Electric vehicles (EVs) are viewed as an attractive option to reduce carbon emission and fuel consumption, but the popularization of EVs has been hindered by the cruising range limitation and the inconvenient charging process. In public charging stations, EVs usually spend a lot of time on queuing especially during peak hours of charging. Therefore, building an effective charging planning system has become a crucial task to reduce the total charging time for EVs. In this paper, we first introduce EVs charging scheduling problem and prove the NP-hardness of the problem. Then, we formalize the scheduling problem of EV charging as a Markov Decision Process and propose deep reinforcement learning algorithms to address it. The objective of the proposed algorithms is to minimize the total charging time of EVs and maximal reduction in the origin-destination distance. Finally, we experiment on real-world data and compare with two baseline algorithms to demonstrate the effectiveness of our approach. It shows that the proposed algorithms can significantly reduce the charging time of EVs compared to EST and NNCR algorithms.

In terms of preventing traffic accidents, improving traffic efficiency, and ensuring personal safety, the research on vehicular ad hoc networks (VANETs) is of great significance. Message authentication is an important security foundation for VANETs. With the rapid growth of the number of access terminals, the existing computing power of the VANETs will not be able to meet the fast message verification service load of large-scale dynamic networks. This paper proposes a novel distributed collaborative authentication method. By selecting a reasonable number of assistance verification terminals in the VANETs system and cooperating with roadside unit (RSU) to jointly undertake the task of network message verification, the purpose is to reduce the verification delay and achieve fast message verification. The simulation results show that in a large-scale connected vehicle with a large number of system terminals, the system message verification delay of our scheme is shortened to one tenth of the centralized verification system message verification delay.

As microwave device designs become increasingly complex, traditional full-wave electromagnetic (EM) simulations and manual parameter tuning have become time-consuming and computationally prohibitive. This work introduces an intelligent framework for the automated inverse design of passive microwave components, using substrate-integrated waveguide (SIW) filters as a validation platform. Our approach establishes a versatile parametric model that classifies structural features into fundamental geometric and complex cross-coupling parameters. This structured design space is explored by a deep reinforcement learning (DRL) agent, which is guided by a fast and accurate $S$ -parameter prediction model based on a multihead attention-enhanced convolutional neural network (CNN). The multihead attention-enhanced CNN excels in performance prediction and generalization, while the DRL agent adaptively generates optimal structural parameters. We experimentally demonstrate that our framework achieves high-fidelity performance prediction and provides a novel, highly efficient pathway for the automated synthesis and optimization of advanced microwave systems.

As the core component of many large machinery, rotating equipment occupies a high proportion in industrial manufacturing. Intelligent fault diagnosis for them is becoming the core focus of Industry 4.0 and its evolution. To address the problem of poor fault classification accuracy of rotating equipment, this paper proposes a new solution to deploy a composite fault diagnosis model at the front end to achieve low power consumption, continuous automatic identification and monitoring of equipment faults. We creatively combined the idea of speech recognition to solve the problem that traditional feature extraction methods are insufficient in handling composite fault diagnosis when dealing with fault waveform files, and used Fbank speech feature extraction idea to extract fault features, thus simplifying the complex fault diagnosis problem into an image classification problem. The generated composite fault Fbank feature data set is significantly different in visual observation. Therefore, we further designed the LeNet-F network based on the LeNet network for composite fault diagnosis. The model was validated on the K210 development board to achieve an accuracy of 97.41% for hybrid fault classification at a model size of 2.4MB. This makes it possible to apply the deep learning method to the front end to directly complete the composite fault diagnosis.

In recent years, with the rapid deployment of various Internet of Things (IoT) devices, it becomes a crucial and practical challenge to enable real-time search for objects, data, and services in the Internet of Everything. The IoT data prediction model can not only provide solutions for the real-time acquisition of the IoT sensor data but also provide more meaningful applications than the traditional IoT event detection model. In this paper, we use the complex time series formed by various types of sensors to establish a multi-dimensional feature selection model and a dynamic sensor-data prediction model. Compared with the traditional data prediction model, our model improves the accuracy and stability of the long-term prediction results of the IoT sensor data. Finally, we evaluate our prediction model using the Intel Berkeley Research Lab sensor data with an accuracy of over 98% and 92% accuracy on the Chicago Park District weather&water data.

The coexistence of multiple numerologies can cause interference and degrade system performance in 5G devices. In this letter, a convolutional neural networks (CNN)-based mixed numerology interference (MNI) recognition approach is devised to resolve the interference for user equipment (UE) to receive new radio (NR) downlink signal. The results are learned from the frequency domain magnitude and phase waveform, for the best precision and efficiency. We use as little sample data as possible to reduce memory/buffer costs and to alleviate the computing resources. Simulations are performed to verify the viability on various signal-noise ratio (SNR), channel condition, modulation, etc. It is shown that the accuracy can reach 97% or higher at varying SNR and fading channels. Furthermore, a dynamic filtering/windowing (DFW) scheme is proposed. The link level simulation results show that the scheme can improve the attainable Signal to Interference plus Noise Ratio (SINR) by 3~6dB for the indicated cases.