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Found 12 papers, 2 papers with code
Boosting Tail Neural Network for Realtime Custom Keyword Spotting
In this paper, we propose a Boosting Tail Neural Network (BTNN) for improving the performance of Realtime Custom Keyword Spotting (RCKS) that is still an industrial challenge for demanding powerful classification ability with limited computation resources.
Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots
Self-supervised denoisers, which learn only from single noisy images, solve the data collection problem.
Probabilistic load flow calculation of AC/DC hybrid system based on cumulant method
The operating conditions of the power system have become more complex and changeable.
Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments
The community integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention.
Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach
A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties.
Optimal Scheduling of Integrated Demand Response-Enabled Integrated Energy Systems with Uncertain Renewable Generations: A Stackelberg Game Approach
In order to balance the interests of integrated energy operator (IEO) and users, a novel Stackelberg game-based optimization framework is proposed for the optimal scheduling of integrated demand response (IDR)-enabled integrated energy systems with uncertain renewable generations, where the IEO acts as the leader who pursues the maximization of his profits by setting energy prices, while the users are the follower who adjusts energy consumption plans to minimize their energy costs.
Neural Relational Inference with Efficient Message Passing Mechanisms
Additionally, the structural prior knowledge, symmetry as a special case, is introduced for better relation prediction in more complex systems.
Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images
The TSA interpolation module aggregates temporal contexts and then adaptively samples the spatial-related features with the proposed residual spatial adaptive block.
DAN: A Deformation-Aware Network for Consecutive Biomedical Image Interpolation
The continuity of biological tissue between consecutive biomedical images makes it possible for the video interpolation algorithm, to recover large area defects and tears that are common in biomedical images.
PSDNet and DPDNet: Efficient channel expansion, Depthwise-Pointwise-Depthwise Inverted Bottleneck Block
To design more efficient lightweight concolutional neural netwok, Depthwise-Pointwise-Depthwise inverted bottleneck block (DPD block) is proposed and DPDNet is designed by stacking DPD block.
GUN: Gradual Upsampling Network for Single Image Super-Resolution
The GUN consists of an input layer, multiple upsampling and convolutional layers, and an output layer.
Robust Degraded Face Recognition Using Enhanced Local Frequency Descriptor and Multi-scale Competition
Recognizing degraded faces from low resolution and blurred images are common yet challenging task.
