Fault diagnosis for rolling elements in rotating machinery persistently receives high research interest due to the said machinery's prevalence in a broad range of applications. State-of-the-art methods in such setups focus on effective identification of faults that usually involve a single component while rejecting noise from limited sources. This paper studies the data-based diagnosis of mixed faults coming from multiple components with an emphasis on model robustness against a wide spectrum of external perturbation. A dataset is collected on a rotor and bearing system by varying the levels and types of faults in both the rotor and bearing, which results in 48 machine health conditions. A duplet classifier is developed by combining two 1-D convolutional neural networks (CNNs) that are responsible for the diagnosis of rotor and bearing faults, respectively. Experimental results show that the proposed classifier can reliably identify the onset and nature of mixed faults. In addition, one-vs-all classifiers are built using the features generated by the developed 1-D CNNs as predictors to recognize previously unlearned fault types. The effectiveness of such classifiers is demonstrated using data collected from four new fault types. Finally, the robustness and ability to reject external perturbation of the duplet classification model are analyzed using kernel density estimation (KDE).

In modern urban areas, inefficiency traffic management is one of the main causes of road congestion, leading to reduced fuel efficiency and increased traffic safety hazards. Traditional researches typically focus only on enhancing the throughput of intersections by optimizing traffic signals or individual vehicle trajectories. However, these methods often overlook the dynamic nature of the traffic system and the potential benefits of vehicle platooning, limiting their effectiveness in complex traffic environments. Addressing this challenge, this article presents MARP, a Cooperative Multiagent deep reinforcement learning (DRL) System for connected autonomous vehicle (CAV) Platooning. Utilizing vehicle to vehicle (V2I) and vehicles to infrastructure (V2V) technologies, MARP integrates sensing, computing, and communication to collect and process real-time data on traffic conditions, thereby achieving dynamic synchronization between traffic signal controllers and CAV platoons. By constructing platoons that collaborates with the infrastructure through a multiagent DRL collaboration model, MARP adapts to real-time traffic flow changes, significantly optimizing the fluidity and efficiency of the entire traffic network. Detailed experiments show that MARP effectively reduces traffic congestion, shortens intersection travel times, and cuts fuel consumption and emissions, surpassing the state-of-the-art approach.

Modern smart railroad management relies heavily on Internet of Things (IoT)-enabled sensors to monitor train performance, resulting in the generation of extensive data streams. The ensuing data influx poses critical challenges in storage, processing, and transmission. This paper presents a novel data compression method specifically designed for IoT-generated data within railroad management scenarios. Leveraging deep reinforcement learning (DRL), our approach intelligently compresses data from onboard IoT sensors. This not only streamlines data streams, ensuring essential information is retained and redundant data is pruned, but also alleviates strain on resource-limited devices such as edge servers tasked with complex computations or aggregating large datasets for long-term trend analysis. Experimental results demonstrate that our approach can outperform current baselines with achieving an enhancement of more than 18% on compression rate in real-time configurations. This signifies a transformative solution for handling IoT-induced big data in contemporary railroad management systems.

To address the coupled fault diagnosis challenge between temperature sensors and equipment in traction converter cooling systems, this paper proposes a dual-level intelligent diagnostic architecture. This method achieves online sensor fault isolation and early equipment anomaly warning by leveraging spatiotemporal correlation modeling of multimodal sensor data and ensemble learning-based prediction. At the first level, it integrates multi-source parameters such as outlet temperature and pressure to establish dynamic prediction models, which are combined with adaptive threshold mechanisms for detecting various sensor faults including offset, open-circuit, and noise interference. At the second level, it monitors the status of temperature sensors through time-series analysis of inlet temperature data. Verified on an edge computing platform, the proposed method effectively resolves the coupling misdiagnosis between sensor distortion and equipment faults while maintaining physical interpretability, thereby significantly enhancing diagnostic robustness under complex operating conditions.

Video data has emerged as a primary source of information input in contemporary CIoT systems, significantly driving its development. However, due to the diversity of video capture devices, videos exhibit significant heterogeneity in various aspects, such as color, texture, and lighting conditions, posing challenges for video manipulation and analysis. Moreover, different information processing terminals have limited requirements for data types, which has led to a demand for the translation of heterogeneous videos. In this paper, we propose a novel method named Structure and Motion Consistency Network (SMCN). It updates and optimizes the model from the feature level, making it more efficient at extracting invariant spatiotemporal information from different types of video data. Specifically, it fuses the structure information, a.k.a. mean and standard deviation of features at each spatial position across channels, then re-injects it to refine the spatial consistency, and maximizes motion mutual information of features from adjacent frames to improve the temporal consistency of intermediate features. We conducted experiments on the common video translation dataset Viper and the infrared-to-visible video translation dataset IRVI. Extensive experiments indicate our SMCN outperforms the state-of-the-art methods and the lightweight module can be easily applied to other models in a plug-and-play manner, showing significant advantages in addressing the problem of heterogeneous video data transformation.