Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Approximately Bi-Similar Symbolic Model for Discretetime Interconnected Switched System

Dear Editor,This letter concerns the development of approximately bi-similar symbolic models for a discrete-time interconnected switched system(DT-ISS).The DT-ISS under consideration is formed by connecting multiple switched systems known as component switched systems(CSSs).Although the problem of constructing approximately bi-similar symbolic models for DT-ISS has been addressed in some literature,the previous works have relied on the assumption that all the subsystems of CSSs are incrementally input-state stable.

FADSF:A Data Sharing Model for Intelligent Connected Vehicles Based on Blockchain Technology

With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is the key to organically maximizing their efficiency.However,in the context of increasingly strict global data security supervision and compliance,numerous problems,including complex types of connected vehicle data,poor data collaboration between the IT(information technology)domain and OT(operation technology)domain,different data format standards,lack of shared trust sources,difficulty in ensuring the quality of shared data,lack of data control rights,as well as difficulty in defining data ownership,make vehicle data sharing face a lot of problems,and data islands are widespread.This study proposes FADSF(Fuzzy Anonymous Data Share Frame),an automobile data sharing scheme based on blockchain.The data holder publishes the shared data information and forms the corresponding label storage on the blockchain.The data demander browses the data directory information to select and purchase data assets and verify them.The data demander selects and purchases data assets and verifies them by browsing the data directory information.Meanwhile,this paper designs a data structure Data Discrimination Bloom Filter(DDBF),making complaints about illegal data.When the number of data complaints reaches the threshold,the audit traceability contract is triggered to punish the illegal data publisher,aiming to improve the data quality and maintain a good data sharing ecology.In this paper,based on Ethereum,the above scheme is tested to demonstrate its feasibility,efficiency and security.

Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles

With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points(NCPs).Considering the amount of task data and the idle resources of NCPs,a computing resource scheduling model for NCPs is established.Taking the heterogeneous task execution delay threshold as a constraint,the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs.The proposed problem is proven to be NP-hard by using the method of reduction to a 0-1 knapsack problem.A many-to-many matching algorithm based on resource preferences is proposed.The algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by NCPs.This enables the filtering out of un-schedulable NCPs in the initial stage of matching,reducing the solution space dimension.To solve the matching problem between ICVs and NCPs,a new manyto-many matching algorithm is proposed to obtain a unique and stable optimal matching result.The simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6%compared to the reference scheme,and the total performance can be improved by up to 15.9%.

MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Evaluating the Robustness of MDSS Maintenance Forecasts Using Connected Vehicle Data

The Indiana Department of Transportation (INDOT) adopted the Maintenance Decision Support System (MDSS) for user-defined plowing segments in the winter of 2008-2009. Since then, many new data sources, including connected vehicle data, enhanced weather data, and fleet telematics, have been integrated into INDOT winter operations activities. The objective of this study was to use these new data sources to conduct a systematic evaluation of the robustness of the MDSS forecasts. During the 2023-2024 winter season, 26 unique MDSS forecast data attributes were collected at 0, 1, 3, 6, 12 and 23-hour intervals from the observed storm time for 6 roadway segments during 13 individual storms. In total, over 888,000 MDSS data points were archived for this evaluation. This study developed novel visualizations to compare MDSS forecasts to multiple other independent data sources, including connected vehicle data, National Oceanic and Atmospheric Administration (NOAA) weather data, road friction data and snowplow telematics. Three Indiana storms, with varying characteristics and severity, were analyzed in detailed case studies. Those storms occurred on January 6th, 2024, January 13th, 2024 and February 16th, 2024. Incorporating these visualizations into winter weather after-action reports increases the robustness of post-storm performance analysis and allows road weather stakeholders to better understand the capabilities of MDSS. The results of this analysis will provide a framework for future MDSS evaluations and implementations as well as training tools for winter operation stakeholders in Indiana and beyond.

