Events

Seminar on "Mode Interactions and their Applications in Nanophotonic Devices" by Prof Qinghai SONG

Date: 29 November 2024, Friday Time: 3:30pm Venue: DE309, The Hong Kong Polytechnic University Speaker: Prof Qinghai SONG, Harbin Institute of Technology (HIT), Shenzhen

Seminar on "How Collective Intelligence Emerges in a Crowd of People Through Learned Division of Labor" by Prof. Hongwei Zhang

Date: 22 November 2024, Friday Time: 4:30pm Venue: YEUNG-B5311, City University of Hong Kong Zoom Meeting ID: 859 2865 1236 Password: 123456 Speaker: Prof. Hongwei Zhang, Harbin Institute of Technology, Shenzhen   How Collective Intelligence Emerges in a Crowd of People Through Learned Division of Labor Prof. Hongwei Zhang, Harbin Institute of Technology, Shenzhen   Abstract: In this talk, we will investigate the factors fostering collective intelligence (CI) through a case study of LinYi’s Experiment, where over 2000 human players collectively controll an avatar car. By conducting theoretical analysis and replicating observed behaviors through numerical simulations, we show how self-organized division of labor among individuals fosters the emergence of CI and identify two essential conditions fostering CI by formulating this problem into a stability problem of a Markov Jump Linear System. These conditions, independent of external stimulus, emphasize the importance of both elite and common players in fostering CI. Additionally, we propose an index for emergence of CI and a distributed method for estimating joint actions, enabling individuals to learn their optimal social roles without global information of the whole crowd. Speaker’s Bio: Hongwei Zhang received the Ph.D. degree in mechanical and automation engineering from the Chinese University of Hong Kong in 2010. Subsequently, he held postdoctoral positions at the University of Texas at Arlington and the City University of Hong Kong. He held a professorship at Southwest Jiaotong University from 2012 to 2020, and then joined Harbin Institute of Technology, Shenzhen, China in 2020 as a Professor. His research interests include cooperative control of multi-agent systems, distributed control of microgrids, and active noise control. He is an Associate Editor of Neurocomputing.   WEBINAR WEBSITE: https://www.ee.cityu.edu.hk/~cccn/webinar/    

Seminar on "Lifting for Nonlinear Systems and Model Predictive Control" by Prof. Yutaka Yamamoto

Date: 15 November 2024, Friday Time: 4:30pm Venue: YEUNG-B5311, City University of Hong Kong Zoom Meeting ID: 859 2865 1236 Password: 123456 Speaker: Prof. Yutaka Yamamoto, Kyoto University, Japan   Lifting for Nonlinear Systems and Model Predictive Control Prof. Yutaka Yamamoto, Kyoto University, Japan   Abstract:It is well recognized that the lifting technique has played a crucial role in modernizing the theory of sampled-data control. Unfortunately, this superb idea does not easily carry over to the nonlinear systems due to the outputs depending nonlinealy both on inputs and states. This talk intends to circumvent this difficulty by lifting even the state trajectories. While this can induce some difficulties, it still helps us to formalize nonlinear sampled-data control systems while maintaining intersample behavior - same advantage enjoyed in linear systems. We will give fast-sample/fast-hold approximation formulas to take care of computational difficulties, and then apply it to model predictive control. Simulation results show that the proposed method exhibits an advantage in controlling the intersample behavior over the normal model predictive control focused on sample-point behavior.   Speaker’s Bio:Yutaka Yamamoto received his B.S. and M.S. degrees in engineering from Kyoto University, Kyoto, Japan in 1972 and 1974, respectively, and the M.S. and Ph.D. degree in mathematics from the University of Florida, in 1976 and 1978, respectively. From 1978 to 1987 he was with Department of Applied Mathematics and Physics, Kyoto University. In 1987 he joined the Department of Applied Systems Science as an Associate Professor, and became a professor in 1997. He had been a professor at the Department of Applied Analysis and Complex Dynamical Systems, Graduate School of Informatics of Kyoto University until 2015. He is now Professor Emeritus of Kyoto University. His research and teaching interests are in realization and robust control of distributed parameter systems, learning control systems, and sampled-data systems, its application to digital signal processing, with emphasis on sound and image processing.   He received Sawaragi memorial paper award in 1985, outstanding paper award of SICE in 1987 and in 1997, the best author award of SICE in 1990 and in 2000, the George S. Axelby Outstanding Paper Award in 1996, and the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Prizes for Science of Technology in 2007. He received the IEEE Control Systems Society Distinguished Member Award in 2009, and the Transition to Practice Award of the Control Systems Society in 2012, as well as the ISCIE Best Industrial Paper Award in 2009. He received the Tateishi Prize of the Tateishi Science and Technology Foundation in 2015. He is a Fellow of the IEEE, IFAC and SICE.   He served as President of the IEEE Control Systems Society for 2013. He served as vice President for Technical Activities of the CSS for 2005-2006, and as vice President for Publication Activities for 2007-2008. He was an associate editor of the IEEE Transactions on Automatic Control, Automatica, Systems and Control Letters, Mathematics of Control, Signals and Systems. He served as a Senior Editor for the IEEE Transactions on Automatic Control for 2010-2011. He also served as an organizing committee member of 35th CDC in 1996, MTNS91 in Kobe, and as a member of program committees of several CDC’s. He was the chair of the Steering Committee of MTNS, served as General Chair of MTNS 2006. He is a past President of ISCIE of Japan.   WEBINAR WEBSITE: https://www.ee.cityu.edu.hk/~cccn/webinar/  

