A5101828277- Traffic Prediction and Management Techniques
- Traffic control and management
- Human Mobility and Location-Based Analysis
- Scheduling and Optimization Algorithms
- Optimization and Search Problems
- Advanced Manufacturing and Logistics Optimization
- Transportation Planning and Optimization
- Image Processing and 3D Reconstruction
- Energy Load and Power Forecasting
- Hydrological Forecasting Using AI
- Water Quality and Pollution Assessment
- Assembly Line Balancing Optimization
- VLSI and FPGA Design Techniques
- Distributed and Parallel Computing Systems
- Hydrology and Watershed Management Studies
- 3D Surveying and Cultural Heritage
- Cloud Computing and Resource Management
- Distributed systems and fault tolerance
- Water Quality Monitoring Technologies
- Air Quality Monitoring and Forecasting
- Anomaly Detection Techniques and Applications
- Industrial Vision Systems and Defect Detection
Hebei Normal University of Science and Technology
2024
Northwest A&F University
2022
Rutgers, The State University of New Jersey
2022
Yunnan University
2013-2020
Traffic flow prediction has great significance for improving road traffic capacity and safety. However, in a certain area is usually affected by some factors such as weather, holidays neighboring areas. So, situation complicated difficult. How to use existing data information predict future the key this problem. In paper, we develop an accurate model based on dilated convolution — ST-MINet (Deep Spatio-Temporal Modified-Inception with Dilated Networks). We fully consider complexity,...
The use of deep learning methods to predict traffic flow in transportation systems has become a hot research project. existing predictive model method faces problems such as long calculation time and difficult data pre‐processing, especially for the prediction effect high area. In this study, authors propose novel framework ST‐ESNet, spatio‐temporal expand‐and‐squeeze networks, that designs several effective strategies considering complexity, non‐linearity uncertainty flow, better captures...
Abstract Traffic flow prediction is very important for city construction.Time and space factors have a great impact on traffic flow, Traditional methods can only capture temporal correlations not spatial regional correlations. The use of convolutional neural networks well the correlation between regions dependence time space, which make more accurate. Therefore we introduced dual-path network, divided profile after convolution into two paths trained at same time. One path ResNet one...
The work proposed a novel model to accurately trace the pollution sources of water incidents based on moth-flame optimization and Metropolis-Hastings sampling algorithms.The first utilized estimate parameters pollutant migration-diffusion by minimizing error between monitored predicted concentration.It then traced optimal source location, discharge volume, time using M-H algorithm.Simulation experiments demonstrated achieved significantly lower errors in tracing information compared previous...
Abstract Precise and reliable monthly runoff prediction plays a vital role in optimal management of water resources but non-stationarity skewness time series can pose major challenges for developing appropriate models. To address these issues, this paper proposes novel hybrid model based on Elman neural network (Elman), variational mode decomposition (VMD) Box-Cox transformation (BC), named VMD-BC-Elman model. Firstly, the observed is decomposed into sub-time using VMD better frequency...
The phenomenal growth of the object has brought huge increase in traffic on World Wide Web. Long read latency service experienced by end-users, especially during peak hours, continues to be common problem popular web servers while retrieving objects. A replication and deletion algorithm is presented solve problem, which a NP-hard can formulated as 0-1 constraint optimization problem. defines an appropriate replica distribution with objective decrease number access operations over excessive...
The flexible job-shop scheduling algorithm with the high computational complexity is important in both fields of production management and combinatorial optimization. We developed an improved genetic approach for problem (FJSP), since it quite difficult to achieve optimal solution this within reasonable computation time. A chromosome representation combines routing sequencing information represented algorithms (GAs) commonly optimize by minimizing objective, makespan. Our selects according...