A UAV-Assisted V2X Network Architecture with Separated Data Transmission and Network Control

With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.

Behavior Measurement Model Based on Prediction and Control of Trusted Network

In order to construct the trusted network and realize the trust of network behavior,a new multi-dimensional behavior measurement model based on prediction and control is presented.By using behavior predictive equation,individual similarity function,group similarity function,direct trust assessment function,and generalized predictive control,this model can guarantee the trust of an end user and users in its network.Compared with traditional measurement model,the model considers different characteristics of various networks.The trusted measurement policies established according to different network environments have better adaptability.By constructing trusted group,the threats to trusted group will be reduced greatly.Utilizing trusted group to restrict individuals in network can ensure the fault tolerance of trustworthiness of trusted individuals and group.The simulation shows that this scheme can support behavior measurement more efficiently than traditional ones and the model resists viruses and Trojans more efficiently than older ones.

Dynamic Coordination of Uncalibrated Hand/Eye Robotic System Based on Neural Network

A nonlinear visual mapping model is presented to replace the image Jacobian relation for uncalibrated hand/eye coordination. A new visual tracking controller based on artificial neural network is designed. Simulation results show that this method can drive the static tracking error to zero quickly and keep good robustness and adaptability at the same time. In addition, the algorithm is very easy to be implemented with low computational complexity.

A new centrality measure based on neighbor loop structure for network dismantling

Nearly all real-world networks are complex networks and usually are in danger of collapse.Therefore,it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network functionalities.Network dismantling aims to find the smallest set of nodes such that after their removal the network is broken into connected components of sub-extensive size.To overcome the limitations and drawbacks of existing network dismantling methods,this paper focuses on network dismantling problem and proposes a neighbor-loop structure based centrality metric,NL,which achieves a balance between computational efficiency and evaluation accuracy.In addition,we design a novel method combining NL-based nodes-removing,greedy tree-breaking and reinsertion.Moreover,we compare five baseline methods with our algorithm on ten widely used real-world networks and three types of model networks including Erd€os-Renyi random networks,Watts-Strogatz smallworld networks and Barabasi-Albert scale-free networks with different network generation parameters.Experimental results demonstrate that our proposed method outperforms most peer methods by obtaining a minimal set of targeted attack nodes.Furthermore,the insights gained from this study may be of assistance to future practical research into real-world networks.

Joint Resource Allocation and Admission Control Mechanism in Software Defined Mobile Networks

This paper presented a joint resource allocation(RA) and admission control(AC) mechanism in software defined mobile networks(SDMNs). In this mechanism, the joint RA and AC problem can be formulated as an optimization problem with the aim of maximizing the number of admitted users while simultaneously minimizing the number of allocated channels. Since the primal problem is modeled to be a mixed integer nonlinear problem(MINLP), we attain the suboptimal solutions to the primal MINLP by convex relaxation. Additionally, with the global information collected by the SDMNs controller, a centralized joint RA and AC(CJRA)algorithm is proposed by the Lagrange dual decomposition technique to obtain the global optimum. Meanwhile, we propose an OpenFlow rules placement strategy to realize CJRA in an efficient way. Moreover, a distributed algorithm is also developed to find the local optimum, showing a performance benchmark for the centralized one. Finally, simulation results show that the proposed centralized algorithm admits more users compared with the distributed.

Facing a Problem of Electrical Energy Quality in Ship Networks-measurements, Estimation, Control

In this paper, electrical energy quality and its indices in ship electric networks are introduced, especially the meaning of electrical energy quality terms in voltage and active and reactive power distribution indices. Then methods of measurement of marine electrical energy indices are introduced in details and a microprocessor measurement-diagnosis system with the function of measurement and control is designed. Afterwards, estimation and control of electrical power quality of marine electrical power networks are introduced. And finally, according to the existing method of measurement and control of electrical power quality in ship power networks, the improvement of relative method is proposed.

