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Photovoltaic Array Fault Diagnosis and Localization Method Based on Modulated Photocurrent and Machine Learning.
Sensors (Basel)
December 2024
College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China.
Photovoltaic arrays are exposed to outdoor conditions year-round, leading to degradation, cracks, open circuits, and other faults. Hence, the establishment of an effective fault diagnosis system for photovoltaic arrays is of paramount importance. However, existing fault diagnosis methods often trade off between high accuracy and localization.
View Article and Find Full Text PDFDeep Learning Unravels Differences Between Kinematic and Kinetic Gait Cycle Time Series from Two Control Samples of Healthy Children Assessed in Two Different Gait Laboratories.
Sensors (Basel)
December 2024
Pediatric Neurology, ERN-RND, Euro-NMD, Vall d'Hebron Institut de Recerca (VHIR), Hospital Universitari Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus, Passeig Vall d'Hebron 119-129, 08035 Barcelona, Spain.
Article Synopsis
- The study examines how deep learning can compare gait cycle time series from healthy children assessed in two different labs using similar protocols.
- Researchers used a ResNet-based model that effectively identified the source of each dataset with high accuracy by analyzing various gait parameters.
- The findings highlight the need for standardized protocols and effective data pre-processing to improve the consistency and applicability of machine learning models in clinical environments.
Payload State Prediction Based on Real-Time IoT Network Traffic Using Hierarchical Clustering with Iterative Optimization.
Sensors (Basel)
December 2024
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
IoT (Internet of Things) networks are vulnerable to network viruses and botnets, while facing serious network security issues. The prediction of payload states in IoT networks can detect network attacks and achieve early warning and rapid response to prevent potential threats. Due to the instability and packet loss of communications between victim network nodes, the constructed protocol state machines of existing state prediction schemes are inaccurate.
View Article and Find Full Text PDFExploring Task-Related EEG for Cross-Subject Early Alzheimer's Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis.
Sensors (Basel)
December 2024
Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China.
The early prediction of Alzheimer's disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT).
View Article and Find Full Text PDFCharacterizing Droughts During the Rice Growth Period in Northeast China Based on Daily SPEI Under Climate Change.
Plants (Basel)
December 2024
School of Water Conservancy and Electric Power, Heilongjiang University, Harbin 150080, China.
In agricultural production, droughts occurring during the crucial growth periods of crops hinder crop development, while the daily-scale standardized precipitation evapotranspiration index () can be applied to accurately identify the drought characteristics. In this study, we used the statistical downscaling method to obtain the daily precipitation (), maximum air temperature () and minimum air temperature () during the rice growing season in Heilongjiang Province from 2015 to 2100 under the SSP1-2.6, SSP2-4.
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