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Found 61 papers, 19 papers with code
Activating Wider Areas in Image Super-Resolution
The prevalence of convolution neural networks (CNNs) and vision transformers (ViTs) has markedly revolutionized the area of single-image super-resolution (SISR).
Exploring Hardware Friendly Bottleneck Architecture in CNN for Embedded Computing Systems
We deploy our L-Mobilenet model to ZYNQ embedded platform for fully evaluating the performance of our design.
Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach
In this paper, a Graph neural network guided Distributional Deep Reinforcement Learning (GD2RL) method is proposed, for the first time, to solve the uncertainty-aware transient stability-constrained preventive redispatch problem.
Distributional Reinforcement Learning
reinforcement-learning
Distribution Locational Marginal Emission for Carbon Alleviation in Distribution Networks: Formulation, Calculation, and Implication
This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the marginal change of both active and reactive load demand for incentivizing carbon alleviation.
Active Support of Inverters for Improving Short-Term Voltage Security in 100% IBRsPenetrated Power Systems
Due to the energy crisis and environmental pollution, the installed capacity of inverter-based resources (IBRs) in power grids is rapidly increasing, and grid-following control (GFL) is the most prevalent at present.
Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Network
This paper proposes a multiagent based bi-level operation framework for the low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side.
Dynamic Operating Envelopes Embedded Peer-to-Peer-to-Grid Energy Trading
A novel decentralized peer-to-peer-to-grid (P2P2G) trading mechanism considering distribution network integrity is proposed.
Meta-Adapter: An Online Few-shot Learner for Vision-Language Model
Without bells and whistles, our approach outperforms the state-of-the-art online few-shot learning method by an average of 3. 6\% on eight image classification datasets with higher inference speed.
SoTaNa: The Open-Source Software Development Assistant
To meet the demands of this dynamic field, there is a growing need for an effective software development assistant.
An Adaptive Approach for Probabilistic Wind Power Forecasting Based on Meta-Learning
This paper studies an adaptive approach for probabilistic wind power forecasting (WPF) including offline and online learning procedures.
A Robust Planning Model for Offshore Microgrid Considering Tidal Power and Desalination
First, a deterministic planning model (DPM) is formulated for the OM with TPG and SDU.
Multi-Interval Rolling-Window Joint Dispatch and Pricing of Energy and Reserve under Uncertainty
In this paper, the intra-day multi-interval rolling-window joint dispatch and pricing of energy and reserve is studied under increasing volatile and uncertain renewable generations.
Short-Term Voltage Security Constrained UC to Prevent Trip Faults in High Wind Power Penetrated Power Systems
Due to the time delay in WTs' controllers, it is difficult for WTs alone to meet the reactive power demands in different stages of the transient process.
Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach
Based on the constraint learning and mechanism design, this paper proposes the carbon-aware energy management model in the tractable MILP form to unlock the carbon reduction potential from the demand side.
Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach
So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model.
Conservative Sparse Neural Network Embedded Frequency-Constrained Unit Commitment With Distributed Energy Resources
The increasing penetration of distributed energy resources (DERs) will decrease the rotational inertia of the power system and further degrade the system frequency stability.
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Our experimental study shows that (1) lexical, syntactic and structural properties of source code are encoded in the lower, intermediate, and higher layers, respectively, while the semantic property spans across the entire model.
Optimal scheduling of park-level integrated energy system considering ladder-type carbon trading mechanism and flexible load
In an attempt to improve the utilization efficiency of multi-energy coupling in park-level integrated energy system (PIES), promote wind power consumption and reduce carbon emissions, a low-carbon economic operation optimization model of PIES integrating flexible load and carbon trading mechanism is constructed.
Dynamic Grained Encoder for Vision Transformers
Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region.
Object-fabrication Targeted Attack for Object Detection
Evaluation results demonstrate that, the proposed object-fabrication targeted attack mode and the corresponding targeted feature space attack method show significant improvements in terms of image-specific attack, universal performance and generalization capability, compared with the previous targeted attack for object detection.
Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side
The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load.
Appendix for Nonparametric Multivariate Probability Density Forecast in Smart Grids With Deep Learning
This paper proposes a nonparametric multivariate density forecast model based on deep learning.
Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Control
We investigate that a large action space increases the learning difficulties of DRL and degrades the optimization performance in the process of generating data and training neural networks.
Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process
The problem of state estimations for electric distribution system is considered.
Flux Linkage Based Evaluation Method for Voltage Inertia and Voltage Recovery Capability Under Large Disturbances
The voltage support effect of the two capabilities is analyzed based on the flux linkage and an approximate simulation of the fault impact.
Energy-grade double pricing mechanism for a combined heat and power system using the asynchronous dispatch method
The problem of heat and electricity pricing in combined heat and power systems regarding the time scales of electricity and heat, as well as thermal energy quality, is studied.
A Multiple Market Trading Mechanism for Electricity, Renewable Energy Certificate and Carbon Emission Right of Virtual Power Plants
With the introduction of the inventory mechanism of REC and CER, the profit of the VPP increases and better trading decisions with multiple markets are made under the requirements of renewable portfolio standard (RPS) and carbon emission (CE) quota requirements.
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference.
MPC-Based Operation Strategy for Electric Vehicle Aggregators Considering Regulation Markets
The optimal operation problem of electric vehicle aggregator (EVA) is considered.
An Efficient Optimal Energy Flow Model for Integrated Energy Systems Based on Energy Circuit Modeling in the Frequency Domain
First, an energy circuit method (ECM) that algebraizes the PDEs of NGNs and DHNs in the frequency domain is introduced.
