frequency sliding mode control (SMC) law for interleaved DC–DC converters. Constant switching frequency and interleaving are achieved by dynamically adjusting the hysteresis of the control signals generating comparators. The interleaving method neither imposes constraints on the number of required phases to obtain a specific output voltage nor uses quasi-SMC. Hence, the control scheme achieves high flexibility, robustness, and performance. Furthermore, a sliding mode observer (SMO) for reconstructing the inductor currents is proposed. Experimental results for a two-phase buck converter are reported. The control concept accomplishes an improved dynamic performance in comparison with quasi-SMC.     

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。     

冠状动脉系统的微分积分终端滑模混沌抑制

冠状动脉系统的混沌现象会导致严重的健康问题。以非线性冠状动脉系统为研究对象，建立了不确定性冠状动脉系统动力学模型，提出了不确定性冠状动脉系统的微分积分终端滑模混沌抑制方法，针对该模型的不确定性设计了扰动观测器，根据Lyapunov理论证明了所设计控制系统的稳定性，通过仿真实验验证了所提出的混沌抑制方法的有效性和可行性。     

TLCD基础隔震框架结构的减振控制研究

为了避免和减轻由过大隔震层位移引起的损害,对基础隔震框架结构装设调频液柱阻尼器(tuned liquid column damper,简称TLCD)后混合系统的减振效果进行研究。建立了单层和多层混合控制系统在地震作用下的运动方程,采用TLCD-结构体系转化为调频质量阻尼器(tuned mass damper,简称TMD)-结构体系的等效方法,利用TMD参数优化公式,得到单个TLCD初始设计参数,并采用状态空间方程得到多个TLCD最优设计参数。通过对某8层基础隔震结构进行模拟,证明了该理论设计方法的合理性。该混合结构不仅可以减小隔震层位移和加速度,而且对上部结构位移和加速度反应都能更有效的控制。     

Experimental investigation of far-field human cough airflows from healthy and influenza-infected subjects

Seasonal influenza epidemics have been responsible for causing increased economic expenditures and many deaths worldwide. Evidence exists to support the claim that the virus can be spread through the air, but the relative significance of airborne transmission has not been well defined. Particle image velocimetry (PIV) and hot-wire anemometry (HWA) measurements were conducted at 1 m away from the mouth of human subjects to develop a model for cough flow behavior at greater distances from the mouth than were studied previously. Biological aerosol sampling was conducted to assess the risk of exposure to airborne viruses. Throughout the investigation, 77 experiments were conducted from 58 different subjects. From these subjects, 21 presented with influenza-like illness. Of these, 12 subjects had laboratory-confirmed respiratory infections. A model was developed for the cough centerline velocity magnitude time history. The experimental results were also used to validate computational fluid dynamics (CFD) models. The peak velocity observed at the cough jet center, averaged across all trials, was 1.2 m/s, and an average jet spread angle of θ = 24° was measured, similar to that of a steady free jet. No differences were observed in the velocity or turbulence characteristics between coughs from sick, convalescent, or healthy participants.