Prof. Dan Zhang

(Keynote Speaker)

York University, Canada

The title of speech: Aerial Solution for Urban Traffic Problems: Overview of Autonomous Manned Aircraft

Abstract: Firstly, the prospect of Autonomous Manned Aircraft for urban air mobility is introduced. Secondly, the necessity of Autonomous Manned Aircraft autonomy is stated. The architecture and core pillars of Autonomous Manned Aircraft autonomous system are introduced. And then, combined with some cases, the research status of Autonomous Manned Aircraft autonomy is discussed. Finally, a brief introduction to Autonomous Manned Aircraft research plan and progress is presented including self-adaptative landing gear and multi-mode drone are introduced.

Biography: Dr. Dan Zhang is a Kaneff Professor and Tier 1 York Research Chair in Advanced Robotics and Mechatronics in the Department of Mechanical Engineering at York University. Dr. Zhang was a Canada Research Chair in Advanced Robotics and Automation, was a founding Chair of the Department of Automotive, Mechanical, and Manufacturing Engineering with the Faculty of Engineering & Applied Science at Ontario Tech University. He received his Ph.D. in Mechanical Engineering from Laval University, Canada, in June 2000.

Dr. Zhang's research interests include synthesis and optimization of parallel and hybrid mechanisms; generalized parallel mechanisms research; reconfigurable robots; innovation design of parallel robots: parallelization of serial robots; micro/nano manipulation and mems devices (e.g., sensors); rescue robots; smart biomedical instruments (e.g., exoskeleton robots and rehabilitation robotics); AI/robotics/autonomous systems; Aerial and Underwater Robotics; Artificial Intelligence for Robotics; intelligent reconfigurable adaptive landing gear and manipulator (manipulander).

Dr. Zhang’s contributions to and leadership within the field of robotic and automation have been recognized with several prestigious awards, within his own university (Kaneff Professorship, Tier 1 York Research Chair in Advanced Robotics and Mechatronics, Research Excellence Award both from university level and faculty level), the Province of Ontario (Early Researcher Award), the professional societies (Fellow of CAE, Fellow of the ASME, Fellow of the EIC and Fellow of the CSME), and federal funding agencies (Canada Research Chair in January 2009 and renewed in January 2014). Besides, he was awarded the Inaugural Teaching Excellence by the Faculty of Engineering and Applied Science of UOIT in 2006 and the Best Professor Award by UOIT Engineering Students' Society in 2012.

Dr. Zhang has published 217 journal papers and 182 conference papers, 12 books, 9 book chapters and numerous other technical publications. Dr. Zhang has served as a General Chair for 51 International Conferences and delivered 94 keynote speeches. Dr. Zhang is listed as the World’s Top Two Percent Researchers by Stanford’s Standardized Citation Indicators in 2020.

Dr. Zhang is a Fellow of the Canadian Academy of Engineering (CAE), a Fellow of the Engineering Institute of Canada (EIC), a Fellow of American Society of Mechanical Engineers (ASME), and a Fellow of Canadian Society for Mechanical Engineering (CSME), a Senior Member of Institute of Electrical and Electronics Engineers (IEEE), and a Senior Member of SME.

The title of speech: Study on a new structure for high sensitivity gas sensor

Abstract: Among the five sensory system, the smell sensing is developed relative slow. Focused on one of the main problems existed in current gas sensors, i.e., low sensitivity, we studied the structure and fabrication of gas sensors based on fractal geometry. We also analyzed theoretically the advantage of such kind of sensor in its specific surface area and electric field compared with traditional Euclidian geometry. We made gas sensors based on the space filling curve (Hilbert curve) combined with nanomaterial fabrication, doping, MEMS (Micro-Electro-Mechanical Systems) and micro-hotplate techniques. We also made gas sensors based on the Koch flake cube by 3D printing. Experimental results under the same sensing material/condition show that the performance of the sensor (such as sensitivity and recovering time etc.) is effectively improved compared with traditional interdigital or cylinder structures, and a new way of raising sensitivity of gas sensor is obtained.

