Prof. Yajun Liu South China University of Technology, China | Bio: Prof. Yajun Liu was born on September 20, 1974 in Jiangxi, China. Native speaker of Chinese, fluent in English. His Education and Academic Research Experiences is as follows: December, 2016- Now Professor in South China University of Technology School of Mechanical and Automotive Engineering. December, 2009- December, 2010. Visiting Professor in Fluid Power Research Center (FPRC) Purdue University at West Lafayette, USA. Feb, 2005 – July, 2016. Post-doctoral Research Fellow, Tokheim JV company in China. June, 2002 Ph. D. in Mechanical Engineering. South China University of Technology, Guangzhou,China. His research interests include Digital signal processing technology and its application in mechanical systems (such as hydraulic System for Energy Saving.); Intelligence control and Manufacturing Engineering. Moreover, Prof. Yajun Liu has published more than270 papers in Journals and proceedings of international conferences. 40+ patents on Mechanical System design and manufacturing. TItle：An Intelligent Fire Detection Technology Based on Acceleration Signal and Machine Learning Abstract：Fire is a common and destructive disaster in modern society, and traditional fire detection methods have limitations in terms of accuracy and speed. In this study, an artificial intelligence-based fire detection technique is proposed, which utilizes the vibration features of fireproof materials during combustion. Signal processing techniques, such as time-domain analysis and wavelet packet decomposition, are used to analyze the acceleration signals generated during burning and identify unique features that distinguish fire signals from other disturbances. Machine learning algorithms are then applied to train the feature data and perform parameter tuning to optimize the detection performance. The effectiveness of the method is validated through simulated fire experiments, demonstrating that the technique can detect actual fire signals more quickly and accurately than traditional methods. This proposed method provides a new perspective for fire detection technology and has the potential to minimize the damage caused by fires. |
Prof. Haijun Zhang Harbin Institute of Technology, China | Haijun Zhang received the B.Eng. and Master’s degrees from Northeastern University, Shenyang, China, and the Ph.D. degree from the Department of electronic Engineering, City University of Hong Kong, Hong Kong, in 2004, 2007, and 2010, respectively. He was a Post-Doctoral Research Fellow with the Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada, from 2010 to 2011. Since 2012, he has been with the Shenzhen Graduate School, Harbin Institute of Technology, China, where he is currently a Professor of Computer Science. His current research interests include data mining, machine learning, generative AI, fashion intelligence, and service computing. He published over 170 technical papers in international journals and conferences. Prof. Zhang is currently an Associate Editor of IEEE Trans. on Consumer Electronics, etc. Title：Generative Fashion Intelligence Abstract: "Clothing," as one of the basic needs, plays an important role in human life. Following the emergence of the concept of the metaverse, generative large models such as GPT, as new tools marking the milestone of civilian AI, have attracted extensive attentions in both academia and industrial practitioners. This talk will cover the topics and basic tasks of fashion intelligence associated with its recent progresses. In particular, this talk showcases the trends and applications of fashion intelligence driven by multi-modal large language models, such as image and video generations. Moreover, the speaker will report some results from his research group, including controllable editing, collocation generation and recommendation, Mannequin2Real applications, cross-scene object layout, etc. At last, this talk will give some insights on the future prospects of fashion AI. |
Prof. Mouquan Shen Nanjing Tech University, China | Bio: Professor, the "Six Talent Peaks" of Jiangsu Province. Postdoctoral at Southeast University, visiting scholar at overseas universities such as the University of Hong Kong, Yeungnam University, South Korea, and the University of Adelaide, Australia. He is the PI of more than 10 provincial-level projects, including the National Natural Science Foundation of China, the National Bureau of Foreign Experts Affairs, and the Jiangsu Provincial Natural Science Foundation. In recent years, more than 100 papers with an H-index of 24 have been published in journals such as IEEE Transactions on · Automatic Control, IEEE Transactions on Cybernetics, and IEEE Transactions on Systems, Man, and Cybernetics: Systems. He has been appointed Editor-in-Cheif, Associate Editor, or Editorial Board Member of 12 international journals. He is also an active reviewer of over 80 domestic and international journals, including IEEE, TAC, and Automatica, as well as the corresponding reviewer for the National Natural Science Foundation of China and multiple provincial and municipal science and technology projects. TItle：Key techniques of data driven optimal control under network environment Abstract：This talk focuses on data-driven optimal control under network environment with some important techniques and the corresponding solutuions. Based on the modified index, two optimal data-driven control laws are provided by employing adaptive dynamic programming method and Q-learning algorithm, respectively. Two novel event-triggered mechanisms are constructed by utilizing instantaneous and averaged data as well as performance cost, respectively. Sufficient conditions are developed to ensure the ultimately uniform boundedness of the resultant systems. Finally, some examples are presented to verify the effectiveness of the proposed schemes. |
Prof. Steven Guan Xi'an Jiaotong-Liverpool University (XJTLU), China | Steven Guan received his BSc. from Tsinghua University and M.Sc. & Ph.D. from the University of North Carolina at Chapel Hill. He is currently a Professor at Xi'an Jiaotong-Liverpool University (XJTLU) and Honorary Professor at University of Liverpool. 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 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 140+ journal papers and 200+ book chapters or conference papers. He has chaired, delivered keynote speech for 100+ 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, Self-Modifiable 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. Title：Recursive Learning of Genetic Algorithms for Classification Problem Solving Abstract：Three recursive domain decomposition approaches combined with task decomposition are proposed to tackle the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. Incremental partitioning based upon hypercubes, hyperplanes, and hyperspheres are considered for recursive domain decomposition. Hypercubes, hyperplanes, or hyperspheres that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithms (GAs). We solve classification problems through a simple and flexible chromosome encoding scheme. A new method - Incremental Linear Encoding based Genetic Algorithm (ILEGA) is also proposed for the proposed hyperplane approach, where the partitioning rules are encoded by linear equations rather than If-Then rules. These incremental learning algorithms are tested with benchmarks and some artificial datasets. The experimental results show that such recursive domain decomposition approaches outperform in both lower- and higher-dimensional problems compared with the original GA. |
