A5016266277- IoT and Edge/Fog Computing
- Machine Learning and ELM
- Reinforcement Learning in Robotics
- Robotic Path Planning Algorithms
- Metaheuristic Optimization Algorithms Research
- Robot Manipulation and Learning
- Elevator Systems and Control
- Robotics and Sensor-Based Localization
- Viral Infectious Diseases and Gene Expression in Insects
- Teleoperation and Haptic Systems
- Stock Market Forecasting Methods
- Digital Transformation in Industry
- Cloud Computing and Resource Management
- Computational and Text Analysis Methods
Northwestern Polytechnical University
2023-2024
New York University
2024
This paper presents a new deep learning (DRL) framework for resource allocation and optimization in cloud computing. The proposed method leverages the multi-agent DRL architecture to address extensive decision-making processes large environments. We formulate problem based on Markov's decision, creating state space that includes use of resources, work characteristics, energy. workspace comprises VM placement, migration, physical power determination. Careful reward balances energy,...
This study presents a new method for optimising high-risk trading (HFT) strategies using deep learning (DRL). We propose multi-time DRL framework integrating advanced neural network architectures with sophisticated business data processing techniques. The employs combination of convolutional networks manual order analysis, short-term memory time series processing, and multi-head listening mechanism body fusion. formulate the HFT problem based on Markov Decision Processes use Proximal Policy...
This study discussion point of this paper is to make an in-depth analysis the development impact Internet Things combined with edge computing and artificial intelligence. In process, importance criticality data processing decision making as well challenges faced should be elaborated respectively. With rapid popularization devices, has brought more innovative solutions for different application scenarios such intelligent furniture industrialization, automatic driving transportation by...
This paper proposes a novel hierarchical reinforcement learning (HRL) framework of complex manipulation tasks which integrates the human prior knowledge. The involves following steps: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \textbf{1)} $</tex-math> </inline-formula> process is divided into several stages based on \textbf{2)} Transition conditions between are determined in form "if-then" rules....
This paper presents an innovative approach to real-time multilingual transcription and minutes generation for video conferences using Large Language Models (LLMs). The proposed system integrates advanced speech recognition techniques with sophisticated natural language processing capabilities address the challenges of communication in virtual meetings. implementation incorporates a novel hierarchical architecture combining transformer-based models rhetorical structure modeling automated...