Search Results for author:
Found 29 papers, 0 papers with code
PI-Controlled Variable Time-Step Power System Simulation Using an Adaptive Order Differential Transformation Method
Dynamic simulation plays a crucial role in power system transient stability analysis, but traditional numerical integration-based methods are time-consuming due to the small time step sizes.
Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
Extreme hazard events such as wildfires and hurricanes increasingly threaten power systems, causing widespread outages and disrupting critical services.
Spatio-Temporal Conformal Prediction for Power Outage Data
In recent years, increasingly unpredictable and severe global weather patterns have frequently caused long-lasting power outages.
Hierarchical Spatio-Temporal Uncertainty Quantification for Distributed Energy Adoption
The rapid deployment of distributed energy resources (DER) has introduced significant spatio-temporal uncertainties in power grid management, necessitating accurate multilevel forecasting methods.
An Analysis of Market-to-Market Coordination
Then, we explore enhancements and develop an ADMM method for the M2M coordination that optimizes congestion management.
FreeAvatar: Robust 3D Facial Animation Transfer by Learning an Expression Foundation Model
In the facial animation transfer component, we propose a novel Expression-driven Multi-avatar Animator, which first maps expressive semantics to the facial control parameters of 3D avatars and then imposes perceptual constraints between the input and output images to maintain expression consistency.
Market Implications of Alternative Operating Reserve Modeling in Wholesale Electricity Markets
Pricing and settlement mechanisms are crucial for efficient re-source allocation, investment incentives, market competition, and regulatory oversight.
Affective Behaviour Analysis via Progressive Learning
To advance this, the 7th Affective Behavior Analysis in-the-wild (ABAW) competition establishes two tracks: i. e., the Multi-task Learning (MTL) Challenge and the Compound Expression (CE) challenge based on Aff-Wild2 and C-EXPR-DB datasets.
ECNet: Effective Controllable Text-to-Image Diffusion Models
Secondly, to overcome the issue of limited conditional supervision, we introduce Diffusion Consistency Loss (DCL), which applies supervision on the denoised latent code at any given time step.
Affective Behaviour Analysis via Integrating Multi-Modal Knowledge
Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world where devices can understand and react to our emotions as humans do.
A Semi-Analytical Approach for State-Space Electromagnetic Transient Simulation Using the Differential Transformation
Case studies are conducted on EMT models of the IEEE 39-bus system and a synthetic 390-bus system to demonstrate the merits of the new simulation approach against traditional methods.
Federated Battery Diagnosis and Prognosis
Battery diagnosis, prognosis and health management models play a critical role in the integration of battery systems in energy and mobility fields.
Adaptive Distributionally Robust Planning for Renewable-Powered Fast Charging Stations Under Decision-Dependent EV Diffusion Uncertainty
When deploying fast charging stations (FCSs) to support long-distance trips of electric vehicles (EVs), there exist indirect network effects: while the gradual diffusion of EVs directly influences the timing and capacities of FCS allocation, the decisions for FCS allocations, in turn, impact the drivers' willingness to adopt EVs.
Assessing Electricity Service Unfairness with Transfer Counterfactual Learning
Energy justice is a growing area of interest in interdisciplinary energy research.
Multi-modal Facial Affective Analysis based on Masked Autoencoder
The CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) is dedicated to providing high-quality and large-scale Aff-wild2 for the recognition of commonly used emotion representations, such as Action Units (AU), basic expression categories(EXPR), and Valence-Arousal (VA).
Semi-Analytical Electromagnetic Transient Simulation Using Differential Transformation
For electromagnetic transient (EMT) simulation of a power system, a state-space-based approach needs to solve state-space EMT equations by using numerical integration methods, e. g., the Euler method, Runge-Kutta methods, and trapezoidal-rule method, at small time steps.
InterMulti:Multi-view Multimodal Interactions with Text-dominated Hierarchical High-order Fusion for Emotion Analysis
Humans are sophisticated at reading interlocutors' emotions from multimodal signals, such as speech contents, voice tones and facial expressions.
EffMulti: Efficiently Modeling Complex Multimodal Interactions for Emotion Analysis
In this paper, we design three kinds of multimodal latent representations to refine the emotion analysis process and capture complex multimodal interactions from different views, including a intact three-modal integrating representation, a modality-shared representation, and three modality-individual representations.
Transformer-based Multimodal Information Fusion for Facial Expression Analysis
Then, we introduce a transformer-based fusion module that integrates the static vision features and the dynamic multimodal features.
Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling
Frequency-constrained unit commitment (FCUC) is proposed to address this challenge.
Solar Radiation Ramping Events Modeling Using Spatio-temporal Point Processes
Modeling and predicting solar events, particularly the solar ramping event, is critical for improving situational awareness for solar power generation systems.
Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems
Self-healing capability is one of the most critical factors for a resilient distribution system, which requires intelligent agents to automatically perform restorative actions online, including network reconfiguration and reactive power dispatch.
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Deep Active Learning for Solvability Prediction in Power Systems
Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism.
Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering
In this paper, we introduce body part segmentation as critical supervision.
Ranked #104 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Robust Synthesis of Wind Turbine Generators to Support Microgrid Frequency Considering Linearization-Induced Uncertainty
The capability to switch between grid-connected and islanded modes has promoted adoption of microgrid technology for powering remote locations.
Approximating Trajectory Constraints with Machine Learning -- Microgrid Islanding with Frequency Constraints
In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem.
Ensemble Learning Based Convex Approximation of Three-Phase Power Flow
The proposed approach is based on quadratic power flow equations in rectangular coordinates and it can be used in both balanced and unbalanced three-phase power networks.
Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems
Security-Constrained Unit Commitment (SCUC) is a fundamental problem in power systems and electricity markets.
