Addressing the challenges inherent in passive unmanned search and rescue missions at sea,including difficulties in target identification,broad search areas,and slow route planning,a strategic process was introduced for maritime search and rescue area planning and a route planning model specifically designed for passive unmanned missions.By thoroughly understanding the emergency response operations at sea and the specific needs for route planning,an optimal routing model have been developed considering factors such as the efficiency of search and rescue area coverage and the cost of rescue routes.The objective function is constructed within these constraints and solved using the whale optimization algorithm.The validity of the model is confirmed through designated scenario experiments,indicating that our proposed model for maritime passive unmanned search and rescue route planning is capable of swiftly identifying the search and rescue area and efficiently discovering a route with reduced costs.
To address the problem of low cache hit ratio in edge nodes for privacy-preserving in the Internet of Vehicles(IoV),a deep deterministic policy gradient caching(DDPGC)method was proposed.Firstly,a taxi certified by a trusted authority acted as a second-level caching edge node to acquire hotspot data and store it in the local cache.It then broadcasted this information to the neighboring service requesting vehicles(SRV).SRVs cached the broadcasted data locally and search for service requests in the order of priority of local cache,taxi,and cloud server when such requests arise.Secondly,a neural network was deployed in taxis and SRV to maximize the caching benefit through deep reinforcement learning for decision replacement of their cached data.Finally,when SRV were located in vehicle sparsity and could not obtain request data from neighboring vehicles,a combination of k-anonymity and random response perturbation mechanisms generated anonymity sets to send requests to cloud servers in an anonymous manner to obtain services while protecting user location privacy.Simulation experimental results show that DDPGC can effectively improve the vehicle cache hit ratio,reduce the frequency of SRV interaction with the cloud server,and effectively protect user privacy security.
Aiming at the problems of traditional image manipulation detection methods,such as fuzzy boundaries,single scale of extracted features,and ignoring background information,this paper proposes an image manipulation detection method based on multi-scale context-aware and boundary-guided.First,spatial details and base features of manipulated images are extracted using an improved pyramid vision transformer.Second,information related to the edge of the falsified region is explored by an edge context-aware module to generate an edge prediction map.Again,the edge guidance module is utilized to highlight the key channels in the extracted features and reduce the interference of redundant channels.Then,the rich contextual information of the manipulated region is learned from multiple sensory fields through the multi-scale context-aware module.Finally,the feature fusion module is utilized to accurately segment the manipulated region by focusing alternately on the foreground and background of the manipulated images.Comparing this paper's method quantitatively and qualitatively on five commonly used public image manipulation detection datasets,the experimental results show that this paper's method can effectively detect manipulated regions and outperforms other methods.
Aiming at the limitations of single-dimensional car-following model to describe vehicle car-following behavior in local multi-vehicle environment,the mechanism of vehicles in adjacent lane influencing the subject vehicle car-following behavior is explored,and a vehicle car-following model more suitable for local multi-vehicle environment is attempted to be established.The driving behavior variable reflecting the influence of vehicles in adjacent lane was determined by correlation analysis.Vortisch indicator of similarity(VIS)was used to characterize the influence of vehicles in adjacent lane on car-following behavior.Chi-square independent test and kernel density curve were used to determine the VIS demarcation threshold reflecting whether the influence is significant.Recursive feature elimination was used to screen the variables related to car-following samples significantly affected by vehicles in adjacent lane.The influence mechanism of variables was determined according to the statistical analysis results.Based on the mechanism proposed,the car-following model suitable for local multi-vehicle environment was constructed and its prediction effect was evaluated.Results show that VIS between the speed of subject vehicle and following vehicle in adjacent lane can characterize the influence and the VIS threshold is 0.668.It is concluded that the attention mechanism and memory effect can explainthe influence mechanism of car-following behavior in multi-vehicle environment.Considering the attention mechanism and memoryeffect,RMSE decrease by 65%in full velocity difference model(FVD)model and 62%in intelligent driver model(IDM)model,MAE decrease by 65%and 59%and R2 increased by 180%and 288%respectively,which proved the rationality of the attention mechanism and memory effect in explaining the car following behavior of subject vehicle in local multi-vehicle environment.
Traffic risk is an important source of road traffic accidents,and effective prediction of road accident risk propagation after an accident is of great significance to prevent the occurrence of secondary accidents.Therefore,this paper establishes a road accident risk propagation model based on macroscopic traffic flow energy consumption.Firstly,the influence of lane occupancy and lane change on traffic flow after accident is analyzed,and then a macro-traffic flow model is constructed under the effect of post-accident risk.Secondly,combined with the definition of traffic flow energy dissipation,the whole vehicle movement is abstracted as fluid movement process,and the traffic flow energy consumption model under the action of post-accident risk is proposed to quantify the accident risk.Finally,according to the temporal and spatial correlation of risk transmission,a calculation model of risk influence range and duration is proposed to describe the process of risk transmission.In order to verify the effectiveness of the risk propagation model proposed in this paper,numerical simulation and simulation verification of traffic flow operation under different initial traffic flow densities were carried out using MATLAB and VISSIM respectively.The experimental results show that:the greater the initial density of the road section when the accident occurs,the wider the impact range of the risk and the longer the duration.The absolute value of the prediction deviation rate of this model is less than 8%,which has good prediction effect.
