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This paper proposes a deep mining method of high-dimensional abnormal data in Internet of things based on improved ant colony algorithm. Preprocess the high-dimensional abnormal data of the Internet of things and extract the data correlation feature quantity; The ant colony algorithm is improved by updating the pheromone and state transition probability; With the help of the improved ant colony algorithm, the feature response signal of high-dimensional abnormal data in Internet of things is extracted, the judgment threshold of high-dimensional abnormal data in Internet of things is determined, and the objective function is constructed to optimize the mining depth, so as to realize the deep data mining. The results show that the average error of the proposed method is only 0.48%.

The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV’s lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.

Detecting traffic signs is an essential task for intelligent and connected vehicles. In this study, a modified model based on the method of You Only Look Once (YOLO) is proposed for detecting different types of Chinese signs, including mandatory, prohibitory, danger warning, guide, and tourist signs. Images of Chinese traffic signs are collected in real scenes and a new dataset is established. The modified model combines the DenseNet method with the YOLOv3 network. Dense blocks are used to strengthen feature propagation and promote feature reuse in those feature layers with low resolution in the YOLOv3 network. Experimental results on the test dataset reveal that the average precision of the modified model, the original YOLOv3, and the YOLOv2 networks are 95.92, 94.59, and 89.39%, respectively. Further comparative analyses that give more detailed experimental evaluation results are conducted on the designed model, including (i) the performance of the designed model based on five categories; (ii) the influence of training set size on the designed model; (iii) the performance of the designed model on occlusion and no object conditions in real scenes. The experimental results show that the modified model is effective at fast and accurate Chinese traffic sign detection in real scenes.

Vehicular Ad Hoc Network (VANET) is the basic technology of intelligent transportation systems for providing reliable and real-time communications between vehicles and vehicles or roadside units. In order to improve the communication quality of VANET, this paper studies the effects of different maximum routing hop count parameters on the performance of the network under different vehicle densities. We establish the mathematical models of node connectivity probability and the packet delivery ratio by using the Poisson distribution model. And the maximum routing hop count selection algorithm (MRHSA) is proposed based on the theoretical model established in the paper. The simulation experiments and statistical analysis on packet delivery ratio, throughput, and end-to-end delay are performed under the straight road and urban road scenes, supported by the Vehicle in Network Simulation (Veins). The results show that the maximum routing hop count parameter is an important impact factor on the communication quality of the network. It is found that MRSHA proposed in this paper can improve the packet delivery ratio by about 9.1% at most in straight road scenarios, which indicates that MRHSA will contribute to the improvement of the communication quality of VANET.

In order to improve vehicle control safety in intelligent and connected environments, a fuzzy drive control strategy is proposed. Through the fusion of vehicle driving data, an early warning level model was established, and the fuzzy control method was used to obtain the appropriate torque command under the vehicle condition, torque optimization processing was performed according to the different corresponding vehicle following characteristics. The control strategy was tested and verified on an established platform. Based on the experimental results, compared with the traditional drive strategy in one-way front and rear following scenarios, the vehicle avoided excessive opening and closing of the accelerator pedal when the distance between vehicles was close, maintained the correct distance in the following situation, and had better dynamic response when the distance between vehicles was large, indicating that the proposed drive strategy had a better real-time and security performance.

SUMMARY OBJECTIVE: The aim of this study was to analyze the effect of tirofiban on new cerebral microhemorrhage after mechanical thrombectomy in patients with acute ischemic stroke. METHODS: In total, 203 patients with acute ischemic stroke treated by mechanical thrombectomy in our department of neurology were enrolled as the research objects. The patients were divided into two groups: the patients who used tirofiban within 24 h after surgery were assigned to the study group (78 subjects), while patients who did not use tirofiban were assigned to the conventional group (125 subjects). Magnetic resonance imaging was used to detect new-onset cerebral microbleeds in patients with stroke after surgery. The National Institute of Health Stroke Scale, modified ranking scale, and activity of daily living scale were used to assess the prognosis of patients, and the general data and the occurrence of adverse effects between two groups were compared to comprehensively evaluate the efficacy and safety of tirofiban. RESULTS: The proportion of atrial fibrillation in the research group was significantly lower than that in the conventional group. The research group had a much lower rate of new-onset cerebral microbleeds than the conventional group (p0.05). CONCLUSION: The application of tirofiban in mechanical thrombectomy of patients with acute ischemic stroke has high safety, effectively reduces the occurrence of new cerebral microhemorrhage, and provides a guarantee for patient safety.

