Advances in Smart Vehicular Technology, Transportation, Communication and Applications

Rail transit vehicle frame overhaul and maintenance is an important work to ensure the safe and reliable operation of vehicle equipment. To solve the problems of different frame overhaul modes and resource optimization of rail transit operation, this paper summarizes the frame overhaul and maintenance of major cities, and proposes a quality management method for urban rail transit vehicle frame overhaul project. The method is composed of a combination strategy, critical path method and PDCA cycle theory. The result shows that the method saves 4 day and 1 day maintenance times, respectively, by optimizing failure mode 1 and failure mode 2, which means it can improve maintenance efficiency, reduce maintenance cost and provide theoretical for the optimization of enterprise vehicle frame overhaul workshop.

The sharp increase in railway passenger flow during the Spring Festival Travel Season has tested the organization and dispatching ability of the railway transportation system. In this paper, the advantages of least square support vector machine (LSSVM) in small sample data prediction are integrated, and the ARIMA-LSSVM hybrid model based on residual linear transfer superposition is proposed, which is verified by Xiamen Spring Festival railway passenger flow. The analysis results show that the average absolute errors of hybrid model are 0.565 × 104 and 0.979 × 104 person times, respectively, which are 22.50% and 12.43% higher than ARIMA model, and 28.30% and 18.35% higher than LSSVM model. This study plays a positive role in improving the railway passenger flow forecasting ability and adjusting the preparation time during the Spring Festival Travel Season.

With the large-scale construction of China's high-speed railway network, the demand for passenger flow is growing and varies in different seasons. Therefore, refinement operational requirements are increasingly prominent. However, the passenger flow itself has time-varying characteristics, and the division of passenger flow seasons and periods is divided by manual methods and experience subjectively. As a result, it is difficult to adjust it dynamically with the passenger flow changing dynamically. There are few studies on the seasons and periods of the division about high-speed railway passenger flow at home and abroad. In this study, the seasonal and period division methods of high-speed railway passenger flow were studied, the dynamic characteristics of passenger flow were analyzed, and the annual passenger flow fluctuation was considered. Then, the high-speed railway passenger flow was divided into seasons by using the firefly affinity propagation algorithm. Meanwhile, the high-speed railway passenger flow was divided into periods by employing the orderly clustering algorithm with the consideration of the daily passenger flow fluctuation.

Based on the “road network-region-cluster” three-level network, the optimization problem of train operation scheme under network condition is transformed into the problem of direct train operation and train combination between regions on cluster network. The design of train operation scheme based on the railway network realizes the passenger flow transportation within the regional network. However, the cross-network passenger flow on the regional network still needs to transfer at the overlapping nodes. In this paper, the train merging adjustment is carried out on the overlapping nodes identified by the regional network division, and the principle of train merging is studied. The train merging adjustment model is designed with the goal of the highest direct passenger flow, the largest train operation benefit and the lowest passenger travel cost, and the genetic algorithm is designed to solve the model. Finally, taking the high-speed rail network in Chengdu Bureau as an example, the feasibility and rationality of the model and algorithm proposed in this paper are verified.

The special function orientation and passenger flow characteristics of suburban railway put forward higher demands for its transportation organization, such as mass transit type, rapidness, diversification, and convenience. Under the condition of suburban railway of mass transit type, the characteristics of high density in and out of high-speed trains will affect the safety of waiting and landing at passenger platforms. Based on the current passenger organization and technical level, this paper discussed the necessity and technical feasibility of setting platform door in suburban railway. The results show that the installation of platform door can greatly improve the passenger experience, and the technical conditions for the development of platform door system in suburban railway have been met. It is suggested that the new suburban railway should consider the installation of platform gate or reserve the engineering conditions for the installation of platform gate.

With the development of informationization and intelligence of railway passenger stations, problems such as inconvenient information interaction, missing operation and maintenance data, and difficulty in accurate positioning of equipment under the existing equipment operation and maintenance management mode have become the focus. In this paper, the operation and maintenance management process of equipment is divided into three stages: fault prediction and warning, fault diagnosis and processing, and fault rule summary. The implementation schemes of key technologies such as data warehouse, data mining, 5G fusion positioning, and electronic fence are given to realize functions such as condition assessment, fault prediction, fault diagnosis, precise positioning, fence warning, and auxiliary decision-making, which can meet the needs of managers and operations people. Research will help to improve the efficiency and safety of equipment operation and maintenance, and have a reference significance for integrated intelligent operation and maintenance technology and the construction of modern passenger stations.