Complex Network Formation and Analysis of Online Social Media Systems

To discover and identify the influential nodes in any complex network has been an important issue.It is a significant factor in order to control over the network.Through control on a network,any information can be spread and stopped in a short span of time.Both targets can be achieved,since network of information can be extended and as well destroyed.So,information spread and community formation have become one of the most crucial issues in the world of SNA(Social Network Analysis).In this work,the complex network of twitter social network has been formalized and results are analyzed.For this purpose,different network metrics have been utilized.Visualization of the network is provided in its original form and then filter out(different percentages)from the network to eliminate the less impacting nodes and edges for better analysis.This network is analyzed according to different centrality measures,like edge-betweenness,betweenness centrality,closeness centrality and eigenvector centrality.Influential nodes are detected and their impact is observed on the network.The communities are analyzed in terms of network coverage considering theMinimum Spanning Tree,shortest path distribution and network diameter.It is found that these are the very effective ways to find influential and central nodes from such big social networks like Facebook,Instagram,Twitter,LinkedIn,etc.

Study on the Elman Neural Network Operation Control Strategy of the Central Air Conditioning Chilled Water System

The stable operation of the central air conditioning water system always is a major difficulty for the control profession. Paper focus on the water system with multi variable, strong coupling, nonlinear, large time delay characteristics, presented use feed forward coupling compensation method, to eliminate the coupling effect between temperature and pressure. In this paper, the Elman neural network controller is designed for the first time, and the simulation results show that the response time of Elman neural network controller is shorter, the system is more stable and the overshoot is small.

HSCP:a hardware and software combined platform to improve precision in active network measurement

In this paper,an active network measurement platform is proposed which is a combination of hardware and software.Its innovation lies in the high performance of hardware combined with features that the software is easy to program,which retains software flexibility at the same time.By improving the precision of packet timestamp programmable hardware equipment,it provides packet sending control more accurately and supports the microsecond packet interval.We have implemented a model on the NetMagic platform,and done some experiments to analyze the accuracy difference of the user,the kernel and hardware timestamp.

An application-layer based centralized information access control for VPN

With the rapid development of Virtual Private Network (VPN), many companies and organizations use VPN to implement their private communication. Traditionally, VPN uses security protocols to protect the confidentiality of data, the message integrity and the endpoint authentication. One core technique of VPN is tunneling, by which clients can access the in- ternal servers traversing VPN. However, the tunneling technique also introduces a concealed security hole. It is possible that if one vicious user can establish tunneling by the VPN server, he can compromise the internal servers behind the VPN server. So this paper presents a novel Application-layer based Centralized Information Access Control (ACIAC) for VPN to solve this problem. To implement an efficient, flexible and multi-decision access control model, we present two key techniques to ACIAC—the centralized management mechanism and the stream-based access control. Firstly, we implement the information center and the constraints/events center for ACIAC. By the two centers, we can provide an abstract access control mechanism, and the material access control can be decided dynamically by the ACIAC’s constraint/event mechanism. Then we logically classify the VPN communication traffic into the access stream and the data stream so that we can tightly couple the features of VPN communication with the access control model. We also provide the design of our ACIAC prototype in this paper.

Matrix Measure with Application in Quantized Synchronization Analysis of Complex Networks with Delayed Time via the General Intermittent Control

This paper concerned with the quantized synchronization analysis problem. The scope of state vectors of dynamic systems, based on the matrix measure, is estimated. By using the general intermittent control, some simple yet generic criteria are derived ensuring the exponential stability of dynamic systems. Then, both the general intermittent networked controller and the quantized parameters can be designed, which guarantee that the nodes of the complex network are synchronized. Finally, simulation examples are given to illustrate the effectiveness and feasibility of the proposed method.

Factors That Affect the Centrality Controllability of Scale-Free Networks

The influence of a node in a network can be characterized by its macroscopic properties such as eigenvector centrality. An issue of significant theoretical and practical interest is to modify the influence or roles of the nodes in a network, and recent advances indicate that this can be achieved by just controlling a subset of nodes： the socalled controllers. However, the relationship between the structural properties of a network and its controllability, e.g., the control of node importance, is still not well understood. Here we systematically＂ explore this relationship by constructing scale-free networks with a fixed degree sequence and tunable network characteristics. We calculate the relative size （nc＊） of the minimai controlling set required to controi the importance of each individual node in a network. It is found that while clustering has no significant impact on nc＊, changes in degree-degree correlations, heterogeneity and the average degree of networks demonstrate a discernible impact on its controllability.

