Prof. Steven Guan
Honorary Professor at University of Liverpool
Professor and the Director for Research Institute of Big Data Analytics
Xi'an Jiaotong-Liverpool University (XJTLU).
Reasearch Area: Machine Learning, Computational Intelligence, Big Data Analytics, Mobile Commerce, Modeling,Networking, Personalization, Security, and Pseudorandom Number Generation.
Steven Guan received his BSc. from Tsinghua University and M.Sc. (1987) & Ph.D. from the University of North Carolina at Chapel Hill. He is currently a Professor and the Director for Research Institute of Big Data Analytics at Xi'an Jiaotong-Liverpool University (XJTLU).He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK. Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and departmentmanager. After leaving the industry, he joined the academia for three and half years. He served as deputy director for the ComputingCenter and the chairman for the Department of Information & Communication Technology. Later he joined the Electrical & ComputerEngineering Department at National University of Singapore as an associate professor for 8 years.
Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and department manager. After leaving the industry, he joined the academia for three and half years. He served as deputy director for the Computing Center and the chairman for the Department of Information & Communication Technology. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor for 8 years. Prof. Guan’s research interests include: machine learning, computational intelligence, big data analytics, mobile commerce, modeling, networking, personalization, security, and pseudorandom number generation. He has published extensively in these areas, with 130+ journal papers and 180+ book chapters or conference papers. He has chaired, delivered keynote speech for 80+ international conferences and served in 180+ international conference committees and 20+ editorial boards. There are quite a few inventions from Prof. Guan including Generalized Minimum Distance Decoding for Majority Logic Decodable Codes, Prioritized Petri Nets, SelfModifiable Color Petri Nets, Dynamic Petri Net Model for Iterative and Interactive Distributed Multimedia Presentation, Incremental Feature Learning, Ordered Incremental Input/Output Feature Learning, Input/Output Space Partitioning for Machine Learning, Recursive Supervised Learning, Reduced Pattern Training using Pattern Distributor, Contribution Based Feature Selection, Incremental Genetic Algorithms, Incremental Multi-Objective Genetic Algorithms, Decremental Multi-objective Optimization, Multi-objective Optimization with Objective Replacement, Incremental Hyperplane Partitioning for Classification, Incremental Hyper-sphere Partitioning for Classification, Controllable Cellular Automata for Pseudorandom Number Generation, Self Programmable Cellular Automata, Configurable Cellular Automata, Layered Cellular Automata, Transformation Sequencing of Cellular Automata for Pseudorandom Number Generation, Open Communication with Self-Modifying Protocols, etc.
Keynote Speech Topic:
Input Space Partitioning for Machine Learning
This talk introduces an input attribute grouping method to improve the performance of learning. During training for a specific problem, the input attributes are partitioned into groups according to the degree of inter‐attribute promotion or correlation that quantifies the supportive or negative interactions between attributes. After partitioning, multiple sub‐networks are trained by taking each group of attributes as their respective inputs. The final classification result is obtained by integrating the results from each sub‐network. Experimental results on several UCI datasets demonstrate the effectiveness of the proposed method.
Prof. Yulin Wang
Reasearch Area: Image and Video Processing, Digital Rights Management, Information Security, Intelligent System, E-Commerce, IoT, Code Clone
Yulin Wang is a full professor in the School of Computer Science, Wuhan University, China. His research interests include image and video processing, digital rights management, information security, intelligent system, e-commerce, IoT, code clone and so on.
He got his PhD degree from University of London, UK. He got his master and bachelor degree from Huazhong University of Science and Technology（HUST）and Xi-Dian University respectively, both in China.
Prof. Wang served as EiC of 2 international journals and reviewer of top IEEE and ACM journals. He also served as reviewer of Innovative talents projects and national research funds, including National High Technology Research and Development Program of China. Prof. Wang was the external PhD advisor of Dublin City University, Ireland during 2008-2010.
In recently 10 years, Prof. Wang served as chairman of more than 10 international conferences, and keynote speakers in more than 20 international conferences. Besides UK, he visited US, France,Italy, Portugal,Croatia, Australia, Germany, korea, Ireland,Singapore, Malaysia, Japan, and Hong Kong. In addition, Prof. Wang has been appointed as the deputy director of Hubei provincial science and technology commission (CAPD) since 2014.
Keynote Speech Topic:
Drone（UAV）: from bionic flight to brain like autonomous navigation
Animals have strong individual and group navigation ability, which can realize the direct output from the original perceptual information input to the accurate, reliable and flexible navigation action. This end-to-end intelligent behavior has always been one of the focuses in the field of artificial intelligence (AI). In recent years, with the development of brain and neuroscience, researchers have gradually revealed the brain navigation mechanism of insects, mammals and their groups. Inspired by their navigation mechanisms, a new bionic navigation technology "brain like navigation" has been greatly developed with intelligent algorithms and computing power, showing the characteristics of autonomous environment perception, spatial cognition, intelligent navigation.