The adaptability level of the operation plans and passenger flow demand has an influence on improving the passenger service efficiency and service level of urban rail transit express and local trains. In order to improve the matching degree between the operation plan and the passenger flow demand, the evaluation system is constructed from two aspects: passenger flow service quality and passenger flow service structure, and the AHP-set pair analysis method is used to quantitatively evaluate the adaptability of express and local train operation plan and passenger flow demand. Taking Tianjin Metro Line 9 as an example, this paper evaluates the adaptability of the passenger flow demand of four express and local train operation plans, and then obtains the evaluation results and proposes corresponding adjustment suggestions for the operation plan. The research shows that the plan $${P}_{1}$$ P 1 is most suitable for the passenger flow demand of Tianjin Metro during peak hours.

Taking high-speed rail express as the research object, the research is carried out on the network operation mode of high-speed rail express. Taking the minimum cost of transportation enterprises as the optimization goal, the decision model of the network operation mode of high-speed rail express is constructed by taking the remaining capacity limit of the middle section of the high-speed rail express line network, the carrying capacity and operating cost restrictions corresponding to the high-speed rail express operating mode, and the overall transportation task limit as constraints. Taking the train operation map in the third quarter of 2021 as a reference, the decision-making model for the construction of the operation mode is verified, and the ARIMA model is used to calculate the OD of each city node in the prospect year, and the carrying capacity and operation cost of different operation modes are analyzed and calculated. The results show that: my country should adopt a high-speed rail express network operation mode combining direct transit. Transit transportation completes the collection and distribution of small batches of goods, improves the full load rate of high-speed rail freight trains, expands the scope of services, and completes long-distance transportation of large batches of goods through direct transportation, improving economies of scale.

Locomotive routing problem of heavy haul railways is more difficult than that of regular railways because it has some unique characteristics including different numbers of locomotives utilized for different train type, mixed using of various locomotive types, and unpaired locomotive turn-around. This paper extends the existing model of LRP. Locomotive attached variable is introduced to ensure locomotive turn-around in pair. Tractive force supply and demand balance constrain is used to transform the complex effects of multi-locomotive traction and various locomotive types. Locomotive servicing and maintenance requirements is transformed into constrains utilizing conditional discrimination. Then we develop a MIP model which has a huge scale. It is difficult to solve it utilizing the exact algorithm. We design a two-stage heuristic algorithm. In the first stage, the initial solution is produced by the heuristic algorithm based on dynamic table of locomotives staying at stations. In the second stage, Tabu Search is used to optimize the initial solutions deeply with the search space limit method and the feasible solution transformation method which are used to handle the model constraints. We conduct many computational experiments and comparative experiments to prove the rationality of the model and the effectiveness of the algorithm. The results of this paper can provide an effective reference for the study on the complex locomotive routing problem.

The dynamic freight train diagram is the key link to realize the whole-process integrated transportation of goods and the key to realize the “According to the Timetable”. This paper studies the optimization of freight train diagram based on the basic diagram for dynamic route selection with market orientation as the core. Firstly, considering the quality of freight service and the satisfaction of cargo owners, taking the minimum transportation time consumption of each freight train as the optimization goal, starting from the characteristics of dynamic traffic flow, considering the connection time of traffic flow with the train at the station and the arrival period of the loaded goods, the dynamic route selection optimization model based on the basic diagram of the train diagram is established, and the solution strategy of the improved particle swarm algorithm based on binary coding is designed. Finally, a small road network case is constructed to verify the feasibility of the model and algorithm.