Application of Connected Truck Data to Evaluate Spatiotemporal Impact of Rest Area Closures on Ramp Parking

Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ramps, which create safety risks for other interstate motorists. Historically, agencies have employed costly and time intensive manual counting methods, camera surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-time, offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there may be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and documents the observed spatiotemporal impacts of a rest area closure on truck parking on nearby interstate ramps. Results showed that there was a 28% increase in parking on ramps during the rest area closure. Analysis also found that ramps closest to the rest area were most impacted by the closure, seeing a rise in truck parking sessions as high as 2.7 times. Parking duration on the ramps during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ramps, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul commercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in informed data-driven decision making when allocating resources towards improving the nations commercial vehicle parking infrastructure.

Hydrodynamic Performance of Multi-Directional Connected Complex Platform Configurations

The development of very large floating structures(VLFSs)through the integration of multiple modules linked by connectors has resulted in a sophisticated multi-oscillator system.These flexible connectors are crucial to the stability and safety of the entire system,as they accommodate the dynamic interactions between the modules.The versatility of such complex configuration platforms,enhanced by multi-directional connectors,allows for a wide range of engineering applications owing to their adaptability in assembly and arrangement.In this study,a dynamic model within the frequency domain is meticulously constructed by linear wave and dynamic theories.This model facilitates a detailed hydrodynamic response analysis of complex configuration platforms,specifically those composed of triangular modules.The introduction of power flow theory further elucidates the coupling mechanisms and energy transmission effects within multi-directional connectors,offering valuable insights for the preliminary design layout of these platforms.Moreover,the research delves into the optimization of the stiffness configuration of the connectors.An optimization model is established via the linear weighted sum method,which considers the motion responses of the modules and the loads borne by the connectors.The genetic algorithm(GA)is employed to refine the stiffness configuration of the connectors with three-directional layout.This comprehensive approach not only enhances the understanding of the hydrodynamic behavior of VLFSs but also provides a methodological framework for optimizing their structural design.These findings are expected to significantly contribute to the field of marine engineering and inform the development of more robust and efficient VLFSs for various applications.

Evaluating impact of remote-access cyber-attack on lane changes for connected automated vehicles

Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,it can cause significant damage to CAVs or passengers.The primary objective of this study is to model cyberattacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment.Based on the analysis on environmental perception system and possible cyber-attacks on sensors,a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed.The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lanechanging process is then quantitatively analyzed.The evaluation indexes include spatio-temporal evolution of average speed,spatial distribution of selected lane-changing gaps,lane-changing rate distribution,lane-changing preparation search time,efficiency and safety.Finally,the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack.Also,when the traffic system is under cyber-attack,more unsafe back gaps are chosen for lane-changing,especially in the center lane.Therefore,more lane-changing maneuvers are concentrated on approaching the off-ramp,causing severe congestions and potential rear-end collisions.In addition,as the number of cyber-attacked vehicles and the severity of cyber-attacks increase,the road capacity and safety level will rapidly decrease.The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.

Adaptive linear active disturbance-rejection control strategy reduces the impulse current of compressed air energy storage connected to the grid

The merits of compressed air energy storage(CAES)include large power generation capacity,long service life,and environmental safety.When a CAES plant is switched to the grid-connected mode and participates in grid regulation,using the traditional control mode with low accuracy can result in excess grid-connected impulse current and junction voltage.This occurs because the CAES output voltage does not match the frequency,amplitude,and phase of the power grid voltage.Therefore,an adaptive linear active disturbance-rejection control(A-LADRC)strategy was proposed.Based on the LADRC strategy,which is more accurate than the traditional proportional integral controller,the proposed controller is enhanced to allow adaptive adjustment of bandwidth parameters,resulting in improved accuracy and response speed.The problem of large impulse current when CAES is switched to the grid-connected mode is addressed,and the frequency fluctuation is reduced.Finally,the effectiveness of the proposed strategy in reducing the impact of CAES on the grid connection was verified using a hardware-in-the-loop simulation platform.The influence of the k value in the adaptive-adjustment formula on the A-LADRC was analyzed through simulation.The anti-interference performance of the control was verified by increasing and decreasing the load during the presynchronization process.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the“dual carbon”strategy,intelligent networked new energy vehicle technology plays a crucial role.This type of vehicle combines the advantages of new energy technology and intelligent network technology,effectively reduces carbon emissions in the transportation sector,improves energy utilization efficiency,and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles,making significant contributions.This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

