A5101678015- Building Energy and Comfort Optimization
- Robot Manipulation and Learning
- Robotic Path Planning Algorithms
- Smart Grid Energy Management
- Refrigeration and Air Conditioning Technologies
- Membrane Separation Technologies
- Solar Thermal and Photovoltaic Systems
- Advanced Computational Techniques and Applications
- Railway Engineering and Dynamics
- Innovation and Knowledge Management
- Traffic Prediction and Management Techniques
- Railway Systems and Energy Efficiency
- Land Use and Ecosystem Services
- Membrane Separation and Gas Transport
- Qualitative Comparative Analysis Research
- Social Robot Interaction and HRI
- Ethics and Social Impacts of AI
- Innovation Policy and R&D
- Human-Automation Interaction and Safety
- AI-based Problem Solving and Planning
- Conservation, Biodiversity, and Resource Management
- Gear and Bearing Dynamics Analysis
- Digital Image Processing Techniques
- Phase Change Materials Research
- Safety Warnings and Signage
Shenyang Jianzhu University
2023-2025
Colorado School of Mines
2019-2024
Hunan University
2019-2024
State Key Laboratory of Chemobiosensing and Chemometrics
2024
Changsha University of Science and Technology
2023-2024
Chongqing University
2023
Hangzhou Normal University
2022-2023
Liaoning University
2023
Jiangsu Provincial Academy of Environmental Science
2022
Hubei Normal University
2021
The global innovation environment is undergoing major changes. Driven by its entrepreneurship policy, China's level has gradually improved, but the regional gap remains large. As previous studies mainly focused on net impact of policy innovation, knowledge combination policies to improve capability still lacking. To fill this in literature, study uses fuzzy set qualitative comparative analysis (fsQCA) data from 31 provinces China explore how five (i.e., technology transfer, fiscal and tax,...
Resource endowment influences college students' entrepreneurship and has a significant impact on entrepreneurial intention. To explore the relationship between different forms of capital, their synergy, high-level intention, this study used 46 colleges universities as samples analyzed configurations capital Analyses were conducted by combining fuzzy set qualitative comparative analysis (fsQCA) necessary condition (NCA). The results showed that there two paths for to promote with policy...
Deep groove ball bearings are relatively weak in design for withstanding axial forces, but practical applications, they may be subject to slight impact. Spalling failure occur at a non-central position on the raceway. In response this issue, paper studies impact characteristics generated bearing system due uneven contact both sides of raceway when rolling element passes through defect area. A three-degree-of-freedom kinetic model considering is proposed paper, simulating effect flaking...
Robot teleoperation is a transformative field that can enable workers to safely perform tasks in dangerous environments. In this letter, we present our work towards system with safe, realistic force feedback for intuitive control of robotic arm and anthropomorphic hand as its end effector. The interfaces the user via novel data glove, which detects state using inertial measurement units (IMUs) custom curvature sensors, employs pneumatic muscles provide feedback. We use glove Kinova Jaco 3D...
Sampling-based motion planning works well in many cases but is less effective if the configuration space has narrow passages. In this paper, we propose a learning-based strategy to sample these passages, which improves overall time. Our algorithm first learns from graphs and then uses learned information effectively generate passage samples. We perform experiments various 6D 7D scenes. The offers one order of magnitude speed-up compared baseline planners some
We present a learning-based approach to prove infeasibility of kinematic motion planning problems.Samplingbased planners are effective in high-dimensional spaces but only probabilistically complete.Consequently, these cannot provide definite answer if no plan exists, which is important for high-level scenarios, such as task-motion planning.We propose combination bidirectional samplingbased (such RRT-connect) and machine learning construct an proof alongside the two search trees.An closed...
We present a learning-based approach to prove infeasibility of kinematic motion planning problems. Sampling-based planners are effective in high-dimensional spaces but only probabilistically complete. Consequently, these cannot provide definite answer if no plan exists, which is important for high-level scenarios, such as task-motion planning. apply data generated during multi-directional sampling-based (such PRM) machine learning construct an proof. An proof closed manifold the obstacle...