Biography: Fengchun Tian, a professor and supervisor for Ph.D. students at Chongqing Unviersity, professional assessor of National Science Fund of China, UK and Serbia, Academic leader of Chongqing city, Excellent Backbone Teacher of Universities in Chongqing City, head of Chongqing key laboratory of Bio-perception & intelligent information processing, Chair of academic degrees committe of School of Microelectronics and Communication Engineering, Chongqing University.
Financially supported by Chinese Government as a visiting scholar in School of Engineering, University of Guelph, Canada during Oct. 2003~Oct. 2004. Short Time Trained on Computer Real-time Operating System, ISI Company, California, U.S.A. in Sept.1999.
Has been teaching and supervisor for graduates, undergraduates etc.. Experienced in instructing students’ graduation project, during which more than 80 undergraduates have been under supervising, and the same to 100 graduate students either for master degree or for doctor degree. He has finished more than 40 projects supported by national, ministerial, provincial and various research funds, published more than 100 papers and got more than 10 academic awards from the Education Ministry, Chongqing and societies. In 2020, he got the award of excellent scientific worker from Chinese Institute of Electronics. Research Interests include: Intelligent information processing, Wavelet theory and its application, Electronic nose (Machine olfaction).

Membership:

• Adjunct Professor and Special Graduate Faculty of School of Engineering, University of Guelph, Canada
• Editorial Board Member of Journal of Chongqing University (English Version).
• Senior Member of China Electronics Academic Society
• Member of International Society for Olfaction and Chemical Sensing (ISOCS)
• Member of working group for IEEE standard P2520.1

Prof. Zhiyuan Zhu
(Invited Speaker)

Southwest University, China

The title of speech: Smart sensing and analysis enabled by TENG, PIN detector and AI

Abstract: The ΔE-E telescopes based on thin and thick silicon PIN devices are widely used for detecting radiation types and particle energy. Its application in alien radiation detection should meet the requirements of portability, low energy consumption, high reliability, low cost, small size and light weight, which poses an urgent need to achieve breakthroughs in integration principles and energy supply mechanisms. We have fabricated the first 3D integrated silicon PIN device in the early stage of this project. Based on this, the integration principle of silicon PIN radiation detector based on bonding is proposed to reduce packaging weight/size, realize KGD bonding, improve reliability and reduce signal cross-talk. The fabrication process is simple, reliable and compatible with IC process. On the other hand, we have developed the first self-powered silicon PIN photodetector based on TENG by cooperating with CAS. TENG is used for harvesting the mechanical energy of the environment and provides high-voltage output for powering silicon PIN device, which reduces the energy supply cost, improves the endurance time with good reliability.  In addition, the design issue can be combined with artificial intelligence for better efficiency.

Biography: Zhiyuan Zhu is currently a Professor at Southwest University. He received the Ph.D. degree in Microelectronics and Solid-State Electronics from Peking University in January 2016. And then he joined Zhejiang University as an assistant professor. In September 2019, he joined Southwest University as a full professor. Prof. Zhu won Zhongtian Faculty Fellowship in 2018 and was awarded as the outstanding researchers at Chongqing Institute of Electronics in 2021. He is the formal Committee member of IoT Young Scientist Technical Group at Chinese Institute of Electronics. He also serves as editorial board member for Plos One, Electronics and guest editor for Sensors, Processes.