Biological processes involved in mechanical force transmission in connective tissue:Linking bridges for new therapeutic applications in the rehabilitative field

Connective tissue is a dynamic structure that reacts to environmental cues to maintain homeostasis,including mechanical properties.Mechanical load influences extracellular matrix(ECM)—cell interactions and modulates cellular behavior.Mechano-regulation processes involve matrix modification and cell activation to preserve tissue function.The ECM remodeling is crucial for force transmission.Cytoskeleton components are involved in force sensing and transmission,affecting cellular adhesion,motility,and gene expression.Proper mechanical loading helps to maintain tissue health,while imbalances may lead to pathological processes.Active and passive movement,including manual mobilization,improves connective tissue elasticity,promotes ECM-cell homeostasis,and reduces fibrosis.In rehabilitation,understanding mechanical-regulation processes is necessary for ameliorating and developing treatments aimed at preserving tissue elasticity and preventing fibrosis.In this commentary,we aim to globally describe the biological processes involved in mechanical force transmission in connective tissue as support for translational studies and clinical applications in the rehabilitation field.

Alterations of interhemispheric functional connectivity in patients with hypertensive retinopathy using voxelmirrored homotopic connectivity:a resting state fMRI study

AIM:To analyze whether alterations of voxel mirror homology connectivity(VMHC)values,as determined by resting-state functional magnetic resonance imaging(rsfMRI),occur in cerebral regions of patients with hypertensive retinopathy(HR)and to determine the relationship between VMHC values and clinical characteristics in patients with HR.METHODS:Twenty-one patients with HR and 21 agematched healthy controls(HCs)were assessed by rsfMRI scanning.The functional connectivity between the hemispheres of the cerebrum was assessed by measuring VMHC,with the ability of VMHC to distinguish between the HR and HC groups assessed using receiver operating characteristic(ROC)curve analysis.Differences in the demographic and clinical characteristics of the HR and HC groups were analyzed by independent sample t-tests.The relationship between average VMHC in several brain areas of HR patients and clinical features was determined using Pearson correlation analysis.RESULTS:Mean VMHC values of the bilateral cuneus gyrus(BA19),bilateral middle orbitofrontal gyrus(BA47),bilateral middle temporal gyrus(BA39)and bilateral superior medial frontal gyrus(BA9)were lower in the HR than in the HC group.CONCLUSION:VMHC values can predict the development of early HR,prevent the transformation of hypertensive microangiopathy,and provide useful information explaining the changes in neural mechanism associated with HR.

改进RRT-Connect算法的机器人路径规划研究

针对标准RRT-Connect算法在路径规划中存在的路径冗长、转折较多和区域通过性欠缺问题,提出了一种新的改进RRT-Connect算法(TRRT-Connect)。采用改进RRT算法搜索并添加一个中间根节点,实现同时扩展四棵随机树,加快算法收敛速度。在随机点的选取上使用目标偏置策略,在新节点的生成上叠加引力场,同时融合贪婪搜索策略。结合新的动态步长调节方法,通过识别扫描区域内障碍物的个数动态选择合适的步长。对生成的初始路径使用双向剪枝优化方法,加快剪枝效率,剔除路径上的冗余节点。对路径转折处进行光滑处理,减少路径转折。在三种不同环境地图中进行仿真对比实验,结果表明,TRRT-Connect算法与标准RRT-Connect算法相比较,在路径长度、迭代次数和节点数上有较大改善,在密集障碍物区域的通过性较好,路径更加光滑且无转折,证明了该算法的有效性。同时将TRRT-Connect算法应用于现场实例仿真中,使得移动机器人的运输路径长度相较于传统固定路径降低了15.4%,且路径光滑,进一步验证了该算法的实用性。

