Events

EEE Graduation Tea Reception 2024

The EEE Graduation Tea Reception was held in the Department on 15 November 2024 before the Congregation for the graduates of the 2024 class to take pictures and celebrate their success with their families, friends and teachers. Prof. C.Y. Chung, Head of Department and the FYP supervisors presented the certificates to the recipients of the Best Graduate Awards and the Outstanding Final Year Projects, as well as the student representatives on this special occasion. Our alumni association representatives also joined the activity to connect with our fresh graduates in this joyful event.

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/

Seminar on "Mitigating the Structural Bias in Graph Adversarial Defenses" by Mr Junyuan Fang

Date: 27 September 2024, Friday Time: 4:30 pm Venue: YEUNG-B5311, City University of Hong Kong Zoom Meeting ID: 859 2865 1236 Password: 123456 Speaker: Mr Junyuan Fang, City University of Hong Kong Mitigating the Structural Bias in Graph Adversarial Defenses Mr Junyuan Fang, City University of Hong Kong   Abstract: In recent years, graph neural networks (GNNs) have shown great potential in addressing various graph structure-related downstream tasks. However, recent studies have found that current GNNs are susceptible to malicious adversarial attacks. Given the inevitable presence of adversarial attacks in the real world, a variety of defense methods have been proposed to counter these attacks and enhance the robustness of GNNs. Despite the commendable performance of these defense methods, we have observed that they tend to exhibit a structural bias in terms of their defense capability on nodes with low degree (i.e., tail nodes), which is similar to the structural bias of traditional GNNs on nodes with low degree in the clean graph. Therefore, in this talk, we propose a defense strategy by including hetero-homo augmented graph construction, GNN augmented graph construction, and multi-view node-wise attention modules to mitigate the structural bias of GNNs against adversarial attacks. Notably, the hetero-homo augmented graph consists of removing heterophilic links (i.e., links connecting nodes with dissimilar features) globally and adding homophilic links (i.e., links connecting nodes with similar features) for nodes with low degree. To further enhance the defense capability, an attention mechanism is adopted to adaptively combine the representations from the above two kinds of graph views. We conduct extensive experiments to demonstrate the defense and debiasing effect of the proposed strategy on benchmark datasets.   Speaker’s Bio:Junyuan Fang received the B.Eng. degree in software engineering from the Guangdong University of Technology in 2018, the M.Eng. degree in software engineering from Sun Yat-sen University in 2020, and the Ph.D. degree in electrical engineering from the City University of Hong Kong in 2024. His current research interests include robustness analysis and optimization, applications of network science, and graph mining.   WEBINAR WEBSITE: http://cccn.ee.cityu.edu.hk/webinar/

Seminar on "Novel hybrid advanced traction power supply system and high-quality and efficient energy management technology" by Dr Xin Wang

Date: 13 September 2024, Friday Time: 4:30 pm Venue: YEUNG-B5311, City University of Hong Kong Zoom Meeting ID: 859 2865 1236 Password: 123456 Speaker: Dr Xin Wang, City University of Hong Kong   Novel hybrid advanced traction power supply system and high-quality and efficient energy management technology Dr Xin Wang, City University of Hong Kong   Abstract: At present, the problems of neutral section, power quality, and low energy utilization rate are the main constraints that hinder the development of traditional traction power supply systems. As a better power supply mode, the advanced traction power supply system (ATPSS) integrating with photovoltaics (PV) and energy storage (ES) based on power electronics is proposed, which provides new opportunities for simultaneously solving the above issues. Therefore, a novel hybrid-ATPSS and high-quality and efficient energy management technology is discussed in this seminar, and it will be introduced from four aspects: advanced traction power supply device, control technology, fault-tolerant operation, multi-timescale efficient energy management.   Speaker’s Bio: Xin Wang is currently an Associate Researcher at City University of Hong Kong. He was born in Henan, China, 1997. He received the B.S. degree in electrical engineering and automation from Northeast Agricultural University, Harbin, China, in 2019 and the Ph.D. degree in electrical engineering from Hunan University, Changsha, China, in 2024. His current research interests include flexible interconnection device of distribution network, advanced traction power supply system. WEBINAR WEBSITE: http://cccn.ee.cityu.edu.hk/webinar/