Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

基于Cisco Packet Tracer的虚拟仿真软件在“计算机网络技术”实验教学中的应用研究

作者介绍了Cisco Packet Tracer虚拟仿真软件的功能和优点,并根据当前“计算机网络技术”课程的实验教学现状,以“交换机虚拟局域网”实验为例说明Cisco Packet Tracer如何在“计算机网络技术”课程中实现虚拟仿真实验教学。通过问卷调查可知,虚拟仿真实验能够改善实验教学效果,降低实验成本,培养学生的实践操作能力和分析解决问题能力。

基于Packet Tracer的MQTT协议仿真实现

MQTT协议是物联网设备云边传输最常用的通信协议之一。对于物联网相关专业、学科的学生来说,理解MQTT协议的工作原理、数据包结构、消息服务质量等非常重要。通过在Packet Tracer仿真软件上进行MQTT协议网络仿真结构设计、网络连通性配置、程序设计以及相应数据包抓取,仿真MQTT协议的工作过程,分析协议数据包的结构。经过仿真分析与理论分析的对比,进一步加深对MQTT协议的理解。

基于Packet Tracer的网络仿真应用研究

计算机网络体系结构复杂、网络设备价格昂贵等因素造成网络的规划与设计在真实场景下构建与验证是不可行的。基于Pa cket Tracer的网络仿真在网络规划需求多样化的今天变成一种可行的技术手段。文章将通过某校园网的网络规划与仿真案例,展示基于Packet Tracer的网络仿真软件的应用。

Distributed dynamic event-based finite-time dissipative synchronization control for semi-Markov switched fuzzy cyber-physical systems against random packet losses

This paper is concerned with the finite-time dissipative synchronization control problem of semi-Markov switched cyber-physical systems in the presence of packet losses, which is constructed by the Takagi–Sugeno fuzzy model. To save the network communication burden, a distributed dynamic event-triggered mechanism is developed to restrain the information update. Besides, random packet dropouts following the Bernoulli distribution are assumed to occur in sensor to controller channels, where the triggered control input is analyzed via an equivalent method containing a new stochastic variable. By establishing the mode-dependent Lyapunov–Krasovskii functional with augmented terms, the finite-time boundness of the error system limited to strict dissipativity is studied. As a result of the help of an extended reciprocally convex matrix inequality technique, less conservative criteria in terms of linear matrix inequalities are deduced to calculate the desired control gains. Finally, two examples in regard to practical systems are provided to display the effectiveness of the proposed theory.

Network traffic identification in packet sampling environment

With the rapid growth of network bandwidth,traffic identification is currently an important challenge for network management and security.In recent years,packet sampling has been widely used in most network management systems.In this paper,in order to improve the accuracy of network traffic identification,sampled NetFlow data is applied to traffic identification,and the impact of packet sampling on the accuracy of the identification method is studied.This study includes feature selection,a metric correlation analysis for the application behavior,and a traffic identification algorithm.Theoretical analysis and experimental results show that the significance of behavior characteristics becomes lower in the packet sampling environment.Meanwhile,in this paper,the correlation analysis results in different trends according to different features.However,as long as the flow number meets the statistical requirement,the feature selection and the correlation degree will be independent of the sampling ratio.While in a high sampling ratio,where the effective information would be less,the identification accuracy is much lower than the unsampled packets.Finally,in order to improve the accuracy of the identification,we propose a Deep Belief Networks Application Identification(DBNAI)method,which can achieve better classification performance than other state-of-the-art methods.

Packet Scheduling in Rechargeable Wireless Sensor Networks under SINR Model

Two packet scheduling algorithms for rechargeable sensor networks are proposed based on the signal to interference plus noise ratio model.They allocate different transmission slots to conflicting packets and overcome the challenges caused by the fact that the channel state changes quickly and is uncontrollable.The first algorithm proposes a prioritybased framework for packet scheduling in rechargeable sensor networks.Every packet is assigned a priority related to the transmission delay and the remaining energy of rechargeable batteries,and the packets with higher priority are scheduled first.The second algorithm mainly focuses on the energy efficiency of batteries.The priorities are related to the transmission distance of packets,and the packets with short transmission distance are scheduled first.The sensors are equipped with low-capacity rechargeable batteries,and the harvest-store-use model is used.We consider imperfect batteries.That is,the battery capacity is limited,and battery energy leaks over time.The energy harvesting rate,energy retention rate and transmission power are known.Extensive simulation results indicate that the battery capacity has little effect on the packet scheduling delay.Therefore,the algorithms proposed in this paper are very suitable for wireless sensor networks with low-capacity batteries.

