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X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART:20250101T000000
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DTSTART;TZID=Asia/Hong_Kong:20260508T143000
DTEND;TZID=Asia/Hong_Kong:20260508T153000
DTSTAMP:20260511T050650
CREATED:20260430T030808Z
LAST-MODIFIED:20260430T031009Z
UID:115806-1778250600-1778254200@ece.hku.hk
SUMMARY:RPG Seminar – Novel Two-Dimensional (2D) Memory Devices: From Material Innovation to Functional Integration
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/92285676627?pwd=oaba8ue6daTbmrDBhxaIhYsykCedbR.1 \nAbstract\nDuring the period of the Internet of Things (IoT) and big data\, data capacity is growing exponentially\, the delay and loss caused by data transmission make the traditional von Neumann computing architecture urgently need to be overturned and restructured. This has led to a growing demand for emerging memory devices\, posing significant challenges to conventional silicon-based memory technologies. In recent years\, two-dimensional (2D) materials leveraging their unique physical properties\, ultra-thin thickness and no dangling bonds have been wide-ranging used in the fabrication of various electronic and optoelectronic memory devices. In this seminar\, we first briefly introduce several mainstream types of 2D memory devices developed in recent years and their working mechanisms. We then propose a 2D infrared-sensing memory device (ISMD) based on the Se0.3Te0.7/CuInP2S6 (CIPS) heterostructure\, where Se0.3Te0.7 serves as the channel and CIPS functions as the ferroelectric auxiliary layer. The coupling between interfacial defect trapping and ferroelectric polarization endows the device with non-volatile multi-bit memory capability programmable by electrical pulses. Meanwhile\, the device exhibits a transient infrared response at 1550 nm\, with its responsivity negatively correlated to the channel conductance. By integrating sensing\, memory\, and computing in a single device\, this work broadens the research scope of 2D memory devices. \nSpeaker\nMr. Xuyang ZHENG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nXuyang Zheng is an MPhil student in the Department of Electrical and Computer Engineering at The University of Hong Kong\, supervised by Prof. Can Li. He received his B.S. degree in Functional Materials from South China University of Technology (SCUT) in 2024. His research interests include 2D memory devices for neuromorphic computing. \nOrganiser\nProf. Can LI \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260508/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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DTSTART;TZID=Asia/Hong_Kong:20260508T160000
DTEND;TZID=Asia/Hong_Kong:20260508T170000
DTSTAMP:20260511T050650
CREATED:20260504T020614Z
LAST-MODIFIED:20260504T020614Z
UID:115819-1778256000-1778259600@ece.hku.hk
SUMMARY:RPG Seminar – Day–Night Mechanism-Aware Causal Modeling for Wind Power Forecasting: A Physics-Guided NLSEM Framework
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/95263844640 \nAbstract\nWind power forecasting remains highly challenging due to the strong nonlinearity of atmospheric dynamics\,pronounced diurnal regime differences\, and substantial uncertainties in multi-source meteorological data. Conventional black-box machine-learning models mainly rely on observational correlations\, often neglecting physical constraints and causal mechanisms\, which leads to limited interpretability and poor robustness under distribution shifts. In this seminar\, we proposes a unified forecasting framework that integrates physics-constrained data construction\, multi-site causal structure learning\, and a global nonlinear structural equation model (NLSEM). The framework combines full-variable Granger causality networks with PCMCI+ to identify distinct day and night-time causal directed acyclic graphs (DAGs). Within the NLSEM\, physical monotonicity\, environmental invariance\, and counterfactual-consistency regularization are explicitly enforced. The resulting model supports causal inference through dointervention analysis\, ATE/CATE estimation\, and counterfactual reasoning. Experiments conducted on three coastal wind farms demonstrate consistent performance improvements over strong machine-learning baselines\, while revealing physically meaningful causal drivers of wind-power generation. \nSpeaker\nMr. Yuxuan WANG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYuxuan Wang received the B.S. degree in New Energy Science and Engineering from Huazhong University of Science and Technology\, and the M.S. degree in electrical engineering from University of Leeds. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His current research interests include wind power forecasting\, causal inference\, and nonlinear modeling for renewable energy systems. \nOrganiser\nProf. Yunhe HOU \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260508-2/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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