This CPD course will feature presentations by Prof. Dr.- Ing. Stefan Tenbohlen from the Institute of Power Transmission and High Voltage Technology at the University of Stuttgart in Germany. This course will deliver the theoretical background information necessary to develop a good understanding of the phenomena of partial discharge (PD) in high-voltage equipment, as well as current measurement techniques for partial discharge. Along with this, the course will look at the experience of industry using partial discharge for diagnostic and testing work. Using the high-voltage laboratory at the University of Queensland, there will be demonstrations of some key measurement techniques for partial discharge.
This two-day face-to-face course will bring industry professionals together for dialogue and sharing of knowledge to better understand the operation of bushings for power transformers, as well as the design, maintenance and testing thereof.
Don't miss the 2024 Innovation Showcase, a unique opportunity for EECS students to showcase their end of year projects to industry partners and the UQ community.
In this talk, I will introduce Linear-Time Graph Neural Network (LTGNN), a breakthrough approach designed to bridge the scalability gap between GNNs and simpler methods without sacrificing GNNs’ unique ability to capture distant dependencies.
This two-day face-to-face course will bring industry professionals together for dialogue and knowledge sharing to better understand the renewable energy technologies and their integration regarding renewable generator modelling, control techniques, frequency and voltage regulation aligning with grid codes.
This two-day face-to-face course will bring industry professionals together for dialogue and sharing of knowledge to better understand the fundamentals of power systems along with its modelling and operational aspects.
In this talk, I will discuss an alternative approach through inverse rendering, which enables machine learning models to extract explicit physical representations from raw, unstructured image data, such as Internet photos and videos.
This presentation embarks on a comprehensive exploration of the VLN trajectory, tracing its inception to seminal benchmarks such as Room-to-Room (R2R).
In this talk, we will introduce: (1) The long-term technical goal will the GFMs serve (2) The knowledge gap in the graph domain the GFMs can fill (3) The critical problem GFMs can solve.
Searching the gigantic corpus of online podcasts involves multiple challenges ranging from content and style diversity to expensive audio processing to variable length. In this thesis, we aim to address these challenges and devise novel approaches to improve state-of-the-art performance.
This presentation proposes a series of compression approaches to reduce the computation complexity and memory usage of deep models for efficiency improvement.
Entity Alignment (EA) is crucial for integrating heterogeneous knowledge graphs (KGs) into a unified knowledge base by identifying equivalent entities across them.
This presentation explores the comparative efficacy and influencing factors of three major platforms: Search Engines, Symptom Checkers, and Large Language Model (LLM)-powered Conversational Agents.
Extensive experiments on public benchmark datasets have verified the effectiveness of the two proposed works in retaining excellent recommendation performance.
To ensure DNNs effectively retain past knowledge while accommodating future tasks, we explore CL techniques from the viewpoint of augmenting and modularizing the memorization of DNNs.
In this work, we use MetaMap to map the original dataset, which lacks ground truth, with knowledge from drug-drug interaction databases, thereby deriving the ground truth dataset.