The equipment management informatization and intelligentization in railway passenger stations is a significant component of the construction of intelligent stations. The current equipment management of railway passenger stations is slowly developing and backward in its approach. This paper studied the weaknesses of equipment management in railway passenger stations, conducted demand analysis, and proposed the development direction of equipment management in railway passenger stations with the support of theoretical basis and technical basis. The technical architecture of the high-precision positioning-based railway passenger station equipment management system was built from the terminal layer, application layer, service layer, data layer and perception layer, and four functional modules were designed for equipment management: equipment real-time dynamic trackment, full life-cycle management, equipment fault responsibility traceability, and incident response. The key technologies required for the construction of the system were studied, including Bayesian fusion-based equipment positioning technology with high accuracy and neural network-based remaining life prediction technology.

In order to improve the quality, efficiency of urban rail transportation plan with the rapid development and networked operation of urban rail transportation in China, this paper designs an integrated system for train working diagram of urban rail transportation networks. The overall system architecture, network architecture, and functional structure are studied, focusing on the main functions such as network transportation plan evaluation, train working diagram preparation, and train connection evaluation.

Taking the transship trains of the railway hub as the research object, a study was carried out on the optimization of the operation organization of the transship trains in the hub. With the goal of minimizing the running cost of transship trains, an optimization model of the operation organization of transship trains in railway hubs is constructed under the condition that the trains can be picked up and delivered on time. At the same time, the railway hub case is selected, the model is used to analyze, the improved SA algorithm is designed to solve it, and the model and algorithm are verified. The results show that the constructed model and algorithm can make full use of resources such as dispatching vehicles on the premise of realizing the timely pickup and delivery of trains in the hub, and obtain a suitable operation plan for transship trains in the hub, so as to minimize the overall operating cost, so as to achieve the optimization of the operation organization of transship trains in railway hubs improves the efficiency of cargo transportation.

At present, the freight train formation plan cannot dynamically reflect the changes in freight demand in the transportation market, which makes it difficult to guide the actual transportation production work and affects the economic benefits of enterprises. This paper takes Shenhua Heavy Duty Railway as the research object, studies the optimization technology of cargo train programming under dynamic planning type transportation organization mode, and proposes the optimization principle of dynamic cargo train programming of Shenhua Railway. By analyzing the characteristics of the cargo transportation organization of Shenhua Railway, the dynamic planning type transportation organization mode of Shenhua Railway is proposed. On the basis of the concept of the dynamic freight train operation scheme of Shenhua Railway, the feasibility of preparation optimization, influencing factors, and main principles, the idea of dynamic preparation optimization research from the perspective of synergistic consideration of heavy train operation scheme and empty train deployment scheme is also elaborated.

In recent years, domestic waste is increasing day by day, which puts forward higher requirements for waste recycling capacity. In order to make the garbage collection process more efficient, the structure of the garbage collection manipulator is optimized. Firstly, the simulation software UG nx12.0 is used for three-dimensional modeling, and then the virtual simulation software ADAMS is used for kinematic simulation analysis. Finally, the finite element static analysis and modal analysis are carried out for the whole manipulator. The results show that the optimized manipulator has no resonance in the recovery process and operates stably.

In this paper, a space satellite cluster manipulation device based on the interaction of high-temperature superconductors (HTS) and permanent magnets (PM) is designed. The device can maintain the mutual spatial positions and attitudes of different satellites in a self-stabilizing manner without fuel consumption. It can also be manipulated to change their mutual position to achieve arbitrary changes in the satellite cluster configuration.

As new energy vehicles can minimize energy use and environmental harm, both the government and the public have expressed interest in and support for the new energy vehicle industry, which is developing significantly. The market for new energy vehicles is growing and prospering, but it is also struggling with capital limitations. The traditional auto financial service system places restrictions on the financing of the new energy vehicle industry. Additionally, the financing mode is unable to support the requirements of the supply chain for the manufacture of new energy vehicles, and the channel for transmitting supply chain information is constrained. The new energy vehicle industry should aggressively create a financing mode, adopt confirming storage financing mode, accounts receivable financing mode, private equity fund, and financial leasing financing mode to raise funds in order to address the issue of supply chain financing, and support the industry’s wholesome and long-term growth.