V2X technology enables the intelligent and connected vehicles (ICVs) to communicate with surrounding vehicles in real time. In order to obtain the data related to the lane-change behavior of the ICVs and its surrounding vehicles, a positioning data acquisition system with high precision is designed, including hardware and software. In the designed system, the high-precision position, velocity, acceleration and direction angle data are acquired based on the Sinan navigation high-precision receiver M100, the MK5 OBU of Cohda Wireless can be used to realize real-time communication of information, and the main controller is designed based on STM32, completes the collection and transmission of high-precision positioning information, and stores it in the SD card after time synchronization. The relevant data was collected by installing the designed system on the test vehicle, and the lane-changing behavior was manually marked and time stamped. The analysis of experimental results shows that the designed system can basically achieve lane-level accuracy in urban open road scenes, and accurately describe the trajectory of vehicle lane-change behavior.

The Medium Access Control (MAC) layer contention protocol is closely related to the performance of network throughput, end-to-end delay, and access fairness on the Internet of Vehicles (IoV) communication. Based on the MAC layer protocol of the Wireless Access in Vehicular Environments (WAVE) standard system, this paper proposes a MAC layer contention window adaptive adjustment policy using Reinforcement Learning. Through the detection of the number of neighbors and the application of the Q-Learning algorithm, the vehicle can adjust the contention window according to the number of nodes competing for the same channel to adapt to the changing environments of the IoV. Three different MAC protocols are simulated and analyzed under the Vehicle in Network Simulation (Veins) platform. The results show that the proposed MAC protocol based on neighbor detection and Q-Learning performs better than WAVE MAC protocol and general MAC protocol based on Q-Learning.

In order to achieve fast and accurate vehicle taillamp intention recognition for intelligent and connected vehicles, a taillamp intention recognition method based on the YOLOv4 algorithm was proposed. Firstly, the images of the vehicles in different lamping conditions during driving were collected, and the corresponding dataset was established for training and testing the model. Then the input parameter was modified, and the cosine annealing learning rate algorithm was applied to the YOLOv4 framework. Experimental result showed that this method had an accuracy of 90.27% for vehicle taillamp intention recognition and a detection rate of 20fps. This method can realize the intention recognition of vehicle taillamps, and provide a basis for driving decision-making of intelligent and connected vehicles.

Objective: To investigate the effect of different operation time on the surgery effect and quality of life of patients with severe hypertensive cerebral hemorrhage. Methods: A total of 98 patients with severe hypertensive cerebral hemorrhage were selected in this prospective study. According to the random number table, 98 patients were divided into group A and group B. About 47 patients in group A received surgical treatment within 6 hours after onset of a cerebral hemorrhage and 51 patients in group B received surgical treatment within 6-24 hours after onset of a cerebral hemorrhage. The effect of the operation, quality of life (the World Health Organization Quality of Life Scale Brief Version, WHOQOL-BREF) score, neuro function (National Institute of Health stroke scale, NIHSS), the ability of daily living (Barthel index), athletic ability (Fugl-Meyer motor function score), complications and prognosis (GOS) were compared between the two groups. Results: The total effective rate of operation in group A (91.49%) was higher than that in group B (76.47%), and the incidence of complications (8.70%) was lower than that in group B (27.08%; all P

Detection of lane-change behaviour is critical to driving safety, especially on highways. In this paper, we proposed a method and designed a learning-based detection model of lane-change behaviour in highway environment, which only needs the vehicle to be equipped with velocity and direction sensors or each section of the highway to have a video camera. First, based on the Next Generation Simulation (NGSIM) Interstate 80 Freeway Dataset, we analyzed the relevant features of lane-changing behaviour and preprocessed the data and then used machine learning algorithms to select the suitable features for lane-change detection. According to the result of feature selection, we chose the lateral velocity of the vehicle as the lane-change feature and used machine learning algorithms to learn the lane-change behaviour of the vehicle to detect it. From the dataset, continuous data of 14 vehicles with frequent lane changes were selected for experimental analysis. The experimental results show that the designed KNN lane-change detection model has the best performance with detection accuracy between 89.57% and 100% on the selected dataset, which can well complete the vehicle lane-change detection task.