Prof. Yang Yue
(Invited Speaker)

Xi'an Jiaotong University, China

The title of speech: Multiparameter Performance Monitoring of Optical Communication Channels Using Machine Learning

Abstract: Machine learning has attracted widespread interest over the past few years in performance monitoring of the optical communication channels. In this talk, we will introduce a Visual Geometry Group (VGG)-based convolutional neural network (CNN) model with less computational cost, which is utilized to monitor optical channel performance using eye diagram measurements. Experiments show that it can determine the modulation format, optical signal to noise ratio (OSNR), roll-off factor (ROF), and timing skew with >98% prediction accuracy for 32 GBd coherent channels with quadrature phase shift keying (QPSK), 8-QAM or 16-QAM formats. Furthermore, the proposed technique can also achieve a > 97% accuracy to jointly monitor modulation format, probabilistic shaping (PS), ROF, baud rate, OSNR, and chromatic dispersion (CD) for intensity-modulation direct detection (IMDD) channels. Moreover, three other modern CNN networks are also studied, including ResNet-18, MobileNetV3 and EfficientNetV2. By contrast, the designed VGG-based model with fewer layers and the lightweight MobileNetV3 is more cost-efficient without sacrificing the accuracy

Biography: Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is a Professor with the School of Information and Communications Engineering, Xi'an Jiaotong University, China. Dr. Yue’s current research interest is intelligent photonics, including optical communications, optical perception, and optical chip. He has published over 200 peer-reviewed journal papers (including Science) and conference proceedings with >9,000 citations, five edited books, two book chapters, >50 issued or pending patents, >170 invited presentations (including 1 tutorial, >20 plenary and >30 keynote talks). Dr. Yue is a Senior Member of the Institute of Electronic and Electrical Engineers (IEEE). He is an Associate Editor for IEEE Access, and an Editor Board Member for three other scientific journals. He also served as Guest Editor for ten journal special issues, Chair or Committee Member for >80 international conferences, Reviewer for >60 prestigious journals.

Prof. Zhaoxing Gao
(Invited Speaker)

Zhejiang University, China

The title of speech: Divide-and-Conquer: A Distributed PCA Approach to Modeling Large-Scale Time Series Data

Abstract: In this talk, I will introduce a hierarchical Principal Component Analysis (PCA) approach to analyzing high-dimensional, large-scale heterogeneous time series data using distributed computing. The new method employs a multiple-fold dimension reduction procedure using PCA and shows great promises for modeling large-scale data that cannot be stored nor analyzed by a single machine. Each computer at the basic level performs a PCA to extract common factors among the time series assigned to it and transfers those factors to one and only one node of the second level. Each 2nd-level computer collects the common factors from its subordinates and performs another PCA to select the 2nd-level common factors. This process is repeated until the central server is reached, which collects factors from its direct subordinates and performs a final PCA to select the global common factors. The noise terms of the 2nd-level approximate factor model are the unique common factors of the 1st-level clusters. We focus on the case of 2 levels in our theoretical derivations, but the idea can easily be generalized to any finite number of hierarchies, and the proposed method is also applicable to data with heterogeneous and multilevel subcluster structures that are stored and analyzed by a single machine. We introduce a new diffusion index approach to forecasting based on the global and group-specific factors. Some clustering methods are discussed in the supplement when the group memberships are unknown. We further extend the analysis to unit-root nonstationary time series. Asymptotic properties of the proposed method are derived for the diverging dimension of the data in each computing unit and the sample size T. We use both simulated and real examples to assess the performance of the proposed method in finite samples, and compare our method with the commonly used ones in the literature concerning the forecasting ability of extracted factors.

Biography: Dr Zhaoxing Gao is currently a ZJU100 Young Professor at Center for Data Science, Zhejiang University. In 2016, Dr. Zhaoxing Gao obtained a Ph.D. in mathematics from the Hong Kong University of Science and Technology, and worked as a postdoctoral researcher at the London School of Economics and Political Science in the United Kingdom from 2016 to 2017 and from 2017 to 2019 at the University of Chicago Booth School of Business in the United States. Before returning to China, he worked as an assistant professor in the Department of Mathematics of Lehigh University in the United States from 2019 to 2021. The research direction mainly focuses on data analysis and machine learning methods in the economic and financial markets, statistical analysis of high-dimensional and large-scale statistics and time series data, and financial asset pricing models. The research results were published in some top Statistical and Data Science journals such as the Journal of the American Statistical Association, Journal of Econometrics, International Journal of Forecasting, Statistica Sinica and Journal of Time Series Analysis and others.