A5100676340- Fault Detection and Control Systems
- Advanced Algorithms and Applications
- Stability and Control of Uncertain Systems
- High-Voltage Power Transmission Systems
- Advanced Computational Techniques and Applications
- Advanced Sensor and Control Systems
- HVDC Systems and Fault Protection
- Machine Fault Diagnosis Techniques
- Advanced Decision-Making Techniques
- Granular flow and fluidized beds
- Advanced Chemical Sensor Technologies
- Advanced Data Processing Techniques
- Control Systems and Identification
- Industrial Technology and Control Systems
- Advanced Battery Technologies Research
- Power Systems and Technologies
- Smart Grid and Power Systems
- Reliability and Maintenance Optimization
- Energy Load and Power Forecasting
- Industrial Vision Systems and Defect Detection
- Power Systems Fault Detection
- Industrial Automation and Control Systems
- Wireless Sensor Networks and IoT
- Energy Efficient Wireless Sensor Networks
- Indoor and Outdoor Localization Technologies
Wuhan University of Science and Technology
2011-2025
Chongqing Institute of Green and Intelligent Technology
2025
Anhui University
2008-2024
Yancheng Teachers University
2021
Tianjin University of Commerce
2009-2021
State Grid Corporation of China (China)
2013-2021
Changchun University
2021
Jilin Agricultural Science and Technology University
2021
China Mobile (China)
2020
Huazhong University of Science and Technology
2010-2020
In industrial applications, nearly half the failures of motors are caused by degradation rolling element bearings (REBs). Therefore, accurately estimating remaining useful life (RUL) for REBs crucial importance to ensure reliability and safety mechanical systems. To tackle this challenge, model-based approaches often limited complexity mathematical modeling. Conventional data-driven approaches, on other hand, require massive efforts extract features construct health index. paper, a novel...
The main focus of this paper is on the analysis and integrated design $\mathcal {L}_2$ observer-based fault detection (FD) systems for discrete-time nonlinear industrial processes. To gain a deeper insight into FD framework, existence condition introduced first. Then, an approach realized by solving proposed with aid Takagi-Sugeno fuzzy dynamic modeling technique piecewise-fuzzy Lyapunov functions. Most importantly, weighted residual generator proposed, aiming at achieving optimal...
The accurate electrocardiogram (ECG) interpretation is important for several potentially life-threatening cardiac diseases. Recently developed deep learning methods show their ability to distinguish some severe heart However, since neural network requires a high cost on memory consumption and computation, implementation scenarios of these are constrained nonportable devices. Few commercial portable devices only have heartbeat detection ability, therefore, few simple diseases can be...
Marketing researchers and managers are interested in understanding how consumers utilize country‐of‐origin (COO) information evaluating foreign products. Argues that factors operating at the individual consumer’s psychological level may offer additional insight into process COO information. Hypothesizes differences their tendency to evaluate product influence effect of COO. Specifically, when a need for cognition (NFC) is low, more influential evaluation. Favourable lead positive evaluation...
Studies on the fluidization of biomass particles and binary mixtures with mediums were carried out. The used wood chip, mung beans, millet, corn stalk, cotton employed silica sand, continental flood basalt (CFB) cinder, aluminum oxide. Experiments performed in a rectangular fluidized bed (cross-sections 0.4 × m dense region 0.5 freeboard region, height 4.4 m). minimum velocity (UMF) approximate sphere (wood millet) long thin (corn stalk stalk) different transection diameters ratios...
Musculoskeletal injuries induced by high-intensity and repetitive physical activities represent one of the primary health concerns in fields public fitness sports. injuries, often resulting from unscientific training practices, are particularly prevalent, with tibia being especially vulnerable to fatigue-related damage. Current tibial load monitoring methods rely mainly on laboratory equipment wearable devices, but datasets combining both sources limited due experimental complexities signal...
In this paper, the torus-event-based fault detection and isolation (FDI) problem is investigated for a class of time-varying multirate systems. An ellipsoidal constraint first adopted to describe in more practical pattern, novel torus-event-triggering scheme proposed improve unilateral triggering mechanism. The aim design filter estimators such that both prescribed variance on estimation error desired H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML"...
In modern manufacturing industry, in order to adapt changes the general environment, industry must improve production efficiency. To this end, article introduces an improved genetic algorithm based on rule selection tackle nondeterministic polynomial hard problem stemming from inventory fibre resources and principles optical cable production. The aims maximize score minimize segmentation rate. It employs a permutation encoding approach link with allocation solutions applies self-attention...
In this paper, a novel load shedding scheme against voltage instability with deep reinforcement learning (DRL). The convolutional neural networks (CNNs) are chosen to automatically learn the features and exploit local spatial correlations from TS data of voltages. DRL controller interacts system dynamics through sequence observations, actions rewards two networks, namely critic actor networks. network is used evaluate quality determine time, location amounts (LS). A global reward function...
The highly dynamic nature of agro-ecosystems in space and time usually leads to high intra-class variance low inter-class separability the fine spatial resolution (FSR) remotely sensed imagery. This makes traditional classifiers essentially relying on spectral information for crop mapping from FSR imagery an extremely challenging task. To mine effectively rich imagery, this paper proposed a Scale Sequence Object-based Convolutional Neural Network (SS-OCNN) that classifies images at object...