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Found 17 papers, 5 papers with code
A Novel Vision Transformer based Load Profile Analysis using Load Images as Inputs
This paper introduces ViT4LPA, an innovative Vision Transformer (ViT) based approach for Load Profile Analysis (LPA).
Constrained Multiview Representation for Self-supervised Contrastive Learning
In this work, we introduce a novel approach predicated on representation distance-based mutual information (MI) maximization for measuring the significance of different views, aiming at conducting more efficient contrastive learning and representation disentanglement.
Uncertainty Regularized Evidential Regression
The Evidential Regression Network (ERN) represents a novel approach that integrates deep learning with Dempster-Shafer's theory to predict a target and quantify the associated uncertainty.
BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles
To adopt a standard Transformer model structure for profile inpainting, we segment the load and temperature profiles into line segments, treating each segment as a word and the entire profile as a sentence.
VisorGPT: Learning Visual Prior via Generative Pre-Training
Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet.
Design Considerations of a Coordinative Demand Charge Mitigation Strategy
This paper presents a coordinative demand charge mitigation (DCM) strategy for reducing electricity consumption during system peak periods.
A Modified Sequence-to-point HVAC Load Disaggregation Algorithm
This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption.
An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data
The residual load profiles are processed using ICA for HVAC load extraction.
Multi-Robot Active Mapping via Neural Bipartite Graph Matching
Previous approaches either choose the frontier as the goal position via a myopic solution that hinders the time efficiency, or maximize the long-term value via reinforcement learning to directly regress the goal position, but does not guarantee the complete map construction.
Cross Language Image Matching for Weakly Supervised Semantic Segmentation
As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.
CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation
As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.
Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model
At the same time, due to the adoption of an individual-based evolution mechanism, the computational cost of the proposed method is independent of the number of decision variables, thus avoiding the problem of exponential growth of the search space.
Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation
To address this issue, we propose a novel architecture, termed as IEGAN, which removes the encoder of each network and introduces an encoder that is independent of other networks.
EdgeCRNN: an edgecomputing oriented model of acoustic feature enhancement for keyword spotting
Keyword Spotting (KWS) is a significant branch of Automatic Speech Recognition (ASR) and has been widely used in edge computing devices.
Ranked #8 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V2 12 metric)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks
However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches.
EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing
Therefore, a small input network can be designed to reduce computation.
Homotopic Convex Transformation: A New Landscape Smoothing Method for the Traveling Salesman Problem
We first define the Homotopic Convex (HC) transformation of a TSP as a convex combination of a well-constructed simple TSP and the original TSP.
