What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Computer network
Tao Tang mainly focuses on Simulation, Energy consumption, Efficient energy use, Regenerative brake and Urban rail transit.
Tao Tang has included themes like Reliability, Parallel processing, Optimization problem, Mathematical optimization and Job shop scheduling in his Simulation study.
His Energy consumption research is multidisciplinary, incorporating perspectives in Scheduling and Automotive engineering.
His studies in Efficient energy use integrate themes in fields like Automatic train control and Optimal control.
Tao Tang works mostly in the field of Urban rail transit, limiting it down to topics relating to Nonlinear programming and, in certain cases, Evolutionary algorithm and Operations research, as a part of the same area of interest.
In his study, Control system is strongly linked to Control theory, which falls under the umbrella field of Real-time computing.
His most cited work include:
- A Cooperative Scheduling Model for Timetable Optimization in Subway Systems (210 citations)
- A Subway Train Timetable Optimization Approach Based on Energy-Efficient Operation Strategy (205 citations)
- A Survey on Energy-Efficient Train Operation for Urban Rail Transit (177 citations)
What are the main themes of his work throughout his whole career to date?
Control system, Simulation, Energy consumption, Urban rail transit and Real-time computing are his primary areas of study.
His research investigates the connection between Control system and topics such as Control engineering that intersect with issues in Control theory and Control.
His research integrates issues of Regenerative brake and Computation in his study of Simulation.
His Energy consumption course of study focuses on Efficient energy use and Automatic train operation.
His biological study spans a wide range of topics, including Quality of service, Scheduling, Job shop scheduling and Nonlinear programming.
His research in Real-time computing tackles topics such as Network packet which are related to areas like Networked control system.
He most often published in these fields:
- Control system (25.41%)
- Simulation (20.49%)
- Energy consumption (18.44%)
What were the highlights of his more recent work (between 2018-2021)?
- Control system (25.41%)
- Control theory (10.25%)
- Beijing (11.89%)
In recent papers he was focusing on the following fields of study:
Tao Tang mainly investigates Control system, Control theory, Beijing, Mathematical optimization and Control.
Tao Tang combines subjects such as Urban rail transit, Quality of service, Computer network, Real-time computing and Software with his study of Control system.
As part of the same scientific family, he usually focuses on Real-time computing, concentrating on Network packet and intersecting with Optimal control.
Tao Tang frequently studies issues relating to Energy consumption and Mathematical optimization.
His Energy consumption research is multidisciplinary, incorporating elements of Regenerative brake, Energy conservation, Optimization problem and Efficient energy use.
His work carried out in the field of Control brings together such families of science as Perspective and Unified Modeling Language.
Between 2018 and 2021, his most popular works were:
- Big Data Analytics in Intelligent Transportation Systems: A Survey (167 citations)
- An energy-efficient rescheduling approach under delay perturbations for metro systems (27 citations)
- Timetable Optimization for Regenerative Energy Utilization in Subway Systems (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Computer network
His main research concerns Control system, Control theory, Beijing, Model predictive control and Mathematical optimization.
The various areas that Tao Tang examines in his Control system study include Urban rail transit and Quality of service, Computer network.
His Quality of service study incorporates themes from Optimal control, Transmission delay, Network packet, Control parameters and Real-time computing.
His work deals with themes such as Lagrange multiplier and Control theory, Lyapunov stability, which intersect with Model predictive control.
Integer programming and Genetic algorithm are the core of his Mathematical optimization study.
His research in Commercial software intersects with topics in Energy consumption, Headway, Regenerative brake, Optimization problem and Robustness.
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