Results 61 to 70 of about 196 (129)

A Novel Method of Pure Output Modal Identification Based on Multivariate Variational Mode Decomposition

Structural Control and Health Monitoring, Volume 2024, Issue 1, 2024.
This paper proposes a novel parameterized frequency‐domain modal parameter identification method, called direct modal variational mode decomposition (DMVMD), based on the multivariate variational mode decomposition (MVMD) framework and the principle of modal superposition. Under the constraint of normalized mode shapes, this paper theoretically derives
Tao Li, Rui Hou, Kangkang Zheng, Lingfeng Li, Bo Liu, Chia-Ming Chang   +5 more
wiley   +1 more source

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

International Journal of Energy Research, Volume 2024, Issue 1, 2024.
The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs.
Zhihu Gao, Bo Sun, Zeyu Wu, Yang Xi, Qiang Feng, Zili Wang, Yeli Zhou, Junlin Pan, Jiankun Xu, Chaofan Sun   +9 more
wiley   +1 more source

EEG Based Biometric Authentication System Using Multivariate FBSE Entropy [PDF]

, 2023
Data privacy and security are severe concerns in our world today. A broad range of biometrics relies on physiological traits, including fingerprints, iris scans, and facial recognition for authentication.
Kritiprasanna Das (15940493), Sai Pranavi Kamaraju (15940497), Ram Bilas Pachori (12592933)   +2 more
core   +1 more source

Bearing Fault Prediction Based on Mixed Domain Features and GWO‐SVM

Journal of Electrical and Computer Engineering, Volume 2024, Issue 1, 2024.
The rotating machinery is composed of rolling bearing connection, so the fault identification of rolling bearing is a very critical task. We propose a bearing fault identification algorithm based on grey wolf optimizer (GWO) to address the common problems of high signal noise, inability of a single indicator to accurately reflect the true state of ...
Xuan Zhou, Ruiyang Xia, Zhaodong Zhang, Sasa Duan, Mao Cheng, Chengjiang Zhou, Min Mao, Antonio J. Marques Cardoso   +7 more
wiley   +1 more source

A customised 1D-CNN for recognition of freezing of gait in Parkinson’s disease using multivariate decomposition techniques

Open Computer Science
The freezing of gait (FoG) presents a sudden challenge in sustaining movement which becomes a common gait issue in people with later stages of Parkinson’s disease (PD). FoG often results in falls that reduces the individual’s impact on life.
Rajendran Nancy, Selvaraj Thomas G., Pennagaram Krushnakumar Kalkeseetharaman, Ashwala Ravi T., Muthu Sinnasamy Pandian Subathra   +4 more
doaj   +1 more source

Multichannel Multiscale Two-Stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction Using Vectorcardiogram Signal

Applied Sciences, 2021
Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle.
Jay Karhade, Samit Kumar Ghosh, Pranjali Gajbhiye, Rajesh Kumar Tripathy, U. Rajendra Acharya   +4 more
doaj   +1 more source

Subsynchronous Oscillation Source Location in Power System with High Penetration of Wind Power Using Multivariate Variational Mode Decomposition [PDF]

Accurately and promptly extracting subsynchronous oscillation (SSO) components from measurements and locating SSO sources are crucial for SSO suppression.
Pons, Enrico, Li, Xue, Liu, Bohan, Jiang, Tao, Huang, Tao, Li, Guoqing, Mazza, Andrea   +6 more
core   +1 more source

A Single-End Location Method for Small Current Grounding System Based on the Minimum Comprehensive Entropy Kurtosis Ratio and Morphological Gradient

Applied Sciences
Fault location technology is crucial for enhancing the efficiency of fault maintenance and ensuring the safety of the power supply in small current grounding systems.
Jiyuan Cao, Yanwen Wang, Lingjie Wu, Yongmei Zhao, Le Wang   +4 more
doaj   +1 more source

Series-Core Fusion Based Multivariate Variational Mode Decomposition for Short-Term Wind Power Prediction Using Multiple Meteorological Data

Forecasting
Accurate wind power forecasting is critical for enhancing the operational efficiency and stability of electrical power grids. Conventional single-variable signal decomposition forecasting methods ignore the coupling relationship between wind power and ...
Wentian Lu, Zhenming Lu, Wenjie Liu, Yifeng Cao   +3 more
doaj   +1 more source

A Transfer Learning Enhanced Decomposition-Based Hybrid Framework for Forecasting Multiple Time-Series [PDF]

, 2023
Time-series forecasting is a challenging task that requires high accuracy and efficiency. Hybrid models that combine decomposition algorithms with multiple individual models have demonstrated promising results for forecasting performance.
Y He (7724882), Zili Zhang (13101534), L Tao (9648497)   +2 more
core