Keynote Speaker I
Dr. Pushpendu Kar
University of Nottingham Ningbo China
Research Area: Internet of Things, Wireless Sensor Networks, Content Centric Networking
Bio:
Dr. Pushpendu Kar is an Assistant Professor in the School of Computer Science at the University of Nottingham Ningbo China (China campus of the University of Nottingham UK). Before this, he was a Research Fellow in the Department of ICT and Natural Sciences at Norwegian University of Science and Technology (NTNU), Norway, the Department of Electrical & Computer Engineering at National University of Singapore (NUS) and the Energy Research Institute at Nanyang Technological University (NTU), Singapore. He has completed all his PhD, Masters of Engineering, and Bachelor of Technology in Computer Science and Engineering. He also completed Sun Certified Java Programmer (SCJP) 5.0, one professional course on Hardware & Networking, two professional courses on JAVA-J2EE, Finishing School Program from National Institute of Technology Durgapur, India and UGC sponsored refreshers course from Jadavpur University, India. Dr. Kar went for a research visit to Inria Paris, France. Dr. Kar was awarded the prestigious Erasmus Mundus Postdoctoral Fellowship of European Commission, ERCIM Alain Bensoussan Fellowship of European Union, and SERB OPD Fellowship of Department of Science and Technology, Government of India. He received the 2020 IEEE Systems Journal (2020 I.F.: 4.463) Best Paper Award. This is one of the seven papers out of 793 papers [top 1%] that have received the award. Dr. Kar is an IEEE Senior Member. He received three research grants as Principal Investigator for conducting research-based projects. He also received several travel grants to attend conferences and doctoral colloquiums. Dr. Kar has more than 12 years of teaching and research experiences, including in a couple of highly reputed organizations around the world. He worked as a software professional in IBM for one and a half years. Dr. Kar is the author of more than 35 scholarly research papers, which has published in reputed journals including ACM TAAS, IEEE TNSM, IEEE Systems Journal, IEEE Sensors Journal, Journal of Building and Environment, conferences including ICC, TENCON, IECON, and IT magazines. He has published two edited books. He is also an inventor of four patents. He has participated in the program committee of several conferences, worked as a team member to organize short term courses, and delivered few invited talks. He is a regular reviewer of IEEE, Elsevier, Wiley, and Springer journals and conferences.
Title: Recommendation based approach for personalized control, Visual Comfort & Energy Efficiency in office buildings
Abstract:
Built-environment, especially open-plan workplaces are often not tailored to meet individual visual comfort needs. Therefore, meeting the need for personalized visual comfort whilst achieving energy efficiency in an open-plan office environment has been an open challenge. However, recent technological advancements in distributed sensing, pervasive computing, context-awareness, and machine learning are progressively closing this gap. We proposed a simple recommender systems-based approach to learn both individual and collaborative user preferences from historical data and offer recommendations for intelligent building lighting controls. The intelligence, in this case, is achieved by being able to derive set points to control task lights such that it balances personalized visual comfort without compromising on energy savings. The proposed approach has been developed using Python and implemented on a real test-bed in a university campus office building at the National University of Singapore. The evaluation of the proposed approach is carried out for two months using field experiments involving distributed Wireless Sensor-Actuator Networks (WSANs) and multiple occupants having varied visual sensations. The novelty lies in proposing a new interdisciplinary approach that supports smart and intelligent buildings paradigm by learning and predicting optimum individual user preferences towards energy-efficient control of personalized light. The results obtained from field experiments present potential energy savings up to 72% when compared to the conventional lighting systems used.