In this work, we focus on the Vehicle-to-Vehicle (V2V) radio channel characteristics of two vehicles meeting under the same direction condition and the opposite direction condition based on actual measurements. For two different conditions, we analyze and model the channel characteristics of small-scale fading distribution, Power delay profiles(PDP), received power and Doppler spread. Through the judgment of Akaike Information Criterion, it is concluded that the small-scale amplitude fading distribution has lower Rician and Weibull distributions in the case of meeting in the opposite direction. For the case of meeting in the same direction, the results of PDP and receiving energy show that there are more reflection paths and higher energy. Through the modeling of multipath model, it is proved that there is more obvious multipath. For the case of meeting in the opposite direction, there is a more obvious Rician and Weibull distribution in the small-scale amplitude characteristics, and a higher value in the Doppler spread. By comparing the results, we find that there are obvious differences in channel characteristics between the two meeting conditions. The above results can provide some reference for the application of V2V communication technology in intelligent transportation system.

In order to achieve high-quality entertainment services and large-capacity sensor sharing, it is imperative to improve the throughput of V2I communication systems. Millimeter wave communication is a promising technology, which is generally combined with beamforming techniques. When it comes to beam design, we need to focus on the tradeoff between system throughput and alignment overhead. However, conventional optimization schemes are limited by uniform beamwidth design. Considering the characteristics of V2I communication on the highway, this paper proposes a non-uniform beamwidth design idea. Firstly, an average throughput model based on beam training is established. Then, a recursive algorithm is used to achieve non-uniform beamwidth optimization. Finally, the simulation results prove that the non-uniform beamwidth scheme can significantly improve the throughput of the V2I system.

As an important part of the Internet of vehicles (IoV) system, many research team have been working on the development of On-Board Unit (OBU) with high precision. In this paper, a new centimeter-level positioning OBU based on LTE-V, dead reckoning (DR) and network real time kinematic (RTK) positioning is designed. The hardware and software design of the terminal are introduced in detail. And the performance indexes, including delay, packet loss rate, static and dynamic positioning accuracy of the terminal, are measured in two scenarios. The experimental results show that the dynamic horizontal positioning error is less than 35mm, which is relatively high precision in IoV system.

Sulfate attack is a type of widely distributed corrosion to cement-based materials, and the utilization of recycled aggregate concrete is beneficial for reducing construction solid waste and the consumption of natural resources. To study the performance of ordinary cast-in-situ recycled aggregate concrete subjected to sulfate attack under various service conditions, five different exposure conditions, full and partial immersion and drying-wetting cycles for 40 weeks were investigated. The sulfate concentration, compressive strength, mass and diameter variation, and visual appearance of the recycled aggregate concrete cylinders were measured. Furthermore, microscopic analysis of the scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD) and mercury intrusion porosimetry (MIP) was carried out to observe the microstructure and corrosion products. The micro observations of the interfacial transition zone (ITZ) were analyzed by SEM and EDS. The evolution of the pore structure under sulfate attack was measured by the MIP test. The results show that the specimens under the partially immersed condition endured the most severe damage, and significant mass loss and scaling were observed due to chemical attack and salt crystallization. The specimens under drying-wetting conditions deteriorated far more rapidly than those under fully immersed conditions. In addition, ettringite, gypsum and micro-cracks were observed regardless of the exposure conditions. However, no evident characteristic of ettringite or gypsum appeared in the ITZ. This paper suggests that the key role of ITZ could be to provide a diffusion path for sulfate due to its high permeability and porosity.

In vehicular network, high reliability and low latency of communication ensure the driving safety. In order to avoid traffic accidents, it is important to warn surrounding vehicles before a potential traffic accident occurs as soon as possible. In the case of short spectrum resource, a reasonable resource allocation for alarm information is critical in V2V (Vehicle to Vehicle) communication. In this paper, a modified scheme is introduced by taking account of vehicle's priority level and spectrum availability to allocate the frequency resources to upcoming cars. The connection among priority level vehicles will be ensured and channel congestion caused by the surrounding vehicles will be drastically reduced while spectrum resources are insufficient. Simulation results show that the combinative use of sharing vehicle level and available spectrum resource improves the efficiency of spectrum resources, shortens the access-time of alarm information on the premise of successful communication, and improves the safety of vehicle driving.

