Category Archives: Conference


Keynote Speakers

DR.Kejun Long Long ,Professor
Changsha University of Science and Technology
Changsha ,Hunan Province ,China

Headway-Based Multi-Route Transit Signal Priority at Isolated Intersection

      This paper advances the issue of Transit Signal Priority (TSP) control by introducing an application to multi-route bus conflicting requests, capitalizing on the headways and improved total delay of a multi-route bus network. The headway-based TSP accommodating conflicting requests overcomes the shortcoming by the traditional First Arrival, First Servestrategy and presents significant improvement on bus service performance. According to the bus arrival time, expected headway, and headway deviation value, we establish an optimal signal control model which aiming to minimize the deviation between the bus headway and the expected headway. The case study analysis conducts three schemes: background cycle-based TSP, total delay-based TSP, and headway deviation-based TSP. The performance of headway-based TSP is compared against other two schemes under three different intersection scenarios. The results show that the headway-based TSP has the best efficiency, and the average headway deviation after passing through the signalized intersection is basically less than 30seconds. It can not only effectively reduce vehicle delay, but also effectively improve the uniformity of bus headway.

About Prof Kejun Long

     Prof Kejun Long ,Male ,born in 1974, he is a professor of Changsha University of Science and Technology, Ph.D.advisor, and now acted as the dean of Hunan province Key lab of Smart Highway and Cooperative Vehicle Infrastructure System,he is a member of Traffic Engineering Undergraduate Specialty Course Instructive Committee of Chinese Department of Education,and member of Hunan Traffic Engineering Society,and acted as reviewer of many Journals related to the traffic field such as China Journal of Highway and Transport,Accident analysis & prevention and etc. And was awarded the nationwide prominent teacher in 2019.

Dr.Jianping Xiang,Professor
Department of Energy
University of the Highlands and Islands
12b Ness Walk, Inverness, Scotland,UK

Study on Artificial intelligence methods for Massive Wind Turbine Data Analysis

Massive wind turbine data set includes the location of the turbines, information of each turbine, and measurements etc. Vibration sensors, speed sensors, temperature sensors and other type of sensors have generated massive real-time data from wind turbines. Scientists and engineers can learn from the massive data to make wind turbines to harvest energy more efficiently, via providing early fault warning from massive data analysis. In this paper, the BP neural network and the particle swarm optimization (PSO) method are applied to a data set of bearing fault of a wind turbine to demonstrate that the artificial intelligence methods are feasible on the analysis of massive wind turbine data. Using the particle swarm optimization (PSO) algorithm to establish an early warning model of main bearing faults, optimized the weights and bias of BP neural network, avoiding BP neural network algorithm falling into local minimum. The results showed that high accuracy can be acquired in predicting main bearing temperature and accurately realize the early warning of main bearing fault. Wind farm operators can therefore facilitate early maintenance and repair of wind turbines components, reducing the operation and maintenance costs of wind turbines.

About Prof. Jianping Xiang

Professor Xiang jianping received the B.S. degree from the Department of Electrical Engineering, Hunan University, the M.S. degree of Automation and Ph.D. degree of earth science and information physics from Central South University. Since October 1999, he has been a postdoctoral fellow at the Royal Society of Leicester University in the UK, a senior fellow at the School of Engineering in Durham University, an Innovation researcher at the Center of Renewable Energy Systems Technology in University of Loughborough (CREST, Loughborough University), the director of the energy department in the British Highland and Islands University (UHI), a visiting professor at North China Electric Power University, an academic leader and graduate tutor at Changsha University of Science and Technology. Professor Xiang Jianping is the member of IEEE, top member of SEG, member of EI, and the International Cooperation Department of International Cooperation Department, Ministry of Science and Technology of China. His academic research includes offshore wind turbine fault diagnosis and condition monitoring, seawater power generation and control, resource exploration data processing. He directed and participated in dozens of large-scale scientific research and industrial projects in Europe and China, involving more than 100 million RMB of funds. Professor Xiang has published a number of international forefront research papers and reports, and developed several innovative systems and methods, some of which were included in the latest development bibliography of motor, the latest research report of the European Union, and the MBA report of the Loughborough University. Professor Xiang has won the British Central Innovation Award. 

Professor Xiaolan Xiao

School of Information and Computational Science 
Tan Kah Kee College
Ximen University

Characterization and reliability analysis of statistical distribution of A Class of Stochastic Signal Systems

In recent years, big data statistical survival analysis has become the most challenging and hot topic for biologists and statisticians. On the basis of extending the research of information flow in general information space to the study of stochastic dynamic information flow in generalized information measurement space with higher value and more information, this paper discusses the stochastic statistical inference of survival analysis of human and biological populations in a class of random effect models, and explores and excavates a series of valuable large-scale processes in this kind of generalized process Based on the statistical information of data, the optimal statistical inference mathematical model of random signals of this kind of generalized process is obtained, which provides a very effective mathematical processing method and method for statistical inference and analysis of big data for further study of such processes, such as optimal control and reliability analysis in the research of brain aging.

About Prof.Xiao xiaolan

     Professor Xiao Xiaonan is a professor, Ph.D. advisor, and the chair of Department of Information and Computational Science at Xiamen University Tan Kah Kee College. He is the associate dean of the College of Information Science and Technology and a member of the International Association for Biostatistics & International Statistics Association. Professor Xiao is the executive director of Biological Mathematical Society & Mathematical Society of Fujian Province. He has been awarded The Distinguished Teacher of Higher Education in Fujian Province. His expertise mainly focuses on the studies of complex systems modeling and optimal control. He has published 121 articles at important journals and 22 academic works and textbooks. He was awarded numerous nationwide and international prizes in research and teaching , including 68 research projects that have won the national , provincial, and college awards ( 32 first prizes , 13 second 10 third prizes , 13 Awards of Excellence) . 

DR.Duo Long ,Professor
Jilin Institute of Soft Science 
Changchun ,Jilin Province ,China

Research on the Modernization of Smart City Construction Standard

Practicality, solid, and beauty are the famous “three gold principles” of architecture. These three gold principles were put forward by the Roman architect Vitruvius in his classic work “Ten Books of Architecture”, and have guided the construction industry for more than 2,000 years. And until now, they are also perfectly applicable to the modern standard of smart city construction. The simplest standards are often the most difficult to achieve. Practicability is the first problem to be solved in front of builders and developers. The construction of each smart city should strictly follow the standards, connect with the future Internet of Things technology and truly form the standardized storage and on-demand extraction of big data. The proposition and application of practicality standard will be the basis of the application of data mining technology. This simple standard includes the reliability of hardware and the reliability of network transmission technology; the reliability of the operating system, system software and middleware, the guarantee technology of software testing, simulation, simulation technology and tools, backup, fault tolerance, disaster tolerance, operation, and maintenance monitoring and other support system maintenance technology; the guarantee technology of information security system; the maturity of software R & D technology; the management of software development process. All of the above is what we want to study.

 About Prof. Duo Long 

Dr Duo Long ,Senior research fellow ,born in 1971,Chengdu City,China, Doctor graduated from Jilin University ,with a major in biology and agriculture engineering .Post doctoral research station was in Changchun University of Science and Technology ,Mechatronics major .As Institute director of soft science ,Jilin Province ,has been committed to the construction of smart city research .