As an important tool for parenting, baby walkers (BW) need adequate safety and functionality. At present, many problems in the safety and functionality of BW on the market have affected the personal safety of infants and young children. This paper aims to design a BW that can realize automatic braking and alarm function when overtime use. The strength and size of the BW are checked by the finite element analysis method, the comfort is analyzed according to the modal analysis theory and the ergonomics theory, and the function is realized by the control component with the single-chip as the core. The experimental results show that the design meets the set objectives.

A model based on the random forest is constructed to repair the missing trade times in ETC transaction data. The driving speed and traffic volume characteristics of the vehicles in the ETC transaction data are analyzed, while the driving speed of the missed transaction vehicles, and the distance of the road section where they are located, are combined as input features to repair the missing transaction time. A one-day transaction data of a province is used to test. The analysis results show that the random forest model has a better restoration effect and has a smaller mean absolute error and root mean square error; its mean absolute error is 2.71 s, the highest accuracy among the compared methods, and the data are more accurate and complete after interpolation using the random forest model. This paper suggests that the research based on ETC transaction data should first adopt the processing method in this paper to repair the missing trade time in the transaction data to improve the integrity of the data used and ensure the validity and accuracy of the relevant calculation results.

Highway is facing the pressure of increasing transportation demand, Intelligent Transportation System (ITS) can improve the traffic efficiency and prevent the occurrence of congestion, ETC data provides the data foundation for the construction of ITS. Traffic flow prediction as an important part of ITS, and it has important research meaning. To address the problem that existing traffic flow prediction models cannot identify the dynamic changes of spatial–temporal dependency in traffic flow sequence data, this paper proposes a model based on the combination of self-attentive mechanism and GRU to predict the highway traffic volume. This paper first analyzes the different features affecting the changes in highway traffic flow and then describes the way of combining the attention mechanism and GRU. The GRU layer processes the input of the temporal sequence, and the self-attention layer extracts features that are important to the results in the traffic flow time series data. Finally, the experimental validation is performed by using the actual data generated from the ETC gantries of the highway in Fujian Province, China. The results show that the performance of the model proposed in this paper is better than other models at different time intervals.

Comprehending the variation in traffic flow is critical to alleviating traffic congestion at expressway toll station exits. Despite the fact that various traffic flow forecasting models have been proposed, most of them make predictions based on the entry traffic in the area near the target toll station. For origin–destination data like from entry to exit, these methods can hardly capture information on vehicles in transit. In this work, we suggest for the first time predicting toll station exit flows based on expressway gantry data. Moreover, in order to obtain the contribution of multiple gantry series to the exit traffic flow, a recurrent neural network incorporating spatio-temporal attention mechanism is proposed. The proposal not only predicts effectively but also improves the interpretability of the model. Comparative experiments were conducted using data from the gantry system and toll station data of the expressway in Fujian Province, China. The experimental results show that the proposed model performs better than other baseline methods.

Through the application and expansion of expressway ETC gantry transaction data, we propose a short-term traffic flow forecasting of expressway based on the Kalman Filtering (KF) and Random Forest (RF) model, which not only takes into account the basic external features and periodic features but also considers the spatio-temporal correlation relationship in the road section, so as to construct the spatial correlation features and temporal correlation features. In this paper, we use the ETC gantry transaction data of Fuzhou–Xiamen section of the expressway to forecast and verify in Fujian Province, China, the final results show that: When the rolling window is 20 min, compared with the results before and after Kalman Filtering algorithm processing traffic flow data, the performance indicators is greatly improved, which verifies the positive effect of Kalman Filtering algorithm; it is also verified that the constructed features have a great influence on traffic flow forecasting and play a positive role in improving forecasting accuracy; and it is also verified that the RF model has better forecasting effect than the baseline models.

Due to the increased online activity, people of all ages, especially adolescents, may get exposed to objectionable image content such as internet pornography. These images are spread quickly and widely over the internet, which causes serious social problems. Many researchers have proposed objectionable image content classification models by utilizing deep neural networks to prevent such images from being retrieved while surfing the web. The performance of such models can be enhanced by the semi-supervised learning method by effectively utilizing the image data from an overwhelming number of unlabeled objectionable samples. For many such unlabeled objectionable images, this paper proposes a semi-supervised image content classification framework using a balanced sample inclusion mechanism based on a higher class probability outcome to include the pseudo labels effectively in the existing model. The proposed framework fully utilizes semi-supervised learning and gradually improves model classification accuracy and reliability.