Seminar on "Linear Quantum Systems: Poles, Zeros, Invertibility and Sensitivity" by Dr Guofeng Zhang

Date: 8 November 2024, Friday Time: 4:30pm Venue: YEUNG-B5311, City University of Hong Kong Zoom Meeting ID: 859 2865 1236 Password: 123456 Speaker: Dr Guofeng Zhang, The Hong Kong Polytechnic University   Linear Quantum Systems: Poles, Zeros, Invertibility and Sensitivity Dr Guofeng Zhang, The Hong Kong Polytechnic University     Abstract:The non-commutative nature of quantum mechanics imposes fundamental constraints on system dynamics, which in the linear realm, are manifested through the physical realizability conditions on system matrices. These restrictions give system matrices a unique structure. In this talk I discuss this structure by investigating the zeros and poles of linear quantum systems. Firstly, I show that  -s_0 is a transmission zero if and only if  s_0 is a pole of the transfer function, and -s_0  is an invariant zero if and only if  s_0  is an eigenvalue of the  A-matrix, of a linear quantum system. Moreover,  s_0 is an output-decoupling zero if and only if -s_0 is an input-decoupling zero. Secondly, based on these zero-pole relations, we prove that a linear quantum system must be Hurwitz unstable if it is strongly asymptotically left invertible. Stable input observers are constructed for unstable linear quantum systems. Finally, the sensitivity of a coherent feedback network is investigated. We found that the well-known complementarity constraint between sensitivity and complementary sensitivity functions no longer holds in the quantum regime; instead, much richer fundamental performance limitations exist. The  fundamental tradeoff between ideal input squeezing and system robustness is studied on the basis of system sensitivity analysis..     Speaker’s Bio:Guofeng Zhang received the Ph.D. degree in applied mathematics from the University of Alberta, Edmonton, AB, Canada, in 2005. He joined the University of Electronic Science and Technology of China, Chengdu, China, in 2007. He joined the Hong Kong Polytechnic University, Hong Kong, in December 2011, and is currently an Associate Professor. His research interests include quantum control and tensor-based quantum computing. WEBINAR WEBSITE: https://www.ee.cityu.edu.hk/~cccn/webinar/    

Seminar on "V2I-aided zk-SNARK for Travel Records Verification of Electric Vehicles" by Mr Cao Ding

