Prof. Kehua Guo, Central South University
Dr. Kehua Guo is a Professor at the School of Computer Science and Engineering at Central South University. He was selected as The National Youth Talent Support Program, Hunan Furong scholar and got the Hunan Outstanding Youth Fund. Dr. Guo received his Ph.D. in Computer Application Technology from Nanjing University of Science and Technology in 2008. He has been selected as the Chairman of computer education special committee of Hunan Higher Education Society and Secretary General of Hunan Computer Education Instruction Committee. He has long been engaged in research on artificial intelligence, big data, intelligent computing, etc. He has published more than 100 research papers in international journals or conferences. He owns 16 patents for the invention of the country. Some of his research findings have been successfully applied in industry. He is a member of the procedure Committee of many international conferences and serves as guest editors in many well-known SCI journals.
Title: Machine Learning Method for Weak Sample Multimodal Data
Abstract: With the development of machine learning, especially deep learning, the lack of data labels leads to difficulties in traditional machine learning methods. This report introduces research from Dr. Kehua Guo's team about developing machine learning methods to analyze weak sample analysis for multimodal data, and its applications i.e., small sample data enhancement, modal imbalance semantic fusion and weak sample federated learning.
Prof. Shahid Hussain, School of Materials Science and Engineering, Jiangsu University
Prof. Shahid Hussain is currently working as a professor (Full) at School of Materials Science and Engineering, Jiangsu University, China. He completed his Ph.D. degree at Chongqing University, in 2015, after starting a Post-Doctoral research fellowship from 2015 to 2017. He joined Jiangsu University as Associate Professor in July 2017 and based on his outstanding achievements and experiences, he was promoted to Full Professor in July 2020 and was also approved by the state Govt of China. Dr. Shahid Hussain and the project team has executed a lot of work in the field of metal oxide, sulfides, MXenes and MOF nanomaterials based applications in gas sensors, supercapacitors and LiS Batteries. He has published high-quality research articles, and also has a wealth of experience, which laid a solid foundation for the project related research. Dr. Shahid Hussain has excellent working experience on gas sensors and has been working on sensor device fabrication since 2011. He has published more than 241 journal research articles indexed by SCI with H-Index is 41 in Google Scholar with 5000+ citations (Till date Oct 2022) including Nano Energy, Chemical Engineering Journal, Journal of Hazardous Materials, Applied Materials & Interfaces, Journal of Materials Chemistry A, Sensors and Actuators B, Chemosphere, Inorganic Chemistry, Journal of Cleaner Production, Applied Surface Science, Electrochemica Acta, Materials Science and Engineering, etc. He is also working as an Editor for 18 journals indexed by SCI (Elsevier, Springer, Frontiers, Hindawi, American Scientific Publishers, and MDPI).
Title: New Diversities in Nanomaterials for Gas Sensing Application
Abstract: In the recent years, metal sulfide nanostructured materials have become established in different research fields thanks to their excellent properties. Among the potential applications, MXenes, MOFS and Metal sulfides may have a high standing role for gas sensing, in which, despite the wide assortment of sensing materials, all these materials maintain a leading role because of their high sensitivity, low cost, small dimensions and simple integration. Experimentation carried out in this work with MXenes and MOFs based sensors has showed an unexpected improvements of the chemoresistive properties with respect to their oxides counterparts, in particular toward selectivity to specific compounds, stability and the possibility to operate at room temperature. This opens towards the study of a novel class of sensing materials, which may solve the constant drift of the signal suffered by metal-oxides and ascribed to the in/out diffusion of oxygen vacancies, which alters the doping level.
Assoc. Prof. Kaixuan Chen, Department of Computer Science, AalborgUniversity
Research Areas: Data Mining, Machine Learning, Explainable Artifcial Intelligence, UbiquitousComputing, Brain-Computer Interface
Title: Deep Learning in Sensor-based Human Activity Recognition
Abstract: The vast proliferation of sensor devices and the Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this seminar, I will present the challenges and opportunities of studying sensor-based human activity recognition and their possible solutions.
Assoc. Prof. Sohrab Mirsaeidi, School of Electrical Engineering, Beijing Jiaotong University
Sohrab Mirsaeidi received his Ph.D. degree in Electrical Engineering from Universiti Teknologi Malaysia (UTM), Malaysia in 2016. Subsequently, he furthered his studies as a Postdoctoral Fellow at the Department of Electrical Engineering, Tsinghua University, China from 2016 to 2019. Currently, he is an Associate Professor at the School of Electrical Engineering, Beijing Jiaotong University (BJTU), China. Sohrab Mirsaeidi has published 70+ papers and 2 books in the field of Microgrids and Large-Scale Power Systems. He is a Member of the National Technical Committee of Measuring Relays and Protection Equipment Standardization of China, and has presided over and participated in several national research projects in China. He is an Editorial Board Member for several international journals and a Regular Reviewer for IEEE Transactions journals. He has also served as Chair, Keynote/Invited Speaker, and Committee Member in 70+ international conferences. His main research interests include Control and Protection of Large-Scale Hybrid AC/DC Grids and Microgrids, Application of Power Electronics in Power Systems, and Deployment of Artificial Intelligence in Power System Analysis. He is a Senior Member of IEEE, and a Member of IET, CIGRE, and Chinese Society for Electrical Engineering (CSEE).
Title: Recent Advances and Future Perspectives in Security Enhancement of LCC-HVDC Transmission Systems
Abstract: HVdc transmission technology has been extensively utilized around the world due to its numerous advantages such as increased flexibility in operation, expanded power transfer capability, and easier interconnection of energy sources. With the increasing deployment of dc transmission in the existing ac networks, the possibility of ac and dc circuits running parallel to each other and sharing the same right-of-way or even the same tower is increasing, and hence hybrid ac/dc power grids are rapidly developing. The ac and dc grids have their own characteristics; an ac grid tends to have a large inertia and well-understood responses to disturbances, while a dc grid generally has a low tolerance to fault and can respond very quickly. While the number of ac/dc interconnections gradually increases in the hybrid grids, the mutual influence between ac and dc systems becomes more pronounced, and substantially more complex dynamic and transient interactions may occur; for example, a single ac system fault may lead to simultaneous commutation failures in multiple converter stations. Therefore, one of the main challenges associated with the reliability enhancement of future hybrid ac/dc grids is to identify and analyze the newly emerging contingencies. In this talk, first, the challenges of commutation failure and cascading fault in large-scale hybrid ac/dc grids are discussed, and then the main merits and demerits of the existing approaches addressing these challenges are presented. Finally, an automatic data-sharing platform for connecting PSCAD/EMTDC and MATLAB will be introduced which can combine the main advantages of both software packages and remarkably facilitates the analysis of large-scale hybrid ac/dc grids.