With the development of edge technology in the fields of transportation, wireless sensor networks, and the internet of things, more and more intelligent optimization algorithms are implemented in hardware structures. The Rafflesia optimization algorithm (ROA) is a novel intelligent optimization algorithm proposed recently. To observe its performance on hardware, this paper implements the ROA algorithm in a cooperative way of software and hardware, referred to as the FROA algorithm. To convenient testing and accelerated computing, the initialization module, fitness module, and update module of the FROA algorithm are deployed on the advanced RISC machine (ARM) platform and the field programmable gate array (FPGA) platform, respectively. In the experimental part, we test the FROA algorithm on nine different benchmark functions. The results are compared with those implemented in software. The experimental results demonstrate the effectiveness and superiority of the FROA algorithm.

Combining the characteristics of PPE, QUATRE algorithm, and orthogonal learning, this paper proposes a hybrid Orthogonal Learning and QUATRE algorithm based on the PPE algorithm (OLQTPPE). This algorithm takes the PPE algorithm as the main body and uses the QUATRE algorithm to search deeper. The purpose of using the QUATRE algorithm is to prevent the algorithm from falling into local optimization. After this, orthogonal learning is used to optimize the whole, to find better particles in a small area. The algorithm is tested on CEC2014 and compared with PSO, PPSO, BA, and PPE. The results show that the algorithm is superior to the four algorithms. In particular, it is the proposed OLQTPPE algorithm that has high performance and effectiveness compared to PSO, PPSO, and BA algorithms.

With the high level of information technology in modern society, a series of intelligent optimization algorithms have emerged to solve classic multi-combinatorial optimization applications. The origin of intelligent algorithms is the intelligent behavior and physical phenomenon of biological communities in nature, and a large number of intelligent optimization algorithms are widely used in various combinatorial optimization problems. Gannet optimization algorithm (GOA) is a newly proposed intelligent optimization algorithm, which is applied to large-scale constrained optimization problems with the advantages of high convergence and high-quality solutions. For the traveling salesman optimization problem (TSP), the original traditional way is very difficult to calculate. The calculation difficulty increases exponentially with the increase in the number of cities and is rarely used in real life. In this paper, we use the GOA to optimize the TSP problem. Experiments are carried out through two TSP instances, it can be seen from the experimental results that GOA can find a better solution with less computation time.

This paper improves the Artificial Hummingbird Algorithm (AHA). First, we introduce a compact scheme to reduce computer storage capacity and speed up computation. Second, the parallel strategy is added to improve the optimization ability of the algorithm. Third, we improve the original algorithm’s territorial and migration foraging strategies. The purpose of enhancing the territorial foraging strategy is to optimize the algorithm to be more directional. We removed the migration-foraging strategy, which is more suitable for combining with the compact scheme. Finally, we tested the improved algorithm on the cec2013 test set, which showed good performance.

The meal management APP created in the context of community senior restaurants aims to support seniors in eating healthily while enhancing the management effectiveness and management of community senior restaurants. This study used a combination of SHERPA and FMEA to test the usability of the APP client in order to further improve its age-appropriateness. Inviting participants to sequentially complete the hierarchical tasks and classify the experimentally produced error steps. Following the calculation of risk priority values based on experimental data, identification of failure modes necessitating design modifications, and validation of the findings. Experimental results show that the improved scheme is more in line with how older people think and behave, with a noticeably lower error frequency in the test.

Traditional statistical scan cleaning methods usually make assumptions about the scanned surfaces or noise model, which requires users to manually adjust the settings. The learning-based method needs a data set for training, and the denoising effect of objects outside the data set is general. A self-learning directed point cloud denoising algorithm has been proposed. By introducing the self-learning method without pre training, this method makes denoising and gridding promote each other, and achieves good denoising effect. Our method does not require pretraining or preset parameters and has a good denoising effect on various noises.