Solar Power Plant Network Packet-Based Anomaly Detection System for Cybersecurity

As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,with solar power accounting for the most significant proportion of renewables.As the scale and importance of solar energy have increased,cyber threats against solar power plants have also increased.So,we need an anomaly detection system that effectively detects cyber threats to solar power plants.However,as mentioned earlier,the existing solar power plant anomaly detection system monitors only operating information such as power generation,making it difficult to detect cyberattacks.To address this issue,in this paper,we propose a network packet-based anomaly detection system for the Programmable Logic Controller(PLC)of the inverter,an essential system of photovoltaic plants,to detect cyber threats.Cyberattacks and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power plants.The analysis shows that Denial of Service(DoS)and Manin-the-Middle(MitM)attacks are primarily carried out on inverters,aiming to disrupt solar plant operations.To develop an anomaly detection system,we performed preprocessing,such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such data.The Random Forest model showed the best performance with an accuracy of 97.36%.The proposed system can detect anomalies based on network packets,identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants,and enhance the security of solar plants.

Robust fault detection for delta operator switched fuzzy systems with bilateral packet losses

Considering packet losses, time-varying delay, and parameter uncertainty in the switched fuzzy system, this paper designs a robust fault detection filter at any switching rate and analyzes the H∞ performance of the system. Firstly, the Takagi-Sugeno(T-S) fuzzy model is used to establish a global fuzzy model for the uncertain nonlinear time-delay switched system,and the packet loss process is modeled as a mathematical model satisfying Bernoulli distribution. Secondly, through the average dwell time method and multiple Lyapunov functions, the exponentially stable condition of the nonlinear network switched system is given. Finally, specific parameters of the robust fault detection filter can be obtained by solving linear matrix inequalities(LMIs). The effectiveness of the method is verified by simulation results.

A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between vehicles.Therefore,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful attacks.Previous machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent shortcomings.Hence,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and AutoEncoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better input.Then the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper level.Finally,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or not.The experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.

Uniquely Decomposable Constellation Group-Based Sparse Vector Coding for Short Packet Communications

Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.

A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.

Quantum Mechanics: Harmonic Wave-Packets, Localized by Resonant Response in Dispersion Dynamics

From a combination of Maxwell’s electromagnetism with Planck’s law and the de Broglie hypothesis, we arrive at quantized photonic wave groups whose constant phase velocity is equal to the speed of light c = ω/k and to their group velocity dω/dk. When we include special relativity expressed in simplest units, we find that, for particulate matter, the square of rest mass , i.e., angular frequency squared minus wave vector squared. This equation separates into a conservative part and a uniform responsive part. A wave function is derived in manifold rank 4, and from it are derived uncertainties and internal motion. The function solves four anomalies in quantum physics: the point particle with prescribed uncertainties;spooky action at a distance;time dependence that is consistent with the uncertainties;and resonant reduction of the wave packet by localization during measurement. A comparison between contradictory mathematical and physical theories leads to similar empirical conclusions because probability amplitudes express hidden variables. The comparison supplies orthodox postulates that are compared to physical principles that formalize the difference. The method is verified by dual harmonics found in quantized quasi-Bloch waves, where the quantum is physical;not axiomatic.

基于Packet Tracer的RIP路由实验教学设计研究

RIP路由协议是计算机网络课程动态路由部分的重点内容,传统RIP实验多注重基本配置与连通性测试,缺少深入RIP原理的教学设计。文章提出了实验教学设计改进方案,扩大拓扑规模,引入路由汇总问题和路由调试环节,让实验过程呈现更多的中间问题,并通过对问题的思考与剖析,引导学生逐步深入实验机理,使学生不仅能够独立完成RIP配置,而且有能力通过观察和分析网络行为,发现并解决与RIP协议相关的网络问题。改进的实验教学设计逻辑主线清晰,理论与实践结合紧密,有助于学生在工程实践中灵活运用相关理论正确部署RIP路由。

浅谈Packet Tracer仿真软件在中职计算机网络实训教学中的应用探析

Packet Tracer的应用降低了办学成本,提高了教学效率。Packet Tracer具有支持大量仿真设备、多种协议、数据报传输的可视化等特点,可直观辅助计算机网络理论理解,让计算机网络实训不再受限于设备的型号和数量,从而实现课堂实训人人化、考核与复习灵活化。

