Yingjun Ji
Biographic Data
| ID | 10053872 |
|---|---|
| NAME | Yingjun Ji |
| GIVEN NAMES | Yingjun |
| FAMILY NAME | Ji |
| SIGNATURE | JI Y |
| AFFILIATIONS | Liaoning University |
| ORCID | 0000-0001-9988-3314 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
A Hybrid Deep Learning Method With Iterative Feature Selection for Electric Load Forecasting Considering Social Activities and User Behaviors
Electric load inherently reflects the collective patterns of social activities and user behaviors, making their accurate prediction a challenging task. Accurate electric load forecasting is crucial for the planning, operation, scheduling, and market management of modern power systems, especially under the increasing complexity of residential energy consumption behaviors. From a data-driven modeling perspective, traditional load forecasting based …
Solving Human–Robot Collaborative Circular Disassembly Line Balancing Problem via Graph Neural Network-Enhanced Proximal Policy Optimization Algorithm
Industry 5.0 promotes the transformation of manufacturing toward flexibility, personalization, and sustainability. As a critical component of closed-loop manufacturing systems, disassembly operations urgently require more flexible and efficient human–robot collaboration models. To this end, this work, for the first time, proposes a multihuman–robot collaborative circular disassembly line balancing problem. By allowing workers to move between robo…
Improved Carnivorous Plant Algorithm for Human–Robot Collaborative U-Shaped Disassembly Line Balancing With Mobile Workers
The advancement of human–robot collaboration technology has positioned remanufacturing as a crucial part of the circular economy, driving both economic growth and environmental sustainability. In the era of Industry 5.0, these technologies enhance the efficiency and flexibility of disassembly tasks. However, most research on human–robot collaborative disassembly (HRCD) line balancing overlooks the mobility of workers. This study introduces a prof…
No prominent works on this page.
Improved Carnivorous Plant Algorithm for Human–Robot Collaborative U-Shaped Disassembly Line Balancing With Mobile Workers
The advancement of human–robot collaboration technology has positioned remanufacturing as a crucial part of the circular economy, driving both economic growth and environmental sustainability. In the era of Industry 5.0, these technologies enhance the efficiency and flexibility of disassembly tasks. However, most research on human–robot collaborative disassembly (HRCD) line balancing overlooks the mobility of workers. This study introduces a prof…
A Hybrid Deep Learning Method With Iterative Feature Selection for Electric Load Forecasting Considering Social Activities and User Behaviors
Electric load inherently reflects the collective patterns of social activities and user behaviors, making their accurate prediction a challenging task. Accurate electric load forecasting is crucial for the planning, operation, scheduling, and market management of modern power systems, especially under the increasing complexity of residential energy consumption behaviors. From a data-driven modeling perspective, traditional load forecasting based …
Solving Human–Robot Collaborative Circular Disassembly Line Balancing Problem via Graph Neural Network-Enhanced Proximal Policy Optimization Algorithm
Industry 5.0 promotes the transformation of manufacturing toward flexibility, personalization, and sustainability. As a critical component of closed-loop manufacturing systems, disassembly operations urgently require more flexible and efficient human–robot collaboration models. To this end, this work, for the first time, proposes a multihuman–robot collaborative circular disassembly line balancing problem. By allowing workers to move between robo…
Artificial neural network (2 works) · Advanced Manufacturing and Logistics Optimization (1 works) · Assembly line (1 works) · Assembly Line Balancing Optimization (1 works) · Automotive industry (1 works) · Benchmarking (1 works) · Customer satisfaction (1 works) · Deep learning (1 works) · Electricity Theft Detection Techniques (1 works) · Energy Load and Power Forecasting (1 works)