Keynote Speaker II
Assoc. Prof. Gang Liu
Harbin Engineering University, China
Research Area: Artificial Intelligence，Machine Learning，Natural Language Processing
Bio:
Gang Liu, Ph.D., Professor, focuses on artificial intelligence, natural language processing, data mining, and machine learning. Born in September 1976, he began working in the College of Computer Science and Technology of Harbin University of Engineering in 1999. Visiting scholar at the University of Illinois at Urbana(UIUC) in 2005 and visiting scholar at Monash University in Australia in 2014. Expert of the Information Construction Professional Committee of China Education Logistics Association. IEEE member, ACM member, CCF senior member, member of the Chinese Artificial Intelligence Society and member of Chinese Information Society of China. For a long time, he has been engaged in teaching and scientific research in universities such as software and theory, pattern recognition and intelligent systems. To undertake 1 project of the National Natural Science Foundation, 1 project of the national key research and development plan, 2 projects of the National Science and Technology Support Plan, 1 project of the Ministry of Education Planning Fund, 2 project of the National Key Laboratory Open Fund, 1 project of the China Postdoctoral Science Fund, and 1 project of the 12th Five-Year Plan in Heilongjiang Province. Won the second prize of China's information technology achievements, Heilongjiang Province, science and technology progress second prize 1, third prize 2, 8 patents, 14 software copyrights. More than 50 academic papers were published in foreign academic journals and IEEE international academic conferences, and more than 30 were retrieved by SCI and EI. 5 monographs and teaching materials published.
Title: Mining and Dissemination Mechanism of Policy Lineage Network for Defragmentation
Abstract:
With the vigorous development of the national economy, a large number of new policies have emerged, and the trend of fragmentation among policies has become increasingly obvious. The policy formation process has gradually evolved into a process of collisions and conflicts among various "fragmented policies", which is quite different from the relative consistency of policies. In the process of analyzing the mechanism of fragmentation, we are faced with a series of problems such as the comparative explosion of policy texts, the discovery of hidden deep relationships, and the determination of key points of the system.
We propose a method of combining policy networks and policy lineages, and study the technology of policy lineage network construction from the aspects of policy feature dimensionality reduction, factor decomposition, and communication evolution. The main contents of this report include: (1) Policy modeling and discourse Similarity measurement; (2) Conceptual factor decomposition and commonality extraction mechanism; (3) Latent gene nature of policy blood relationship; (4) Communication mechanism and dynamic mechanism of blood lineage network; (5) Empirical analysis and application verification.
Through the above technical theoretical research, enrich and develop the policy network theory and its application, study the potential similar mechanisms of policies, discover the distribution law of policy hidden lineage relationship, establish the policy lineage network model, explore the key points of the policy network analysis method, and have broad application prospects.
Keynote Speaker Ⅲ
Assoc. Prof. Wei Wei
Xi’An University of Technology, China
Research Area: Internet of Things, Artificial Intelligence, Big Data Processing
Bio:
In 2011, he received a doctorate degree in computer software and theory from Xi’an Jiaotong University. In 2009, he completed a one-year return to school at the University of Nebraska. In 2015, he completed the electrical postdoctoral research work at Xi’an University of Technology. In 2017, he did not have a University of Texas. The Department of Computer Science of the University of Dallas visited and completed post-doctoral research. He has been engaged in research on the Internet of Things, artificial intelligence, big data processing and other related aspects, published more than 80 research papers, presided over the completion of 3 ministerial-level funds, won 58 provincial and municipal scientific and technological progress awards as a backbone, and participated in the completion of the country as a backbone 6 fund projects. In 2019 and 2020, they won the second prize of scientific and technological progress in Shaanxi universities. As a senior member of IEEE, he served as the official editor and reviewer of many high-level journals.
Title: Gradient-Driven Parking Navigation Using a Continuous Information Potential Field Based on Wireless Sensor Network
Abstract:
Wireless sensor networks can support building and transportation system automation in numerous ways. An emerging application is to guide drivers to promptly locate vacant parking spaces in large parking structures during peak hours. This paper proposes efficient parking navigation via a continuous information potential field and gradient ascent method. Our theoretical analysis proves the convergence of a proposed algorithm and efficient convergence during the first and second steps of the algorithm to effectively prevent parking navigation from a gridlock situation. The empirical study demonstrates that the proposed algorithm performs more efficiently than existing algorithms.