M/S Controller Area Network(CAN) System Using Shared-Clock Scheduler

The Controller Area Network (CAN) is a well established control network for automotive and automation control applications. Time-Triggered Controller Area Network (TTCAN) is a recent development which introduces a session layer,for message scheduling,to the existing CAN standard,which is a two layer standard comprising of a physical layer and a data link layer. TTCAN facilitates network communication in a time-triggered fashion,by introducing a Time Division Multiple Access style communication scheme. This allows deterministic network behavior,where maximum message latency times can be quantified and guaranteed. In order to solve the problem of determinate time latency and synchronization among several districted units in one auto panel CAN systems,this paper proposed a prototype design implementation for a shared-clock scheduler based on PIC18F458 MCU. This leads to improved CAN system performance and avoid the latency jitters and guarantee a deterministic communication pattern on the bus. The real runtime performance is satisfied.

DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

Reliability Analysis of Controller Area Network Based Systems—A Review

This paper reviews the research work done on the Reliability Analysis of Controller Area Network (CAN) based systems. During the last couple of decades, real-time researchers have extended schedulability analysis to a mature technique which for nontrivial systems can be used to determine whether a set of tasks executing on a single CPU or in a distributed system will meet their deadlines or not [1-3]. The main focus of the real-time research community is on hard real-time systems, and the essence of analyzing such systems is to investigate if deadlines are met in a worst case scenario. Whether this worst case actually will occur during execution, or if it is likely to occur, is not normally considered. Reliability modeling, on the other hand, involves study of fault models, characterization of distribution functions of faults and development of methods and tools for composing these distributions and models in estimating an overall reliability figure for the system [4]. This paper presents the research work done on reliability analysis developed with a focus on Controller-Area-Network-based automotive systems.

Evolution and Governance of the Structure of Marine Economic Networks in China’s Coastal Areas: Based on Sea-related A-share Listed Companies

Data on discrete,isolated attributes of the marine economy are often used in traditional marine economic research.However,as the focus of urban research shifts from internal static attributes to external dynamic linkages,the importance of marine economic net-work research is beginning to emerge.The construction of the marine economic network in China’s coastal areas is necessary to change the flow of land and sea resources and optimize regional marine economic development.Employing data from headquarters and branches of sea-related A-share listed enterprises to construct the marine economic network in China,we use social network analysis(SNA)to discuss the characteristics of its evolution as of 2010,2015,and 2020 and its governance.The following results were obtained.1)In terms of topological characteristics,the scale of the marine economic network in China’s coastal areas has accelerated and expan-ded,and the connections have become increasingly close;thus,this development has complex network characteristics.2)In terms of spatial structure,the intensity of the connection fluctuates and does not form stable development support;the group structure gradually becomes clear,but the overall pattern is fragmented;there are spatial differences in marine economic agglomeration radiation;the radi-ation effect of the eastern marine economic circle is obvious;and the polarization effect of northern and southern marine economic circles is significant.On this basis,we construct a framework for the governance of a marine economic network with the market,the government,and industry as the three governing bodies.By clarifying the driving factors and building objectives of marine economic network construction,this study aims to foster the high-quality development of China’s marine economy.

New Rural Community Construction or Retention Development:A Comparative Analysis of Rural Settlement Transition Mechanism in Plain Agriculture Area of China Based on Actor Network Theory

It is an important way to realize rural revitalization and sustainable development to guide rural settlement transition(RST)in an appropriate way.This paper uses actor network theory(ANT)to construct a theoretical framework for the study of RST.Taking two typical villages with different transition paths in rural areas of North China Plain as examples,this paper reveals the mechanism of RST and makes a comparative analysis.The results show that:1)after identifying problems and obligatory passage point,key actors recruit heterogeneous actors into the actor network by entrusting them with common interests,and realize RST under the system operation.2)Rural settlements under different transition paths have similarities in the problems to be solved,collective actions and policy factors,but there are differences in the transition process,mechanism and effect.The actor network and mechanism of RST through the path of new rural community construction are more complex and the transition effect is more thorough.In contrast,the degree of RST of retention development path is limited if there is no resource and location advantage.3)Based on the applicable conditions of different paths,this paper designs a logical framework of‘Situation-Structure-Behavior-Result’to scientifically guide the identification of RST paths under the background of rural revitalization.

Priority Based Energy Efficient MAC Protocol by Varying Data Ratefor Wireless Body Area Network

Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.

Research on the Impact and Strategies of Rail Transit Network Integration in the Context of Metropolitan Area Development:A Case Study of the Wuhan Metropolitan Area

The metropolitan area is one of the key focal points in the construction and development of China’s new urbanization.Urban integration is an emerging trend in metropolitan areas.This paper explores the traffic demand characteristics and economic aspects of rail transit within metropolitan regions and argues that the construction of an integrated urban rail transit network is an effective approach to support their development.Rail transit in metropolitan areas offers both technical and economic advantages,improving the efficiency of time and space resource utilization,fostering economic cooperation,and ultimately contributing to an integrated development model.However,the integration of rail transit networks faces several challenges,including road network planning,technical standards,and operational organization.Using the Wuhan metropolitan area as a case study,this paper analyzes the challenges of rail transit network integration and proposes strategic solutions for development.

Modeling and Comprehensive Review of Signaling Storms in 3GPP-Based Mobile Broadband Networks:Causes,Solutions,and Countermeasures

Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.