高量测丢包率下基于共享模糊等价关系的配电网状态感知

配电网分布式新能源渗透率的不断增加,对实时监测的要求不断提高,大量量测数据在集中传输过程中常常出现延迟、丢包等现象导致配电网状态感知精度降低甚至不可观测。针对上述问题,提出基于共享模糊等价关系的状态感知方法。首先,将采集得到的多时间断面历史量测数据集作为源数据集,实时采集得到的高量测丢包率下的数据作为目标数据集。利用三角形隶属度函数分别将两组数据集写成模糊集的形式。然后采用一种向量间距离度量方法,分别计算得到两组模糊集的模糊等价关系矩阵。在此基础上,采用共享模糊等价关系的聚类方法,将知识从源数据集转移到目标数据集,减小两组数据集特征分布之间的差异。最后,利用深度神经网络对知识迁移后低维特征空间中高量测丢包率对应的数据进行标记,得到配电网实时状态。通过对IEEE标准算例和某实际地市公司配电网算例进行仿真测试,结果表明所提出的基于共享模糊等价关系的配电网状态估计方法能在高量测丢包率下获得准确的配电网实时运行状态,实现不同量测数据缺失程度下的配电网实时监测。

含丢包和量化的网络控制系统随机鲁棒稳定

针对一类含有随机丢包和考虑信号量化情况下的网络控制系统,通过量化状态反馈控制方法,研究了系统的随机鲁棒稳定性问题。采用满足伯努利分布的随机变量对丢包过程进行建模;同时,采用对数量化器对传感器-控制器通道的信号进行量化,利用扇形界的方法将产生的量化误差描述为扇形界的不确定性。设计状态反馈控制器,结合所设计的量化器和随机丢包模型,利用Lyapunov函数和线性矩阵不等式方法得到系统稳定和鲁棒稳定的充分条件。最后,利用数值仿真实例验证了所提方法的有效性。

基于经验模态分解和小波包能量熵的杉木加载过程中细观损伤监测与识别

【目的】细观损伤是承载木材断裂的主要原因之一。木材的多孔层状结构使其损伤过程变得复杂,针对单一信号处理方法较难充分挖掘木材断裂声发射信号中的细观损伤信息,造成识别信息不充分、不完备的问题。本研究提出通过经验模态分解(EMD)和小波包能量熵结合的信号处理方法,通过声发射无损检测手段,识别杉木加载过程中的细观损伤类型。【方法】以杉木为研究对象,进行单轴压缩、双悬臂梁和顺纹拉伸3种单一损伤试验,并对其进行加载过程中声发射信号的采集、监测与分析。通过小波包阈值法消除损伤试验中采集的声发射信号噪声,经由EMD和相关系数计算,分离出最能体现杉木细观损伤特征的本征模态(IMF)分量,并对IMF分量进行基于傅里叶变换的峰值频率分析和小波包能量熵分析,提取杉木细观损伤的特征。【结果】(1)EMD和小波包能量熵结合的信号处理方法能够判断杉木加载过程中声发射信号对应的细观损伤类型与构成。(2)杉木不同细观损伤类型的声发射信号对应不同的小波包能量熵区间:胞壁屈曲与塌溃(0.69~0.99)、层间开裂(1.57~1.78)、纤维束断裂(1.92~2.27)。(3)宏观断口观察和电镜显微分析验证了该方法的准确性。【结论】经验模态分解–小波包能量熵法避免了声发射信号模态堆叠的影响,并解决了木材细观损伤复杂且难以识别的问题,为杉木木材断裂的早期诊断方法提供了理论支撑。

基于增强多头注意力机制的Optuna-BiGRU测井岩性识别

测井岩性识别是油气勘探开发中至关重要的内容。针对现有算法模型在处理测井曲线数据时,无法有效捕获曲线内部深层关联和深度方向关系、拟合能力较弱、难以准确提取关键特征、噪声干扰以及模型超参数调优过程复杂困难等问题,提出了一种通过Optuna超参数优化双向门循环单元(Optuna-BiGRU)结合增强多头注意力机制(EMHA)的测井岩性识别模型——Optuna-BiGRU-EMHA模型。该模型引入残差机制和层归一化以改进多头注意力机制模块,并结合双向门循环单元(BiGRU)解决了处理测井数据时的问题,同时使用Optuna超参数优化框架和小波包自适应阈值方法分别解决了超参数调优和噪声干扰问题。首先通过交会图分析和敏感性箱线图分析选取自然伽马、深感应电阻率、中子-密度孔隙度、平均中子-密度孔隙度和岩性密度5个特征参数的测井数据,通过小波包自适应阈值方法对数据进行去噪,并将测井数据分割成数据块,然后利用Optuna框架优化BiGRU-EMHA模型超参数,最后通过实验对比K-近邻算法(KNN)、随机森林(RF)、极端梯度提升算法(XGBoost)、长短期记忆(LSTM)神经网络、BiGRU、双向长短期记忆(BiLSTM)神经网络、BiGRU-MHA、Optuna-BiGRU-EMHA等8种模型在测井岩性识别中的精度。结果表明:Optuna-BiGRU-EMHA模型识别准确率达到80%,相对于传统机器学习模型和深度学习模型,综合岩性识别准确率分别提高15.94%~23.14%和3.93%~15.94%,该模型为常规测井岩性识别提供了坚实的理论支持。