A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.     

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.     

Contract Theory-Based Incentive Mechanism for Full Duplex Cooperative NOMA with SWIPT Communication Networks

Cooperative Non-Orthogonal Multiple Access (NOMA) with Simultaneous Wireless Information and Power Transfer (SWIPT) communication can not only effectively improve the spectrum efficiency and energy efficiency of wireless networks but also extend their coverage. An important design issue is to incentivize a full duplex (FD) relaying center user to participate in the cooperative process and achieve a win–win situation for both the base station (BS) and the center user. Some private information of the center users are hidden from the BS in the network. A contract theory-based incentive mechanism under this asymmetric information scenario is applied to incentivize the center user to join the cooperative communication to maximize the BS’s benefit utility and to guarantee the center user’s expected payoff. In this work, we propose a matching theory-based Gale–Shapley algorithm to obtain the optimal strategy with low computation complexity in the multi-user pairing scenario. Simulation results indicate that the network performance of the proposed FD cooperative NOMA and SWIPT communication is much better than the conventional NOMA communication, and the benefit utility of the BS with the stable match strategy is nearly close to the multi-user pairing scenario with complete channel state information (CSI), while the center users get the satisfied expected payoffs.     

A Study of the Dynamics of Formation of Plasmoids in the Gatchina Discharge

Technical Physics - The results of studies of the Gatchina discharge conducted at the Harbin University of Technology (China) have been presented. The Gatchina discharge is mainly used to create an...     

电子束长程传输中离子通道的振荡特性研究

离子通道可以有效抑制电子束在等离子体环境内传输过程中的径向扩散,已有工作研究了离子通道对电子束的影响,但离子通道建立过程和暂态特性研究则更有助于理解和利用离子通道在电子束长程传输中的作用。本文利用PIC方法对离子通道的时空分布进行二维模拟,并基于单粒子理论推导出描述离子通道振荡的解析模型,对上述两种模型的结果相互校验。上述模型的计算结果表明,在长程传输过程中,相对论电子束在等离子体内部建立的离子通道是持续周期振荡的,电子束密度、电子束初始半径以及环境等离子体密度都会对离子通道的振荡规律产生影响,针对不同的等离子体环境选择合适的电子束参数可以有效提高离子通道的稳定性,进而提升传输过程中电子束的束流质量。     

连续调谐太赫兹回旋管非稳定振荡状态研究

太赫兹回旋管可实现高功率输出,并具有一定的频率调谐范围,是核磁共振波谱系统理想的高功率太赫兹辐射源。设计了263 GHz,TE_5,2基波连续调谐回旋管,通过磁场调节实现频率调谐范围为1.39 GHz,利用时域多模多频自洽非线性理论对设计的连续调谐回旋管非稳定振荡状态进行了研究。结果表明,在低次纵向谐波模式工作磁场范围内,当工作电流大于起振电流时,连续调谐回旋管先进入稳定状态,高次纵向谐波模式被抑制,工作模式TE_5,2的输出功率随时间不变;当电流增大,纵向谐波模式间的竞争引起回旋管由稳定状态进入到非稳定振荡状态,工作模式TE_5,2的输出功率随时间呈振荡变化且互作用效率大大降低;随着电流的进一步增大,回旋管又回到与低电流不同的稳定状态,互作用效率进一步降低。同时发现非稳定振荡状态的起始电流随着磁场增加而增大。本研究对需工作于稳定状态的面向DNP-NMR应用的连续调谐太赫兹回旋管的研制具有一定指导意义。     

减反膜对单晶硅太阳能电池性能的影响分析

在太阳能电池效率的评价中,电池材料、掺杂浓度、扩散长度等都是比较重要的参数,合理地改变相关参数可以优化太阳能电池的性能,提高电池效率。此外,在太阳能电池表面镀一层具有减反作用的光学薄膜（简称减反膜）也是提高电池效率的重要手段。以提高电池效率为目标,对单晶硅太阳能电池的掺杂浓度和扩散长度等微观参数进行计算优化,分析了掺杂浓度和扩散长度变化对电池效率的影响。并在此基础上分析了不同类型的减反膜对于电池效率的影响,给出了最佳减反膜材料及其膜系厚度,并且结合镀膜后电池量子效率的变化验证了其准确性。结果表明,在优化电池掺杂浓度和扩散长度的基础上,选择合适的减反膜,电池效率最高可达20.35%,相比于优化前提高了8.25%。     

中学物理到大学物理的过渡教学研究

大学物理是理工类学生的必修课,往往因为学习或教学过程忽略与中学物理结合等问题,成为大部分学生的难点课程.因此,文章对中学物理到大学物理的过渡教学进行研究,主要为学生对大学物理的学习提供帮助,为教师教学提供参考.     

大学物理教学改革实践浅析——“问题导向”式学习的意义及应用

针对当前我国高校大学物理教学中存在诸多问题的现状,提出"问题导向"式教学的解决途径."问题导向"学习中"教"与"学"的方式有较大跨度的转变,它以学生为中心,以问题为导向,其中问题的设置环节对师生良好互动的实现至关重要.文章就振动内容问题的设计和提出进行简单应用尝试,以期为后续大学物理教学改革的持续进行提供帮助.     

Wide-Angle Ultra-Broadband Metamaterial Absorber with Polarization-Insensitive Characteristics

An ultra-wideband metamaterial absorber is developed,which is polarized-insensitive and angular-stable.Three layers of square resistive films comprise the proposed metamaterial.The optimal values of geometric parameters are obtained,such that the designed absorber can achieve an ultra-broadband absorption response from 4.73 to 39.04 GHz(relative bandwidth of 156.7%)for both transverse electricity and transverse magnetic waves.Moreover,impedance matching theory and an equivalent circuit model are utilized for the absorption mechanism analysis.The compatibility of equivalent circuit calculation results,together with both full-wave simulation and experimental results,demonstrates the excellent performance and applicability of the proposed metamaterial absorber.