基于MobileViT轻量化网络的车载CAN入侵检测方法

车载控制区域网络(controller area network,CAN)总线因缺少安全措施而易被攻击,因此入侵检测系统(intrusion detection system,IDS)在保护车载CAN总线免受网络攻击中发挥着重要作用.现有基于深度学习的车载CAN总线入侵检测方法存在资源开销大和延迟较高的问题.为减少检测延迟,提高检测率,提出一种利用改进的轻量化MobileViT模型对车载CAN总线进行入侵检测的方法.首先,将攻击流量可视化为彩色图,再使用GELU替换MobileViT的MV2模块中常规ReLU6,从而作为该模块的激活函数,可有效解决神经元死亡问题,提升模型收敛速度.使用指数衰减自动更新学习率,并通过迁移学习加速训练过程实现对彩色图分类,从而达到对入侵行为的检测.基于CAR-HACKING DATASET数据集的实验表明,改进后的MobileViT在消耗较少算力的情况下对入侵行为的检测准确率为100%,模型参数仅为2.12 MB,平均响应时间仅为1.6 ms,节省了训练资源,并保证了检测的准确率.

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.

A Human Body Posture Recognition Algorithm Based on BP Neural Network for Wireless Body Area Networks

Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been proposed.However, the recognition rate is relatively low. In this paper, we apply back propagation(BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude(SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.

Stand basal area modelling for Chinese fir plantations using an artificial neural network model

Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.

Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

On Reliability-optimized Controller Placement for Software-Defined Networks

By decoupling control plane and data plane,Software-Defined Networking(SDN) approach simplifies network management and speeds up network innovations.These benefits have led not only to prototypes,but also real SDN deployments.For wide-area SDN deployments,multiple controllers are often required,and the placement of these controllers becomes a particularly important task in the SDN context.This paper studies the problem of placing controllers in SDNs,so as to maximize the reliability of SDN control networks.We present a novel metric,called expected percentage of control path loss,to characterize the reliability of SDN control networks.We formulate the reliability-aware control placement problem,prove its NP-hardness,and examine several placement algorithms that can solve this problem.Through extensive simulations using real topologies,we show how the number of controllers and their placement influence the reliability of SDN control networks.Besides,we also found that,through strategic controller placement,the reliability of SDN control networks can be significantly improved without introducing unacceptable switch-to-controller latencies.

Neural Network Based Robust Controller for Trajectory Tracking of Underwater Vehicles

A robust neural network controller （NNC） is presented for tracking control of underwater vehicles with uncertainties. The controller is obtained by using backstepping technique and Lyapunov function design in combination with neural network identification. Modeling errors and environmental disturbances are considered in the mathematical model. A twolayer neural network is introduced to compensate the modeling errors, while H∞ control strategy is used to achieve the L2-gain performance. The uniformly ultimately bounded （UUB） stabilities of tracking errors and NN weights are guaran- teed through the proposed controller. An on-line NN weights tuning algorithm is also propesed. Good performances of the tracking control system are illustrated bv the results of numerical simulations.

Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering

The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in SDN.Every controller supports a set of features.However,the support of the features may be more prominent in one controller.Moreover,a single controller leads to performance,single-point-of-failure(SPOF),and scalability problems.To overcome this,a controller with an optimum feature set must be available for SDN.Furthermore,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN.Herein,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E topology.The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS.Moreover,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.

Exponential stabilization of networked control systems and design of switching controller

This paper deals with the problem of switching between an open-loop estimator and a close-loop estimator for compensating transmission error and packet dropout of networked control systems. Switching impulse is considered in order to reduce the error between theory and application, a sufficient condition for exponential stabilization of networked control systems under a given switching rule is presented by multiple Lyapunov-like functions. These results are presented for both continuous-time and discrete-time domains. Controllers are designed by means of linear matrix inequalities. Sim- ulation results show the feasibility and efficiency of the proposed method.

Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model

Sanduao is an important sea-breeding bay in Fujian,South China and holds a high economic status in aquaculture.Quickly and accurately obtaining information including the distribution area,quantity,and aquaculture area is important for breeding area planning,production value estimation,ecological survey,and storm surge prevention.However,as the aquaculture area expands,the seawater background becomes increasingly complex and spectral characteristics differ dramatically,making it difficult to determine the aquaculture area.In this study,we used a high-resolution remote-sensing satellite GF-2 image to introduce a deep-learning Richer Convolutional Features(RCF)network model to extract the aquaculture area.Then we used the density of aquaculture as an assessment index to assess the vulnerability of aquaculture areas in Sanduao.The results demonstrate that this method does not require land and water separation of the area in advance,and good extraction can be achieved in the areas with more sediment and waves,with an extraction accuracy>93%,which is suitable for large-scale aquaculture area extraction.Vulnerability assessment results indicate that the density of aquaculture in the eastern part of Sanduao is considerably high,reaching a higher vulnerability level than other parts.

Fuzzy controller design for networked control system with time-variant delays

Networked control system is new hot-point in control engineering. A new delayed model for networked control systems is presented, based on which an LQR controller is designed. A method of delays estimation online is also given. For the difficulty on implementation of LQR in NCSs with time-variant delays, the Mamdani intelligent logic with LQR controller is addressed. The stability of the networked control system is also given. Simulation results prove that the novel controller can make the system stable and robustly preserve the performance in terms of time-variant delays.

An Experimental Study of Network-based Language Learning in Less-developed Areas

The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effectively with the help of modern technology especially in less-developed areas.This thesis focuses on network-based experimental study.The research shows that the students under NBLL environment have cultivated the capabilities in information collection,computer operation,and information evaluation,as well as the abilities in problem solving,reasoning with criticism,and cooperating with others.