As a result of long‐term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R‐CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang‐Taiyuan high‐speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.

Photovoltaic (PV) power prediction plays a significant role in supporting the stable operation and resource scheduling of integrated energy systems. However, the randomness and volatility of photovoltaic power generation will greatly affect the prediction accuracy. Focusing on this issue, a prediction framework is proposed in this research by developing an improved sparrow search algorithm (ISSA) to optimize the hyperparameters of long short-term memory (LSTM) neural networks. The ISSA is specially designed from the following three aspects to support a powerful search performance. Firstly, the initial population variety is enriched by using an enhanced sine chaotic mapping. Secondly, the relative position of neighboring producers is introduced to improve the producer position-updating strategy to enhance the global search capabilities. Then the Cauchy–Gaussian variation is utilized to help avoid the local optimal solution. Numerical experiments on 20 test functions indicate that ISSA could identify the optimal solution with better precision compared to SSA and PSO algorithms. Furthermore, a comparative study of PV power prediction methods is provided. The ISSA-LSTM algorithm developed in this paper and five benchmark models are implemented on a real dataset gathered from the Alice Springs area in Australia. In contrast to the SSA-LSTM model, most MAE, MAPE, and RMSE values of the proposed model are reduced by 20∼60%, demonstrating the superiority of the proposed model under various weather conditions and typical seasons.

Pedestrian detection is an important problem in video surveillance. While pedestrians often have diverse postures and mutual occlusion which make the detection quite difficult, their head-shoulder portions are relatively stable. Thus we choose to use head-shoulder outline features of a pedestrian for detecting. First, we apply a hierarchical classification method using Haar features and HOG features to head-shoulder location detection. Second, we define a combined feature named Joint HOG based on the symmetry of head-shoulder portion. Third, we filter out most negative samples by using the Haar classifier. Finally, we execute an elaborate HOG verification and thus obtain the head-shoulder target box expected. Experimental results show that our method achieved a real-time processing accuracy rate of nearly 90%, arguing that it is applicable to people counting and video surveillance in library.

The resource allocation problem in Multi-access Edge Computing (MEC) has been widely studied to maximize its operation efficiency under limited resource constraint. However, the existing literatures overlooked the setup cost and the associated dynamic operations. In this work, we consider server configuration and overload in the multi-server scenario where servers are switched on/off depending on the network environment. A novel pricing mechanism maximizing the utility of base station (BS) monitoring multiple servers is proposed, which jointly optimizes the setup cost and server load. We aim to maximize the BS utility under one-day task requests, and divide the time into off-peak and peak periods based on task requests. In the off-peak period, we flexibly switch on/off servers for BS to reduce setup costs. In the peak period, to avoid overloading, we introduce crowdsourcing where servers as agents purchase idle resources from private users (PUs) for mobile users (MUs) and minimize MUs’ cost by a contract-based knapsack algorithm. Lastly, a pricing mechanism is proposed to solve the BS utility maximization problem with an exploratory Upper Confidence Bound (UCB)-based algorithm adjusting server prices dynamically. Simulation results show that the proposed algorithm is superior to others in minimizing MUs cost and maximizing BS utility.

Hydrogen energy plays an increasingly vital role in global energy transformation. However, existing electric–hydrogen coupled integrated energy systems (IESs) face two main challenges: achieving stable operation when integrated with large-scale networks and integrating optimal dispatching code with physical systems. This paper conducted comprehensive modeling, optimization and joint simulation verification of the above IES. Firstly, a low-carbon economic dispatching model of an electric–hydrogen coupled IES considering carbon capture power plants is established at the optimization layer. Secondly, by organizing and selecting representative data in the optimal dispatch model, an electric–hydrogen coupled IES planning model considering the integration of wind, photovoltaic (PV), diesel and storage is constructed at the physical layer. The proposed electric–hydrogen coupling model mainly consists of the following components: an alkaline electrolyzer, a high-pressure hydrogen storage tank with a compressor and a proton exchange membrane fuel cell. The IES model proposed in this paper achieved the integration of optimal dispatching mode with physical systems. The system can maintain stable control and operation despite unpredictable changes in renewable energy sources, showing strong resilience and reliability. This electric–hydrogen coupling model also can integrate with large-scale IES for stable joint operation, enhancing renewable energy utilization and absorption of PV and wind power. Co-simulation verification showed that the optimized model has achieved a 29.42% reduction in total system cost and an 83.66% decrease in carbon emissions. Meanwhile, the simulation model proved that the system’s total harmonic distortion rate is controlled below 3% in both grid-connected and islanded modes, indicating good power quality.

The decrease of the residual direct current (DC) voltage (V_rdc) of the anti-parallel liquid crystal (LC) cell using silver (Ag)-doped Polyimide (Ag-d-PI) alignment layers is presented in this manuscript. A series of Ag/PI composite thin layers are prepared by spurting or doping PI thin layers with Ag nano-particles, and Ag/PI composite thin layers are highly transparent and resistive. LC are homogeneously aligned between 2.0 mg/mL Ag-d-PI alignment layers, and the V_rdc of the cell that assembled with Ag-d-PI alignment layers decreases about 82%. The decrease of V_rdc is attributed to the trapping and neutralizing of mobile ions by Ag nano-particles. Regardless of the effect of Ag nano-particles on the conductivity of Ag-d-PI alignment layers, the voltage holding ratio (VHR) of the cells is maintained surprisingly. The experiment results reveal a simple design for a low V_rdc LC cell.