Soft-switching Grid-connected PV Inverter With Interleaved Flyback Topology

提出一种应用于单个光伏组件的软开关交错反激并网逆变器拓扑及其软开关控制策略。针对反激变压器漏感问题，提出漏感能量吸收回馈电路，实现了漏感能量吸收再利用，并实现了开关管漏源电压的钳位，提高了变换效率同时降低了开关管关断电压尖峰；提出基于数字处理器的反激变换器变开关频率谐振软开关控制策略，实现了开关管的零电压开通，同时改善了逆变器的电磁兼容特性；提出的交错并联反激逆变器有助于减小变压器和滤波器的体积，提高功率密度。详细分析变换器的工作原理，分析变开关频率谐振软开关控制方式的原理和实现条件，最后进行实验验证。

(α,β)-constraints connected dominating set algorithm in wireless sensor network

To cope with the constraint problem of power consumption and transmission delay in the virtual backbone of wireless sensor network, a distributed connected dominating set （CDS） algorithm with （α,β）-constraints is proposed. Based on the （α, β）-tree concept, a new connected dominating tree with bounded transmission delay problem（CDTT） is defined and a corresponding algorithm is designed to construct a CDT-tree which can trade off limited total power and bounded transmission delay from source to destination nodes. The CDT algorithm consists of two phases： The first phase constructs a maximum independent set（MIS）in a unit disk graph model. The second phase estimates the distance and calculates the transmission power to construct a spanning tree in an undirected graph with different weights for MST and SPF, respectively. The theoretical analysis and simulation results show that the CDT algorithm gives a correct solution to the CDTF problem and forms a virtual backbone with（ α,β）-constraints balancing the requirements of power consumption and transmission delay.

技术推动学习 为未来做好准备——美国ConnectED行动计划述评

随着对教育信息化水平的要求不断提高,美国亟待进一步提升网络连接水平。为此,奥巴马政府发起了"连结教育"(ConnectED)计划行动。该项目旨在通过升级网络、更新设备、培训教师以及推行"为未来做好准备的学区承诺"(Future Ready District Pledge)来提高美国教育信息化水平和质量。我国教育信息化发展可以从该项目的实施中吸取经验。

DYNAMIC ANALYSIS OF DAMPER CONNECTED ADJACENT BUILDINGS UNDER EARTHQUAKE

A formulation of the multi degree of freedom equations of motion for damper connected adjacent multi story buildings under earthquake excitation is presented.The ground acceleration due to earthquake is regarded as a stochastic process,and a pseudo excitation algorithm in frequency domain is implemented in a computer program to handle the non orthogonal damping properties of the system.The effectiveness of joint dampers is then investigated in terms of the reduction of displacement,acceleration and shear force responses of adjacent buildings.

Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment

We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from con-nected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period. Moreover, it can predict the traffic flow for various pene-tration rates of connected vehicles (the ratio of the number of connected vehicles to the total number of vehicles). At first, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetra-tion rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator. We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous pre-diction methods depend highly on data from fixed sensors (i.e., loop detectors and video cameras), which are associ-ated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data.

Dynamic responses of two buildings connected by viscoelastic dampers under bidirectional earthquake excitations

In this study,dynamic responses of two buildings connected by viscoelastic dampers under bidirectional excitations are extensively investigated.The two buildings are a 10-story building and a 16-story building,with the shorter building on the left.Viscoelastic dampers are installed at all fl oors of the shorter building.Equations of motion are formulated using a fractional derivative model to represent the viscoelastic dampers.Three cases are considered with mass eccentricities at 0,10% and-10% with respect to the dimensions of the buildings.The responses of the buildings are numerically predicted at different damper properties.The simulation results indicated that the maximum horizontal responses of the buildings without eccentricities are signifi cantly mitigated.However,torsional effects are adversely increased.For asymmetric buildings,the effectiveness of the connecting dampers is affected by building eccentricities.As a result,mass eccentricities must be taken into account in damper selection.When compared with vibrations induced by unidirectional excitations,bidirectional excitations can increase the responses of coupled asymmetric buildings.In addition,installing dampers only at the top fl oor of the shorter building may cause a sudden change in lateral stiffness of the taller building.Consequently,the story shear envelopes of the taller building are changed.