Considering various fault states under severe working conditions, the comprehensive feature extraction from the raw vibration signal is still a challenge for the diagnosis task of rolling bearing. To deal with strong coupling and high nonlinearity of...
Reconstructing continuous deflection and stress fields from sparse deflection measurements is challenging because the two responses have different spatial distributions and stress depends on deflection curvature. Limited experimental data further con...
Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accu...
This work concerns the autonomous navigation of non-holonomic ground mobile robots in a GPS-denied environment. The objective was to locate, in a global frame, without GPS, anonymous ground mobile robots starting from two consecutive aerial images ca...
In disaster emergency response, spatial location information embedded within social media texts holds substantial value for the rapid localization of affected areas and the implementation of precise rescue operations. Existing research predominantly...
Wi-Fi-based human activity recognition has attracted broad attention for its advantages, which include being device-free, privacy-protected, unaffected by light, etc. Owing to the development of artificial intelligence techniques, existing methods ha...
Advances in machine-learning techniques can serve practical water management needs such as salinity level estimation. This study explores machine learning, particularly deep-learning techniques in developing computer emulators for a commonly used pro...
Side-scan sonar (SSS) target recognition is an important part of building an underwater detection system and ensuring a high-precision perception of underwater information. In this paper, a novel multi-channel multi-location attention mechanism is pr...
Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In th...
Reliable short-term prediction of global horizontal irradiance (GHI) is essential for maximizing photovoltaic (PV) power generation, improving grid reliability, and enabling efficient integration of renewable energy sources. This study proposes Lite-...
This study investigates the multi-locality and multi-temporal characteristics of mobility destinations in Zanjan, Iran, throughout a typical day. Existing approaches often overlook critical geographical concepts, including the influence of multiple m...
This study examines the relationship between self-leadership strategies and occupational well-being and whether psychological safety has moderated these relationships in the context of enforced remote work caused by COVID-19. Altogether, 2493 higher...
Reliable facial expression recognition (FER) is essential for human–computer interaction across scenarios. However, existing methods often overlook spatial structure when seeking region-specific features due to fixed or limited local regions, l...
In computer vision, ethnicity classification tasks utilize images containing human faces to extract ethnicity labels. Ethnicity is one of the soft biometric feature categories useful in data analysis for commercial, public, and health sectors. Ethnic...
It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor...
Wearable inertial sensors produce multi-axis motion signals with rich spatial and temporal structure. Existing deep-learning pipelines for human activity recognition (HAR) rarely tackle three issues jointly: explicit modeling of the body-part groupin...
Data-driven recognition of machining conditions in CNC milling processes represents a significant research focus within process monitoring applications. This work assesses whether physics-informed, spindle-synchronous vibration representations remain...
Soil health is central to sustainable agriculture, but remains challenging to assess in diversified agroecosystems such as permaculture. Soil condition reflects the interaction of physical, chemical, and biological properties, but practical assessmen...
In recent years, the industrial motor bearing fault diagnosis method based on deep learning and multi-source information fusion has made some research progress, and research results show that the uncertainty of noise interference and signal measureme...