CAGCN:Centrality-Aware Graph Convolution Network for Anomaly Detection in Industrial Control Systems

In industrial control systems,the utilization of deep learning based methods achieves improvements for anomaly detection.However,most current methods ignore the association of inner components in industrial control systems.In industrial control systems,an anomaly component may affect the neighboring components;therefore,the connective relationship can help us to detect anomalies effectively.In this paper,we propose a centrality-aware graph convolution network(CAGCN)for anomaly detection in industrial control systems.Unlike the traditional graph convolution network(GCN)model,we utilize the concept of centrality to enhance the ability of graph convolution networks to deal with the inner relationship in industrial control systems.Our experiments show that compared with GCN,our CAGCN has a better ability to utilize this relationship between components in industrial control systems.The performances of the model are evaluated on the Secure Water Treatment(SWaT)dataset and the Water Distribution(WADI)dataset,the two most common industrial control systems datasets in the field of industrial anomaly detection.The experimental results show that our CAGCN achieves better results on precision,recall,and F1 score than the state-of-the-art methods.

Data-Based Predictive Control for Networked Nonlinear Systems with Packet Dropout and Measurement Noise

In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise and the number of consecutive packet dropouts in both channels are assumed to be random but bounded. A data-based networked predictive control method is proposed, in which a sequence of control increment predictions are calculated in the controller based on the measured output error, and based on the control increment predictions received by the actuator, a proper control action is obtained and applied to the plant according to the real-time number of consecutive packet dropouts at each sampling instant. Then the stability analysis is performed for the networked closedloop system. Finally, the effectiveness of the proposed method is illustrated by a numerical example.

智能配电控制系统的设计思路

鉴于电力行业日新月异的发展变化,传统的配电控制架构已难以适应现代电网日益增长的复杂形势。因此,智能配电控制系统的诞生尤为必要。文章深入探讨了智能配电控制系统的设计思路,在系统通信方案上,采用先进的EPON光纤通信技术,以确保数据传输的高效和稳定。同时,系统也支持其他多种通信方式接入,如GPRS通信、图像传输等,以适应不同场景的需求。此外,系统还兼容RS485、Canbus等现场总线标准,方便智能传感器的接入和扩展。对于需要更高带宽和稳定性的应用,系统还支持以太网接入技术,以满足更加复杂和多样化的网络通信需求。

高海拔低纬度灌区GNSS控制网投影变形问题研究

针对高海拔低纬度地区,GNSS控制网存在投影变形的问题,如何选取合理的投影面和投影带建立坐标系,从而控制投影变形小于2.5 cm/km是问题的关键。文章从理论上分析长度变形的影响规律,提出合理的解决方法,通过工程案例验证该方法在实际工程中应用的可行性。

基于GRU-BP算法的高精度动态物流称重系统

针对动态物流秤测量精度对载重、采样频率、带速较为敏感的问题,提出了一种高精度动态物流称重系统。首先,采用三因素五水平正交试验法,结合皮尔逊相关性检验原则,使用低通巴特沃斯与卡尔曼滤波器对传感器压力信号进行了滤波降噪处理,并将加速度信号作为模型输入信号,进行了特征补偿;然后,基于深度学习算法,提出了一种改进的门控循环单元模型,在该模型采样区间内将压力与振动改写为时序化信号,并将其共同输入门控循环单元(GRU)模型;最后,对GRU模型进行了改进,对其结构输出了层堆叠误差反向传播神经网络(BP),有效加强了模型的非线性映射能力。研究结果表明:在各类传动速度及测试货物下,该模型的最大测量误差相对于同类型深度学习模型长短期记忆(LSTM)神经网络、循环神经网络(RNN)时序模型及传统数值平均模型的误差,依次降低了16.14%、27.14%、76%,可用于各类称重系统。

麻家边水库库容测量与变化分析

以麻家边水库为例,根据中小型水库库容测量的工作内容,探讨无人机载激光雷达测量、无人船自动测量、等高线法和不规则三角网法等先进方法在水库库容测量中的应用,并通过实地调查和对比不同时间的卫星影像资料合理解释水库库容变化,可为类似项目提供有益参考。