分数阶时滞非线性多智能体系统的自适应控制

针对分数阶多智能体系统中存在时滞和非线性特性, 时滞往往会引起控制系统的性能下降甚至出现系统 不稳定等问题, 提出了一种含时滞非线性的分数阶多智能体系统自适应控制方法. 对于多智能体系统的控制协议, 设计了基于领导者和相邻智能体状态信息的自适应控制协议, 减小了过大常数控制增益带来的能源浪费. 对于一 致性, 利用图论基础、分数阶Halanay不等式稳定性定理、Kronecker积和Schur补引理, 获得了分数阶时滞非线性多 智能体系统的LMI一致性条件. 仿真结果验证了本文算法的正确性和有效性. 由于整数阶系统是分数阶系统的特殊 形式, 本文结论可以直接推广到整数阶多智能体系统中.     

SVPWM教学研究之重点解析

SVPWM是电力电子技术教学中的重要内容,本文对空间矢量调制的教学重点做了一个较为全面系统的梳理。重点阐述SVPWM的核心思想,特别是三相正弦交流电SVPWM的基本原理,严格证明了三相电压的空间矢量相等与线电压相等等价。同时,证明在SVPWM下,逆变器输出的最大圆为基本空间矢量构成的正六边形的内接圆、直流母线电压利用率提高了约15.4%。     

新型星载超宽带微波光子变频技术

传统的星载变频通道存在易受电磁干扰、动态范围受限、带宽瓶颈等不足,将微波光子技术引入卫星通信系统中,可克服电域信号变频的局限性,提升星载变频通道的性能。文章提出了一种基于并联马赫-增德尔调制器(MZM)与相干接收的宽带微波光子变频技术,通过采用相干接收方法,能够实现较好的共模噪声抑制,并降低谐波失真的影响,同时结合抑制载波调制方式所带来的优势,有效改善了变频无杂散动态范围等指标。通过实验验证,其杂散抑制比优于60 dB,无杂散动态范围优于100 dB·Hz2/3,为新型星载微波光子变频方案设计、实际应用提供了必要的理论依据与技术支撑。     

数据要素市场化的理论内涵、现实挑战和实践路径

数据要素是数字经济时代的全新生产要素,数据要素市场化在数字经济推进经济高质量发展中具有显著的战略意义.目前,全球对于数据要素的认识仍然处在积极探索中,正确把握数据要素市场化的新内涵、新特征、新要求、新挑战至关重要.在对数据要素市场化的理论内涵和重大意义进行系统阐述的基础上,通过数据要素市场化指数测度发现我国当前数据要素市场化起步晚、起步缓,面临数据产权确权难、数据价值评估难、数据交易流通难、数据保护监管难等挑战,从而从加快数据汇聚、推动数据流通、深化数据应用、强化数据安全等方面提出具有实践价值的解决路径.     

Roll angle of autocollimator measurement method based on hollow cube corner reflector

In this paper, a method of measuring roll angle with hollow cube corner reflector (HCCR) is proposed. Firstly, the space coordinate vector relationship between the mirror and the autocollimator is established by using the Euler rotation relationship, and the structure of the HCCR is designed. Secondly, through the actual measurement of the HCCR reflector''s angle sensitivity, the specific formula of roll angle measurement is obtained. The experimental results show that the method can be used to measure the roll angle, and the maximum root mean square (RMS) error is 15" in the range of 350", and the repeatability of the experiment is better than 2.7". On the basis of retaining the traditional autocollimator, the plane mirror is replaced with HCCR, which realizes the measurement of the roll angle of the autocollimator. This method does not change the internal structure of the autocollimator system, and there is no process error caused by special processing technology, so as to retain the autocollimator''s own long measurement distance, high measurement accuracy, high system stability, and large range of measurement performance, and it can also be used in industrial production.     

Kernel-based adversarial attacks and defenses on support vector classification

While malicious samples are widely found in many application fields of machine learning, suitable countermeasures have been investigated in the field of adversarial machine learning. Due to the importance and popularity of Support Vector Machines (SVMs), we first describe the evasion attack against SVM classification and then propose a defense strategy in this paper. The evasion attack utilizes the classification surface of SVM to iteratively find the minimal perturbations that mislead the nonlinear classifier. Specially, we propose what is called a vulnerability function to measure the vulnerability of the SVM classifiers. Utilizing this vulnerability function, we put forward an effective defense strategy based on the kernel optimization of SVMs with Gaussian kernel against the evasion attack. Our defense method is verified to be very effective on the benchmark datasets, and the SVM classifier becomes more robust after using our kernel optimization scheme.     

Editorial: Machine Learning and Intelligent Communications (MLICOM 2018)

Mobile Networks and Applications -     

Physical Violence Detection Based on Distributed Surveillance Cameras

Mobile Networks and Applications - In recent years, physical violence detection has become a research hotspot in the area of human activity recognition. With the improvement and full coverage of...