A5100344992- Fault Detection and Control Systems
- Advanced Sensor and Control Systems
- Advanced Control Systems Optimization
- Advanced Algorithms and Applications
- Smart Grid Energy Management
- Industrial Technology and Control Systems
- Spectroscopy and Chemometric Analyses
- Smart Grid Security and Resilience
- Advanced Statistical Process Monitoring
- Machine Fault Diagnosis Techniques
- Power Systems Fault Detection
- Microgrid Control and Optimization
- Autonomous Vehicle Technology and Safety
- Power Systems and Technologies
- Traffic and Road Safety
- Traffic control and management
- Vibration and Dynamic Analysis
- Advanced Computational Techniques and Applications
- Elevator Systems and Control
- Structural Health Monitoring Techniques
- Mineral Processing and Grinding
- Gear and Bearing Dynamics Analysis
- Water Quality Monitoring and Analysis
- Smart Grid and Power Systems
- Power Systems and Renewable Energy
Tongji University
2025
Kunming Medical University
2025
Helmholtz Centre for Environmental Research
2025
Institute of Disaster Prevention
2024
Hebei Science and Technology Department
2024
NARI Group (China)
2024
State Grid Corporation of China (China)
2021-2024
Zhejiang University
2016-2024
Dow Chemical (United States)
2019-2024
Beihang University
2024
The conventional distributed secondary control of islanded microgrids (MGs) often requires each generator (DG) to communicate with its neighboring DGs successively a small fixed sampling period, which is neither economical nor efficient for MGs due redundant communications. To this end, paper proposes event-triggered strategy the in reduce requirement By designing trigger function and condition DG Lyapunov method, aim achieved an exponential convergence rate only when communicating neighbors...
Abstract In the Industry 4.0 era, chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view how transforming digitally towards AI at scale. First, historical perspective on used to aid humans in better decision‐making shown. Then state‐of‐the‐art research addressing industrial needs reliability safety, process optimization, supply chain, material discovery, reaction engineering highlighted. Finally,...
This study targets the low accuracy and efficiency of support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) was proposed based on deep learning a swarm intelligence optimization to optimize structural parameters SVM improve A nonlinear contraction factor update strategy also proposed. The variable coefficient changes with shrinkage α. Thus, search ability balanced at different early late stages by controlling dynamic coefficient. In...
To improve the detection accuracy of drone-based oriented vehicle object network and establish high-accuracy trajectory datasets, we present a freeway on-ramp (FRVehicle) dataset with bounding box annotations for vehicles in scenes from drone videos. Based on this dataset, analyzed dimension angle distribution patterns road boxes designed an Asymmetric Selective Kernel Network. This algorithm dynamically adjusts receptive field backbone network’s feature extraction to accommodate...
Abstract Driven by industrial development and the rising population, upward trend of electricity consumption is not going to curb. While suppliers make every endeavor satisfy needs consumers, they are facing plight indirect losses caused technical or non-technical factors. Technical usually induced short circuits, power outage, grid failures. The result from humans’ improper behaviors, e.g., burglars. Due restrictions detection methods, rate in traditional lousy. To provide better service...
Wastewater treatment plants (WWTPs) are complex systems presenting stochastic, non-linear, and non-stationary behavior, which makes their operational management very challenging. In this context, data collected from distributed sources across the plant play a central role in optimized operation control of WWTPs. However, even when available, use is far trivial due to coexistence asynchronous measurements, with different granularity, measurements quality (precision, accuracy), multimodal...
To ensure the safety and stability of shield tunnel construction process, ground settlement induced by needs to be effectively predicted. In this paper, a prediction method combining empirical mode decomposition (EMD), chaotic adaptive sparrow search algorithm (CASSA), extreme learning machine (ELM) is proposed. First, EMD used decompose sequence into trend vectors fluctuation fully extract effective information sequence; Second, improved introducing Cubic mapping initialize population...
The goal of "carbon peak, carbon neutral" and the increasing expansion new energy have helped to advance development storage. However, since operating cost storage is high, emission trading power market emerged, effectively improving efficiency. In this paper, a strategy bidding framework participation in day-ahead joint are studied. A model has been established based on Stackelberg game. Finally, "Day-Ahead Intra-Day Carbon Emission Trading (CET)" clearing constructed. It simplified solve...
We follow up on the recently proposed Dynamic Response Surface Methodology (DRSM) [Klebanov and Georgakis Ind. Eng. Chem. Res. 2016, 55(14), 4022] as an effective data-driven approach for modeling time-varying outputs of batch processes with finite time durations. The present new DRSM methodology, DRSM-2, is capable accurately nonlinear continuous over both semi-infinite horizons easily processes, DRSM-1 did in initial publication, cited above. key innovation here introduction exponential...
With the emergence of Industry 4.0, also known as fourth industrial revolution, an increasing number hardware and software sensors have been implemented in chemical production processes for monitoring key variables related to product quality process safety. The accuracy individual can be easily impaired by a variety factors. To improve reliability, sensor fusion scheme based on Bayesian inference is proposed. proposed method capable combining multi-rate data eliminating spurious signals....