Virtual coupling is a novel railway transport concept that allows trains to split and join on-the-fly by switching from mechanical to virtual couplers. One of the main challenges in applying virtual coupling in metro railways is to reduce the tracking distance between trains without compromising safety. This paper proposes a relative operation-based train separation model to reduce the safe distance between trains. This model applies a fault tolerance principle. The principle is that the preceding train normally operates for a time interval from its last-known state before initiating an emergency brake to stop the train. A difficulty in applying the proposed model is to predict the boundary of all possible time-position trajectories of the preceding train, which is the reachability problem of a hybrid system. To solve this problem, we formalise the operation of the preceding train by a parameterized hybrid automaton. A polytope-based algorithm is then developed for computing an over-approximated reachable set of the automaton. We compare our approach with a state-of-the-art relative braking distance-based train separation model for virtual coupling on a concrete metro line in Chengdu, China, and evaluate the method with several benchmarks. The results demonstrate that the relative operation-based model substantially reduces the safe distances between trains. Compared to conventional approaches, the proposed model provides a considerable 90.7% decrease in unnecessary waiting time at railway stations for virtually coupled trains and a 4.9% increase in the capacity of the given railway lines.

Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.

Evacuation simulations in virtual indoor fire scenes hold great significance for public safety. However, existing evacuation simulation methods are inefficient and provide poor visualized when applied to virtual reality (VR) simulations. Additionally, the influences of the interaction of evacuation processes on the choice of multiple exits have not been fully considered. In the paper, we propose a VR simulation method for crowd evacuation in a multiexit indoor fire environment. An indoor 3D scene model and character model, for studying the environmental factors that affect the multiexit selection of personnel during the fire process, are combined with environmental factors to enhance the evacuation route planning algorithm to improve the efficiency of the VR simulation of evacuation in the scene. In addition, a prototype system that supports multiple experience modes is proposed, and case experiment analyses are performed. The results show that the method described in this paper can effectively support the real-time simulation of indoor fire evacuations in virtual scenes, providing both reliable simulation results and good visualization effects.

Correlation filter (CF) is widely used in unmanned aerial vehicle (UAV) tracking because of its efficient performance. However, due to the existence of edge effects, CF will be confused so that the peak of the response is no longer obvious, resulting in tracking drift and template degradation, thereby degrading CF performance. To handle the problem, we propose a new CF for this problem. First, we introduce a response-weighted background residual term to make CF learn the background in a targeted manner according to the strength of the response. Secondly, a history filter model is constructed and a spatio-temporal regularization term is introduced to improve the robustness of CF. Finally, we conduct experiments on two challenging UAV benchmarks, DTB70 and UAV123_10fps. The results show that our tracker achieves state-of-the-art performance compared with 15 other SOTA trackers, and can run at 58 FPS on a single CPU, meeting the needs of UAV real-time tracking.

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R² was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R² value was 0.906. (3) Shapley analysis showed PM₁₀, humidity and NO₂ were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction.

To capture local complex shape information and fine-grained features from irregular point cloud, this paper proposes a novel local feature encoder-based network named context-awareness (CA)-Net which is used to solve the challenge of 3D object classification and segmentation. The core of the CA-Net is CA-Encoder, which is based on CA and cross-channel multi-head self-attention (CC-MSA). CA-Encoder uses contextual information awareness to aggregate local features from two levels: point cloud 3D coordinate information and high-dimensional implicitly encoded information, leveraging CC-MSA to learn channel-related information. For different point cloud benchmark tasks, CA-Net uses the local feature enhancement module (classification) and the Up-Transformer (segmentation) which includes a cascaded set of CA-Encoders to solve the problem of feature loss at non-edge points in edge sampling, so that the sampled results can both preserve the shape of the point cloud edges and reconstruct the full internal shape structure of the point cloud. The CA-Net has superior performance in experiments on ModelNet and ShapeNetPart datasets with a classification accuracy of 93.8% and a segmentation accuracy of 85.9%.