Date: 1 November 2024, Friday Time: 4:30pm Venue: CD634, The Hong Kong Polytechnic University Zoom Meeting ID: 383 735 6917 Password: 270831 Speaker: Mr Cao Ding, The Hong Kong Polytechnic University   V2I-aided zk-SNARK for Travel Records Verification of Electric Vehicles Mr Cao Ding, The Hong Kong Polytechnic University     Abstract:The rapid increase in the number of electric vehicles (EVs) has resulted in huge fuel tax losses for governments every year. Many countries have levied taxes based on the annual or monthly travel record (TR) submitted by the EV. On the one hand, TR contains important private information, such as the time, locations, and trajectories of EV owners. On the other hand, EV owners may forge TR to reduce taxes. Therefore, the verification protocol of TR requires extremely high security and effectiveness. To solve this outstanding issue, this paper proposes a V2I-SNARK protocol that combines vehicle-to-infrastructure communications (V2I) and zk-SNARK for TR verification of EVs. V2I-SNARK is divided into two stages, the trusted setup stage and the TR verification stage. In the former stage, a trusted authority (TA) will generate the proof key and verification key for verification and store them on the verification server (Verifier). In the latter stage, EV will use the proof key to generate a randomized proof, and the verifier will use the verification key to verify the proof. Regarding the performance of the V2I-SNARK protocol, we first provide security proofs for completeness, soundness, and zero-knowledge properties. Furthermore, we compare the verification efficiency, energy consumption, computational complexity, and other performance of V2I-SNARK with the benchmark protocols. The results show that the proposed V2I-SNARK protocol outperforms other protocols in terms of verification efficiency and energy consumption.   Speaker’s Bio:Cao Ding received the B.Eng. degree in automation from Beijing University of Chemical Technology, Beijing, China, and the M.Sc. degree in electronic and information engineering from The Hong Kong Polytechnic University, Hong Kong. He is now a Ph.D. student of The Hong Kong Polytechnic University. His research interests include vehicular networks and intelligent transport systems (ITS), specifically in vehicular ad hoc network (VANET). His research focuses most on the digital twin of vehicular networks and intelligent transport systems.   WEBINAR WEBSITE: https://www.ee.cityu.edu.hk/~cccn/webinar/  

Seminar on "Dynamic Output Feedback Stabilization of Switched Linear Autonomous Systems: A Hybrid Observer Approach" by Dr Miaomiao Wang

Date: 16 October 2024, Wednesday Time: 11:00am Venue: YEUNG-B5311, City University of Hong Kong Zoom Meeting ID: 859 2865 1236 Password: 123456 Speaker: Dr Miaomiao Wang, Hong Kong University of Science and Technology   Dynamic Output Feedback Stabilization of Switched Linear Autonomous Systems: A Hybrid Observer Approach Dr Miaomiao Wang, Hong Kong University of Science and Technology   Abstract:For switched linear autonomous systems, feedback stabilization is to seek a proper switching strategy to steer the system so that it is exponentially stable. The problem has been proven to be nonconvex, not finitely representable, and automaton-supervision denied. This presentation focuses on dynamic output feedback switching design for stabilization of continuous-time switched linear autonomous systems. Under the assumption that the system is switched observable, we propose a novel hybrid observer that can recover the system state in any given time interval. For any stabilizable switched system, by incorporating the observer into a pathwise feedback switching mechanism, an observer-driven switching law can be designed to achieve exponential stability of the system.     Speaker’s Bio:Miaomiao Wang received the B.S. degree in Statistics from Central South University in 2015, and the Ph.D. degree in Systems Theory from the University of Chinese Academy of Sciences in 2020. >From 2020 to 2024, she was a postdoctoral fellow with the Key Laboratory of Systems Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences. Since 2024, she has been with the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, as a Postdoctoral Fellow. Her research interests are focused on the analysis and design of switched and hybrid systems, and the analysis and optimization of networked control systems. WEBINAR WEBSITE: https://www.ee.cityu.edu.hk/~cccn/webinar/

Seminar on "Optimal adaptive output regulation for discrete-time nonlinear stochastic systems" by Dr Zhaobo Liu