In recent years urban traffic safety has become a widely concerned issue due to the increasing traffic accidents caused by the growth of private car ownership. A very promising method is to provide high precision navigation service to prevent accidents. Therefore, a Network BDS/RTK System Based on Vehicular Networks (NBRSBVNs) is proposed for high precision positioning in an urban complex traffic environment in this paper. This system considers comprehensively the characteristics of the vehicular network and network RTK techniques, and also provides a new distribution method of high precision differential correction information. According to the massive field tests, this system scheme can support real-time and online high precision navigation service of multiple users at the same time. It has important practical significance in applications for vehicles active safety in a complex urban environment.

A challenge in posteriori data processing for passive GPS based travel survey, which constitute the heart of this paper, is to develop a series of methods to automatically restore the sequences of data points, both in space and time. It means the trips and activities occurred in the survey time should be identifiable chronologically and those identified by the program should respect this definition convention. Reference to the research results of our colleagues, and by combining the experiences of other French travel survey and personal mobility survey at Lille, a series of methods has been developed and put into application. The data outcome is ready for further applications.

Multi-radio channel allocation (MRCA) is a key issue to improve the system performance supporting all kinds of mobile applications in wireless networks. However, existed work on non-cooperative MRCA just considers the cases under the extreme saturation load condition and static game, which means that it is assumed that all the nodes always have packets to send and mobile terminals just change the channel once. Such assumption is obviously impractical for actual wireless network and isn't suitable for the mobility of applications without intelligent spectrum management. Meanwhile, the existed schemes are unable to support mobile applications with time-varying channel. In this paper, an enforceable incentive mechanism called EIM is proposed for dynamic non-cooperative multi-radio channel allocation to support mobile applications. In order to optimize the overall performance of mobility management, in the proposed EIM, the strong assumption of saturation network traffic is relaxed, which makes the mobile terminals are able to have time-varying bandwidth requirements. Then, the MRCA problem is modeled as an infinite repeated game. Detailed theoretical analysis proves that there exist multiple pure Nash Equilibriums (NEs) for the game, and, under certain condition, there also exist NEs which are not Pareto-Optimal. Hence, based on the punishment strategy, the proposed EIM can achieve social welfare. Detailed numerical and simulation results show that the proposed EIM can improve the system throughput about 15 % compared with the existed works, which can provide an efficient method to manage channel resource and get better ubiquitous online access to preferred mobile services.

P systems withd-division are a particular class of distributed and parallel computing models investigated in membrane computing, which are inspired from the budding behavior of Baker’s yeast (a cell can generate several cells in one reproducing cycle). In previous works, such systems can theoretically generate exponential working space in linear time and thus provide a way to solve computational hard problems in polynomial time by a space-time tradeoff, where the precise execution time of each evolution rule, one time unit, plays a crucial role. However, the restriction that each rule has a precise same execution time does not coincide with the biological fact, since the execution time of biochemical reactions can vary because of external uncontrollable conditions. In this work, we consider timed P systems withd-division by adding a time mapping to the rules to specify the execution time for each rule, as well as the efficiency of the systems. As a result, a time-free solution to Hamiltonian path problem (HPP) is obtained by a family of such systems (constructed in a uniform way), that is, the execution time of the rules (specified by different time mappings) has no influence on the correctness of the solution.

The communication quality of a vehicular network is confronted with great challenges due to a surge in vehicle numbers as well as the high mobility of vehicles on the road. Network coding technologies can improve network performance in terms of higher throughput, reliability, and robustness. In this paper, we generally consider the characteristics of vehicular communication, network coding, and cooperative communication and study the methods of outage probability analysis in a vehicular cooperative network. Based on this, we propose a novel mobile dynamic relay selection strategy with network coding and also derive the expressions of diversity gain and channel capacity. Finally, we conduct simulation experiments with different scenarios to verify the proposed strategy. The theoretical analysis and simulation results show that the proposed relay selection strategy can effectively reduce the outage probability, improve the system diversity gain, and expand the system capacity.

Existing works about relay selection in Vehicle Cooperative Communication (VCC) network assume that only relay nodes move around while source nodes and destination nodes are static. However, in the practical VCC network, source nodes and destination nodes are also mobile. To address this limitation, this paper considers the mobility of source and destination nodes, and designs a new mobile relay selection scheme, which targets at reducing system outage probability and increasing diversity gain and system capacity. In this paper, first, we utilize the Weibull fading channel model to analyze channel characteristics in the mobile environment. Secondly, a relay selection scheme is designed to improve the network performance. Finally, the performances of the mobile relay selection scheme by simulations were evaluated. Simulation results show that the selection scheme effectively reduces outage probability, and increases system capacity to achieve integrity of the well-organized system.