Reconstructing surfaces and textures simultaneously from multi-view images becomes difficult, because of the inherent flaws of the traditional multi-view 3D reconstruction pipeline and 3D representations. Realistic texture models commonly use texture mapping post-processing to generate texture. This approach requires extra memory and time consumption, while it is inefficient and difficult to compatible with deep learning techniques. This paper present NIST that is a novel neural implicit surface and texture multi-view reconstruction method, which can reconstruct meticulous surface and high-fidelity texture details from multi-view images. In NIST, multi-view pixel features are considered as wavelength features of different viewpoints, which can improve the surface details learned during volume rendering. Furthermore, we design texture loss to learn textures while learning surfaces. Experiments show that NIST outperforms baseline methods on the DTU dataset and the BlendedMVS dataset in both surface and texture reconstruction.

The technology of software-defined changing all areas of life, but the equipment storage system in the field of application is still in its infancy. At present, the traditional equipment warehousing scheduling system is difficult to meet the new needs of rapid scheduling and flexible maintenance of large quantities of equipment. Based on the development and application characteristics of software definition technology, this paper integrates the traditional equipment storage scheduling system and presents the research on the architecture design technology of equipment storage scheduling system under software definition technology. This paper clarifies the key application technologies of the system, realizes the design of new open system architecture based on software-defined technology and the optimization design of system center service platform architecture, and improves the architecture design ability of automation, configuration ability, and easy maintenance of the existing equipment storage scheduling system. It solves the new needs of rapid deployment, intelligent upgrade, flexible configuration, and convenient service of large quantities of equipment warehouse scheduling system.

In response to the problem that the traditional multi-objective firefly algorithm has insufficient exploration capability, poor convergence and easy to fall into local optimum, this paper proposes a multi-objective firefly algorithm for hierarchical mutation learning (MOFA-HML). Firstly, the population is stratified by non-dominated sorting of sequential search strategy (ENS-SS), so that the dominated solution in the latter layer learns from the individuals in the former layer to ensure fast and accurate convergence of the population, and the differential evolution operation is performed on the non-dominated individuals in the population, and the distribution space of the non-dominated solutions is more extensive to improve the exploration ability of the algorithm; the mutation operation is performed on the population to guide the local development of the algorithm and improve the solution accuracy of the algorithm; finally, by the inter-individual Euclidean distance to screen firefly individuals and maintain the distributivity of the population. On 12 test problems of ZDT and UF series, MOFA-HML is compared with 5 classical algorithms and 7 recent algorithms, and the results show that MOFA-HML has excellent exploration ability, good convergence and distributivity of solutions.

The rapid growth of e-commerce provides an excellent opportunity for the logistics and warehousing industry. With Automated Guided Vehicles (AGVs) widely used in warehouses, Unmanned Warehouse Management (UWM) has received great attention. However, UWM faces many challenges: (1) the problems of imbalanced commodity division; (2) the unreasonable layout of the warehouse working area coordination. To deal with these challenges, we design a dynamic unmanned warehouse partition (DUWP) model, consisting of geographical static matching and dynamic division of commodity volume to comprehensively alleviate the problem of imbalanced unmanned warehouse partition. The former optimizes partition by distance nearest principle for position matching. The latter performs secondary optimization by information entropy maximization theory. Experiments on real-world unmanned warehouse datasets verify the effectiveness, accuracy, and feasibility of the proposed DUWP model.

The common forms of manifold data are curvilinear, spiral, and circular. Density peak clustering algorithm (DPC) is difficult to find the cluster center accurately when facing manifold dataset, and it is easy to assign the samples belonging to the same cluster to other cluster centers which are closer to it, resulting in poor clustering accuracy. To this end, Density Peak Clustering Algorithm for Manifold Data Based on Geodesic Distance and Weighted Nearest Neighbor Similarity (DPC-GWNN) is provided in this paper. The DPC-GWNN algorithm firstly combines the weighted K-nearest neighbors to redefine the local density in order to find the clustering center more accurately; secondly, the geodesic distance is used to calculate the sample spacing instead of the Euclidean distance, so that the samples on the same manifold cluster are closer to each other and the samples between different manifold clusters are farther apart; finally, the shared nearest neighbor Finally, the new inter-sample similarity is defined using the shared nearest neighbor and the natural nearest neighbor weighted similarity to fully integrate the local information of samples and avoid the cascading effect of errors, thus achieving a better clustering effect. In this paper, we compare the DPC-GWNN algorithm with other algorithms and find that the DPC-GWNN algorithm can accurately identify the manifold class clusters and find the cluster center to achieve accurate clustering.

