A5100360961- Scheduling and Optimization Algorithms
- Advanced Manufacturing and Logistics Optimization
- Metaheuristic Optimization Algorithms Research
- Distributed Control Multi-Agent Systems
- Industrial Vision Systems and Defect Detection
- Assembly Line Balancing Optimization
- Manufacturing Process and Optimization
- Optimization and Search Problems
- Digital Transformation in Industry
- Optimization and Packing Problems
- Machine Learning and ELM
- Advanced Multi-Objective Optimization Algorithms
- Advanced Control Systems Optimization
- Neural Networks Stability and Synchronization
- Mathematical and Theoretical Epidemiology and Ecology Models
- Gene Regulatory Network Analysis
- Energy Efficient Wireless Sensor Networks
- Industrial Technology and Control Systems
- Domain Adaptation and Few-Shot Learning
- Cooperative Communication and Network Coding
- Evolutionary Algorithms and Applications
- Advanced Measurement and Detection Methods
- Anomaly Detection Techniques and Applications
- Sustainable Supply Chain Management
- Rough Sets and Fuzzy Logic
Tongji University
2016-2025
Changchun 208 Hospital
2025
Shenyang University of Technology
2024
Guizhou Normal University
2024
Chinese University of Hong Kong
2024
China Southern Power Grid (China)
2019-2024
Southwest University
2018-2023
Liaoning University
2022-2023
Shenyang University of Chemical Technology
2023
Beijing Information Science & Technology University
2023
Recent studies on operational wireless LANs (WLANs) have shown that user load is often unevenly distributed among access points (APs). This unbalanced results in unfair bandwidth allocation users. We observe the and can be greatly alleviated by intelligently associating users to APs, termed association control, rather than having greedily associate APs of best received signal strength.In this study, we present an efficient algorithmic solution determine user-AP associations ensure max-min...
Decomposition of a multiobjective optimization problem (MOP) into several simple subproblems, named evolutionary algorithm based on decomposition (MOEA/D)-M2M, is new version optimization-based decomposition. However, it fails to consider different contributions from each subproblem but treats them equally instead. This paper proposes collaborative resource allocation (CRA) strategy for MOEA/D-M2M, MOEA/D-CRA. It allocates computational resources dynamically subproblems their contributions....
Surrogate-assisted evolutionary algorithms (EAs) have been intensively used to solve computationally expensive problems with some success. However, traditional EAs are not suitable deal high-dimensional (HEPs) search space even if their fitness evaluations assisted by surrogate models. The recently proposed autoencoder-embedded optimization (AEO) framework is highly appropriate problems. This work aims incorporate models into it further boost its performance, thus resulting in...
This study presents an autoencoder-embedded optimization (AEO) algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge search space can be compressed to informative low-dimensional by using autoencoder as dimension reduction tool. The operation conducted in this low facilitates the population with fast convergence towards optima. To strike balance between exploration and exploitation during optimization, two phases of tailored...
The misuse of drones can jeopardize public safety and privacy. detection catching intruding are crucial urgent issues to be investigated. This work proposes VDTNet, an accurate, lightweight, fast network for visually detecting tracking drones. We first incorporate SPP module into the head YOLOv4 enhance accuracy. Model compression is utilized shrink model size concurrently speed up inference. then propose insert SPPS a ResNeck neck, introduce effective attention backbone compensate accuracy...
Federated learning (FL) is an emerging setting which implement machine in a distributed environment while protecting privacy. Research activities relating to FLhave grown at fast rate recently control. Exactly what have been carrying the research momentum forward question of interest community. This study finds these and optimization path FL based on survey. Thus, this aims review related studies base baseline universal definition gives guiding for future work. Besides, presents prevailing...
Biogeography-based optimization (BBO), a recent proposed metaheuristic algorithm, has been successfully applied to many problems due its simplicity and efficiency. However, BBO is sensitive the curse of dimensionality; performance degrades rapidly as dimensionality search space increases. In this paper, selective migration operator scale up we name it (SBBO). The differential selected heuristically explore global area far possible whist normal distributed chosen exploit local area. By means...
The performance of neural code search is significantly influenced by the quality training data from which models are derived. A large corpus high-quality query and pairs demanded to establish a precise mapping natural language programming language. Due limited availability, most widely-used datasets established with compromise, such as using comments replacement queries. Our empirical study on famous dataset reveals that over one-third its queries contain noises make them deviate user Models...
With the rapid development of edge computing technology, edge-assisted unmanned aerial vehicle (UAV) networks have become popular, helping with fast and cost-effective data collection in mobile crowdsensing (MCS) environments. This paper investigates online problem for MCS over an UAV network architecture, where UAVs work to collect required by tasks at different on-ground point-of-interests (PoIs) autonomous cooperative manner. Different from conventional networks, nodes our help...
Uncertainty in semiconductor fabrication facilities (fabs) requires scheduling methods to attain quick real-time responses. They should be well tuned track the changes of a production environment obtain better operational performance. This paper presents an adaptive dispatching rule (ADR) whose parameters are determined dynamically by information relevant scheduling. First, we introduce workflow ADR that considers both batch and non-batch processing machines improved fab-wide It makes use...
We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems.We first show that the triangle relation E α i|jk ≤ j|ik + k|ij holds all subadditive bipartite measure E, permutations under parties i, j, k, ∈ [0, 1], pure states.It provides interpretation bipartition entanglement, measured by , corresponds to side triangle, which area with (0, 1) is nonzero if only underlying state genuinely entangled.Then, we rigorously prove...
To facilitate responsive and cost-effective computing resource scheduling service delivery over edge-assisted mobile networks, this paper investigates a novel two-stage double auction methodology via utilizing an interesting idea of overbooking to overcome dynamic uncertain nature from edge servers (sellers) demand devices (as buyers). The proposed integrates multiple essential factors such as social welfare maximization decision-making latency (e.g., the time for determining winning...
Background Scar recovery in elderly burn patients is essential for wound healing and psychological well-being. Total glycosides from Centella asiatica have gained attention their anti-inflammatory, anti-oxidant, wound-healing properties, showing promise care. Purpose This study investigates the synergistic effects of combined nursing interventions Asiaticosides on scar well-being undergoing scald repair. The pharmacological properties these glycosides, including potential, are explored...