Date: 18 October 2024, Friday Time: 4:30 pm Venue: YEUNG-B5311, City University of Hong Kong Zoom Meeting ID: 859 2865 1236 Password: 123456 Speaker: Dr Zhaobo Liu, Shenzhen University   Optimal adaptive output regulation for discrete-time nonlinear stochastic systems Dr Zhaobo Liu, Shenzhen University     Abstract: In this presentation, we address the output regulation problem associated with a basic class of discrete-time nonlinear stochastic systems with unknown parameters. We allow the controlled plant to exhibit highly nonlinear growth, as well as nonlinearly parameterized structures in the exosystem. Our main purpose is to design an adaptive regulator to achieve the optimal performance of the regulated output. The design of the regulator is an integration of the recursive least squares estimator and a modified nonlinear least squares (NLS) algorithm. Under certain mild conditions, it is shown that with stable exosystems, the square of the norm of the closed-loop regulated output is asymptotically optimal in the average sense almost surely. Specifically, we propose a new formula for the strong convergence rate of NLS and derive an almost optimal strong convergence rate of the proposed modified NLS algorithm. Additionally, we employ novel closed-loop analysis techniques to overcome the Bayesian assumptions required in existing research on the stabilizability of discrete-time nonlinear stochastic systems.   Speaker’s Bio:Zhaobo Liu is currently an Assistant Professor at the Institute for Advanced Study, Shenzhen University. He received the B.Sc. degree from the School of Mathematical Sciences, Peking University, in 2015, and the Ph.D. degree from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, in 2020. His current research interests include adaptive control, data science, and computational intelligence. WEBINAR WEBSITE: http://cccn.ee.cityu.edu.hk/webinar/    

Seminar on "Can Graph Neural Networks Become More Interpretable?" by Dr Giorgia Minello

Date: 16 October 2024, Wednesday Time: 11:00am Venue: CD634, Department of Electrical and Electronic Engineering, PolyU Speaker: Dr Giorgia Minello, Shenzhen University

Seminar on “Encapsulation of perovskite solar cells” conducted by Prof. Aleksandra B. Djurišić.

Date: 7 October 2024 (Mon) Time: 3:30pm-5:00pm Venue: CD634, Department of Electrical and Electronic Engineering, PolyU Speaker: Prof. Aleksandra B. Djurišić, Department of Physics, The University of Hong Kong

Seminar on "Differentially Private Graph Neural Networks for Link Prediction" by Mr Xun Ran

Date: 4 October 2024, Friday Time: 4:30 pm Venue: CD634, The Hong Kong Polytechnic University Zoom Meeting ID: 383 735 6917 Password: 270831 Speaker: Mr Xun Ran, The Hong Kong Polytechnic University Differentially Private Graph Neural Networks for Link Prediction Mr Xun Ran, The Hong Kong Polytechnic University   Abstract:Graph Neural Networks (GNNs) have proven to be highly effective in addressing the link prediction problem. However, the need for large amounts of user data to learn representations of user interactions raises concerns about data privacy. While differential privacy (DP) techniques have been widely used for node-level tasks in graphs, incorporating DP into GNNs for link prediction is challenging due to data dependency. To this end, in this work we propose a differentially private link prediction (DPLP) framework, building upon subgraph-based GNNs. DPLP includes a DP-compliant subgraph extraction module as its core component. We first propose a neighborhood subgraph extraction method, and carefully analyze its data dependency level. To reduce this dependency, we optimize DPLP by integrating a novel path subgraph extraction method, which alleviates the utility loss in GNNs by reducing the noise sensitivity. Theoretical analysis demonstrates that our approaches achieve a good balance between privacy protection and prediction accuracy, even when using GNNs with few layers. We extensively evaluate our approaches on benchmark datasets and show that they can learn accurate privacy-preserving GNNs and outperforms the existing methods for link prediction.   Speaker’s Bio:Mr Xun RAN is currently pursuing his Ph.D. degree in the Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong. His research interests include data privacy and neural networks. WEBINAR WEBSITE: http://cccn.ee.cityu.edu.hk/webinar/