Energy Circuit-based Integrated Energy Management System: Theory, Implementation, and Application
Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years.
S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving
To safely and rationally participate in dense and heterogeneous traffic, autonomous vehicles require to sufficiently analyze the motion patterns of surrounding traffic-agents and accurately predict their future trajectories.
Energy-Grade Double Pricing Rule in the Heating Market
The problem of heat system pricing is considered.
CoCoSoDa: Effective Contrastive Learning for Code Search
However, there is still a lot of room for improvement in using contrastive learning for code search.
Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control
Then we design the one-step actor-critic DRL scheme which is a simplified version of recent DRL algorithms, and it avoids the issue of Q value overestimation successfully.
Joint Covering Congestion Rents in Multi-area Power Systems Considering Loop Flow Effects
We consider the problem of how multiple areas should jointly cover congestion rents of internal and tie-lines in an interconnected power system.
A Stochastic Planning Method for Low-carbon Building-level Integrated Energy System Considering Electric-Heat-V2G Coupling
The concept of low-carbon building is proposed to ameliorate the climate change caused by environmental problems and realize carbon neutrality at the building level in urban areas.
RACE: Retrieval-Augmented Commit Message Generation
Furthermore, RACE can boost the performance of existing Seq2Seq models in commit message generation.
A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction
In a modern power system, real-time data on power generation/consumption and its relevant features are stored in various distributed parties, including household meters, transformer stations and external organizations.
Accuracy and Application Scope Analysis for Linearized Branch Flow Model in Radial Distribution Systems
An in-depth analysis of linearized branch flow (LBF) model considering current injection and absolute value of impedance is proposed in this paper.
MPC-Based Real-Time Charging Coordination for Electric Vehicle Aggregator to Provide Regulation Service in a Market Environment
The optimal operation problem of electric vehicle aggregator (EVA) is considered.
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning
Recent studies propose leveraging the rich contextual information in data provenance to detect threats in a host.
A Scenario-oriented Approach to Multi-period Energy-Reserve Joint Procurement and Pricing
In [1], a single-period co-optimization model of energy and reserve is considered to better illustrate the properties of the co-optimization model and the associated market mechanism.
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding
We present MMOCR-an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction.
Two-stage Planning for Electricity-Gas Coupled Integrated Energy System with CCUS Considering Carbon Tax and Price Uncertainty
In this article, we propose two-stage planning models for Electricity-Gas Coupled Integrated Energy System (EGC-IES), in which traditional thermal power plants (TTPPs) are considered to be retrofitted into carbon capture power plants (CCPPs), with power to gas (PtG) coupling CCPPs to gas system.
A Scenario-oriented Approach to Energy-Reserve Joint Procurement and Pricing
We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we price reserve and charge uncertainty sources.
On the Evaluation of Neural Code Summarization
To achieve a profound understanding of how far we are from solving this problem and provide suggestions to future research, in this paper, we conduct a systematic and in-depth analysis of 5 state-of-the-art neural code summarization models on 6 widely used BLEU variants, 4 pre-processing operations and their combinations, and 3 widely used datasets.
An Integration and Operation Framework of Geothermal Heat Pumps in Distribution Networks
Besides, in the Virtual Power Plant (VPP) system, the demand response of clustered GHP systems can improve the operating flexibility of the power grid.
A Multivariate Density Forecast Approach for Online Power System Security Assessment
A multivariate density forecast model based on deep learning is designed in this paper to forecast the joint cumulative distribution functions (JCDFs) of multiple security margins in power systems.
A Conditional Value-at-Risk Based Planning Model for Integrated Energy System with Energy Storage and Renewables
Owing to the potential higher energy supply efficiency and operation flexibility, integrated energy system (IES), which usually includes electric power, gas and heating/cooling systems, is considered as one of the primary forms of energy carrier in the future.
Spatial Dual-Modality Graph Reasoning for Key Information Extraction
In order to roundly evaluate our proposed method as well as boost the future research, we release a new dataset named WildReceipt, which is collected and annotated tailored for the evaluation of key information extraction from document images of unseen templates in the wild.
A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning
For example, power consumption data are stored in numerous transformer stations across cities; mobility data of the population, which are important indicators of power consumption, are held by mobile companies.
Fine-Grained Dynamic Head for Object Detection
To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation.
Rethinking Learnable Tree Filter for Generic Feature Transform
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation.
End-to-End Object Detection with Fully Convolutional Network
Mainstream object detectors based on the fully convolutional network has achieved impressive performance.
ADCPNet: Adaptive Disparity Candidates Prediction Network for Efficient Real-Time Stereo Matching
The evaluation results on multiple datasets and platforms clearly demonstrate that, the proposed network outperforms the state-of-the-art lightweight models especially for mobile devices in terms of accuracy and speed.
RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition
Theoretically, our proposed method, dubbed \emph{RobustScanner}, decodes individual characters with dynamic ratio between context and positional clues, and utilizes more positional ones when the decoding sequences with scarce context, and thus is robust and practical.
Learnable Tree Filter for Structure-preserving Feature Transform
To this end, tree filtering modules are embedded to formulate a unified framework for semantic segmentation.
Geometry Normalization Networks for Accurate Scene Text Detection
Large geometry (e. g., orientation) variances are the key challenges in the scene text detection.
Ranked #10 on
Scene Text Detection
on ICDAR 2017 MLT
TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection
In this paper, we define these ambiguous samples as "transitional states", and propose a Transition-Aware Context Network (TACNet) to distinguish transitional states.
Ranked #7 on
Action Detection
on J-HMDB
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN).