Optimizing the layout of Nucleic Acid Test Sites (NATS) is a primary issue for the blocking-up of COVID-19. In this paper, we propose an optimization model to address this issue with Gannet Optimization Algorithm (GOA) under multiple constraints. The experimental results show that the GOA-based model performed well in searching optima. It is also revealed that optional solutions with different amounts of NATS can be found, which will provide multiple references for decision-makers in planning the layout of NATS.

In this study, 74 college student volunteers were recruited as experimental participants, and 108 game 3D images of the Arena of Valor game were used as the selected samples. The digital avatar is presented to the subject through AR technology. Then, the participants were asked to choose five digital avatar that they would probably go on to use in the future metaverse. Finally, after excluding the effects of game familiarity and other socio-statistical variables, it was found that the gender of the selected Digital avatar was positively correlated with the actual participants’ gender, and it was further found that females were more gender specific and more aggregated in terms of Gender Expression than males, while males were less gender specific and more dispersed. This study found that although there was certain influence of historical culture on the selection aspect of the digital avatar, it was not significant. Local game characters and Japanese game characters have equally important influence.

With the development of UAV ad hoc network and networking, various attacks in network environment pose the more and more increasing threats to UAV cluster system. To solve this problem, this paper selects a forgery attack as the research object and proposes an ant colony algorithm based on octree to carry out the path planning task of a UAV cluster in a complex environment. Firstly, the attack detection algorithm based on differential constraint and the processing method of converting the attacked UAV into static obstacles are proposed, so that the UAV cluster can detect and process forgery attacks in real time; secondly, on this basis, the UAV cluster defense model is established, and the algorithm based on octree is proposed to improve the efficiency of solving the model. The simulation results show that the Oc-ACO algorithm can solve the problem of UAV cluster encountering forgery attack safely and quickly.

The emergence of the Internet of Things (IoT) is gradually changing people’s lives and is widely used in various fields. Wireless body area networks (WBAN) and vehicular ad-hoc networks (VANET) are typical examples of IoT applications. In both environments, an attacker can tamper, intercept, and eavesdrop on data transmitted in the public channel. Therefore, it is necessary to design authentication and key agreement (AKA) protocols to protect the communication of entities. Recently, Wu et al. proposed a novel AKA protocol in WBAN. Jagriti et al. proposed an anonymous AKA protocol in VANET. In this paper, we analyse of Wu et al.’s protocol and Jagriti et al.’s protocol, respectively. Unfortunately, we find that Wu et al.’s protocol cannot resist sensor node capture attacks, and Jagriti et al.’s protocol cannot resist session key disclosure attacks, on board unit (OBU) capture attacks, and man-in-the-middle attacks. Finally, we propose some suggestions for the two protocols.

With the rapid development of domestic expressway ETC system, the trend of intelligent and digital for expressway management began to mature, which provides a solid foundation for the fully connected vehicle–road-cloud intelligent perception and collaborative decision-making system. At present, as one of the largest Internet of Vehicles (IOV) in the world, ETC system can provides multi-dimensional data for expressway and covers the vehicle information of each road sections. Among them, the traffic congestion identification plays an important role for vehicle collaborative decision-making and it has tremendous research value. In order to improve the accuracy and stability of traffic congestion identification, this paper proposes a Fuzzy Comprehensive Evaluation Adaptive Matching Algorithm (FACM) by deeply mining the dimensional information of expressway ETC transaction data. This method introduces the Section Portrait into dimensional analysis, and uses Analytic Hierarchy Process (AHP) to weighted average the dimensions of each section, combined with Fuzzy Comprehensive Evaluation (FCE), the level division of section congestion is carried out. The experimental results show that the average congestion recognition accuracy of FCAM is 98.03%, which is 4.98% and 3.07% higher than FCE method and K-means method respectively. The proposed method has high stability and high recognition rate.