Prof. Yuanchang Zhong
Chongqing University, China
Reasearch Area: Electronic Technology, Electronic Measurement Technology , System Engineering , Electromagnetic Compatibility Technology, Multi-sensor Information Fusion Technology
Prof. Yuanchang Zhong served as an engineer of circuit design, Research Institute of Chongqing Huaguang Instrument factory during July 1988～1997. He served as the deputy director of the Experimental Teaching Center, and the main lecturer of the "National Electrical and Electronic Technology Teaching Team" of Chongqing University. He taught undergraduate courses "Basics of Circuit Analysis", "Electronic Technology", "Circuit Principles" and "Electronic Measurement Technology" , "System Engineering" , and graduate courses "Electromagnetic Compatibility Technology", "Multi-sensor Information Fusion Technology". Now, he served as Director of the Electronic Technology Experiment Center, College of Communication Engineering, Chongqing University, China. Currently, as the backbone of the academician’s research team of professor Yang Shizhong.
Keynote Speech Topic:
Research on the Regulating Method of Electric Gas Pressure Regulator and Its Remote Monitoring and Controlling System
The energy carried by sensor nodes is confinement, which is a major bottleneck in restricting the application of traditional Wireless Sensor Networks (WSNs). When the sensor nodes gradually depleted energy and the number of dead nodes is enormous in the network, the entire network will be paralyzed and can not achieve the goal of monitoring tasks. It is extremely difficult and unrealistic to make energy supplied or replace the battery for the sensor nodes due to the large number of nodes, small size and the particularity of the application environment of WSNs. Moreover, with the death of nodes in harsh or even dangerous environments, a large number of sensor nodes will be discarded bring great harm to the environment.With the gradual matures of energy collection and conversion technology, Energy Harvesting Wireless Sensor Networks (EH-WSNs) gradually get great importance and research by a lot of experts and scholars. For EH-WSNs, the energy of sensor node is no longer a limiting factor in function of network. However, there are new challenges brought at the same time. On one hand, the energy collection power of sensor nodes is fluctuating due to environments factors. The key challenge is to conduct energy consumption management so that the energy consumed by the nodes in any time period is less than the energy collected in the same time period, to maintain the energy neutral state of the nodes, so that the whole network can achieve permanent and sustainable operation. On the other hand, not only to save as much as possible network node energy consumption, and the network node energy consumption should be balanced to avoid the network node energy empty and improve the quality of network services, so that the efficiency of energy utilization of sensor node is improved at the same time. Therefore, it is necessary and feasible to study the energy consumption management mechanism for the EH-WSNs.
Assoc. Prof. Anwar Ali
Zhejiang Sci-Tech University
Research Area: Aerospace Systems, Power Electronics, Analog Electronics, Thermal Aanalysis
Dr. Anwar Ali received his M.S. degree in Electronic Engineering and Ph.D. degree in Electronic and Communication Engineering from Politecnico di Torino, Italy in 2010 and 2014, respectively. During PhD, he worked on AraMiS (Italian acronym stands for modular architecture of small satellites) project which is a joint venture between Politecnico di Torino, MIT (Boston, USA), Spin Electronics and NeOhm.
Since March 2019, he is working as Associate Professor at the School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China. He has published 40 research papers in renowned journals and conferences around the globe and has completed 5 research projects as Principle Investigator. His research interests include design and development of aerospace systems, power electronics, analog circuits and thermal analysis & thermal modeling of aerospace systems.
Keynote Speech Topic:
Mounting Power Management, Attitude Determination and Control Subsystems of a Small Spacecraft on a Single PCB
The nanosatellite market is rapidly growing for scientific and commercial applications. The main reason is the availability of low-cost commercial off-the-shelf (COTS) components and already-developed subsystems in the market. In this regard the first real NanoSatellite is CubeSat, developed by California Polytechnic State University in collaboration with Stanford University. Dimensions of 1U CubeSat are 10cm×10cm×10cm with weight upto 1.33kg. This evolution also enabled many universities and small and medium-sized enterprises (SMEs) to develop their own satellites. The problem with small satellites is the available space and weight constraints for housing a large number of required subsystems, such as power, attitude determination and control, telecommunication, and payload etc. Power management subsystem (PMS) and attitude determination & control subsystem (ADCS) are the most integral elements of any aerospace mission. Efficient PMS and precise ADCS are the core of any spacecraft mission. So keeping in mind their importance, they have been integrated and developed on a single printed circuit board (PCB) called CubePMT module. Modular power management tiles (PMTs) are already available in the market but they are less efficient, heavier in weight, consume more power and contain less number of subsystems. CubePMT is developed on the design approach of AraMiS architecture: a project developed at Politecnico di Torino that provides low-cost and higher performance space missions with modular architecture and dimensions larger than CubeSat. Utilizing the COTS approach, CubePMT extends modularity in two directions; firstly, hardware modularity which is achieved by using individual blocks for each subsystem; secondly, software modularity which is obtained by using separate coding for each subsystem. These modules can be reused for multiple missions which helps in significant reduction of the overall budget, development and testing time. One has just to reassemble the required subsystems to achieve the targeted specific mission.