Due to the epidemic, it is difficult for people to go out. As a result, telemedicine systems are widely used, making it possible for people to see a doctor without leaving home. However, one of the essential issues facing online medical diagnosis is whether the patient’s privacy can be protected. Seno et al. proposed that mutual authentication between the patient and the medical server is required when using a telemedicine system to address this issue. Nevertheless, Seno et al. did not consider comprehensively in the authentication phase when designing this protocol, which led to the security protocol still being flawed. Another critical aspect of the telemedicine system is that the real-time nature of the network must be guaranteed. Due to the increase of various modern wireless applications, it is necessary to manage a large amount of traffic on the server. The operation speed may be inefficient if only a single server is used for data processing. Hence, Kumar et al. propose a protocol for wireless applications in the multi-server environment. In this paper, we analyze the security of the protocols proposed by Seno et al. and Kumar et al. We found that both protocols have some security vulnerabilities, including privileged insider attacks.

The concepts of Digital Twin let the evolution of new energy services and more decentralized business models where people and energy industries are becoming essential participants in donating to smart grid sustainability dreams. In order to provide secure communication in a digital twin-enabled smart grid platform, we propose a lightweight authentication scheme. In our work, each smart meter and its related digital twin entity will calculate a shared session key for further use. The proposed scheme is indeed secure and efficient because we delivered a detailed security analysis and performance evaluation. Compared with other works, the proposed scheme is more suitable for creating the future smart grid industry.

With the rapid growth of the smart home, remote control of devices within a smart home environment has become a primary and essential function. However, there are significant security vulnerabilities in the process of remote control. This paper proposes a secure authentication scheme based on a trusted execution environment for smart home remote control. The proposed scheme generates a session key between smart home devices and users. This session key ensures that a user can securely control devices remotely and resist various well-known attacks. We also utilize the Real-or-Random model to demonstrate our scheme is provably secure. Besides, our work has lower computation and communication costs than other related methods.

Wireless sensor network (WSN) is a self-organizing network composed of distributed sensor nodes. Its appearance has dramatically changed people’s lives and is now widely used in various fields. Due to the openness of wireless channel, malicious attackers can eavesdrop, intercept or tamper with data in the channel. In the communication process, the user’s privacy is easy to leak, which leads to the user’s inability to communicate securely with the sensor node. Hence, it is necessary to design secure authentication and key agreement (AKA) protocols to enhance the communication security of users in the WSN environment. Recently, Jawad et al. proposed an anonymous three-factor authentication protocol based on symmetric encryption in WSN. Polai et al. proposed an authentication protocol using lightweight primitives in wireless body area networks. In this paper, we analyze Jawad et al.’s and Polai et al.’s protocol and find that both protocols have security vulnerabilities. We prove their protocols cannot resist sensor node capture attacks, known temporary information disclosure attacks, and hub node stolen database attacks. Finally, we put forward suggestions for the improvement of these two protocols.

In wireless sensor network (WSN), various data is collected via sensors and transmitted over a public channel. However, during the transmission, an attacker can intercept and eavesdrop on data and use it to launch some attacks. Therefore, it is necessary to design secure authentication and key agreement (AKA) protocols in WSN environments, which ensures secure communication between entities. Recently, Yu et al. designed an authentication protocol in WSN and claimed their proposed protocol can resist well-known attacks. In addition, Wang et al. proposed another three-party mutual authentication protocol in IoT-enabled wireless sensor networks. In this paper, we find some security vulnerabilities in both protocols, including sensor node capture attacks, known session-specific temporary information attacks, and violating perfect forward secrecy. Finally, we introduce several suggestions for the improvement of both protocols.

In this paper, a new YSK neural network is proposed to detect face mask wearing to prevent COVID-19 or other infectious diseases. And the aim is not only to detect the face mask wearing person, but also can detect, track and warn the not-wearing face mask person. The technique we applied is referred to as object detection based on deep learning. Several experiments were made to test the performance of the proposed model and it showed that it has better performance than other common detection models and the result is excellent.