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Found 52 papers, 8 papers with code
Invisible Manipulation Deep Reinforcement Learning Enhanced Stealthy Attacks on Battery Energy Management Systems
By stealthily manipulating measurements of a critical asset prior to the target time period, the attacker can subtly guide the engineering system toward a predetermined operational state without detection.
A Two-Stage Optimization Method for Real-Time Parameterization of PV-Farm Digital Twin
Initially, the method estimates equivalent irradiance from PV power, voltage, and current data, eliminating the need for direct irradiance sensors.
Training Overhead Ratio: A Practical Reliability Metric for Large Language Model Training Systems
In this work, we introduce a novel reliability metric called \emph{Training Overhead Ratio} (TOR) to evaluate the reliability of fault-tolerant LLM training systems.
Backdoor Graph Condensation
Effective backdoor attacks on graph condensation aim to (1) maintain the quality and utility of condensed graphs despite trigger injections and (2) ensure trigger effectiveness through the condensation process, yielding a high attack success rate.
Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis
This paper presents a novel method for utilizing fine-tuned Large Language Models (LLMs) to minimize data requirements in load profile analysis, demonstrated through the restoration of missing data in power system load profiles.
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).
Multi-Feeder Restoration using Multi-Microgrid Formation and Management
This papers highlights the benefit of coordinating resources on mulitple active distribution feeders during severe long duration outages through multi-microgrid formation.
Assessment of Transmission-level Fault Impacts on 3-phase and 1-phase Distribution IBR Operation
The widespread deployment of inverter-based resources (IBRs) renders distribution systems susceptible to transmission-level faults.
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.
Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks
Two cascade neural networks (NN) are used to optimize the joint number of virtually generated UAVs, the DT construction cost, and the performance of multi-UAV networks.
Adopting Dynamic VAR Compensators to Mitigate PV Impacts on Unbalanced Distribution Systems
However, the effectiveness of this scheme is not well documented, and there is limited literature on alternative control and placement schemes that can maximize the effective use of a DVC.
Under-frequency Load Shedding for Power Reserve Management in Islanded Microgrids
Furthermore, in comparison to sectionalizer-based UFLS, using smart meters or controllable loads for UFLS allows for a more accurate per-phase load shedding in a progressive manner.
PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer
We present PBFormer, an efficient yet powerful scene text detector that unifies the transformer with a novel text shape representation Polynomial Band (PB).
Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach
It incorporates a framewise regularization term in CTC loss to emphasize individual supervision, and leverages the maximizing-a-posteriori of latent alignment to solve the inconsistency problem that arises in distillation between CTC-based models.
Large Language Models can be Guided to Evade AI-Generated Text Detection
Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public.
ICDAR 2023 Competition on Reading the Seal Title
To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2).
Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling
The ablation study also validates that the proposed coordinate sequence decoder and the visual-alignment loss are the keys to the success of our method.
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning
In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset.
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
Our comprehensive experiments reveal that in approximately 90\% of cases, word-level attacks lead to the generation of examples where the frequency of $n$-grams decreases, a tendency we term as the $n$-gram Frequency Descend ($n$-FD).
A Novel Feeder-level Microgrid Unit Commitment Algorithm Considering Cold-load Pickup, Phase Balancing, and Reconfiguration
This paper presents a novel 2-stage microgrid unit commitment (Microgrid-UC) algorithm considering cold-load pickup (CLPU) effects, three-phase load balancing requirements, and feasible reconfiguration options.
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.
Optimal Control Design for Operating a Hybrid PV Plant with Robust Power Reserves for Fast Frequency Regulation Services
The controller's objective is to control the PV and BESS to follow power setpoints sent to the the hybrid system while maintaining desired power reserves and meeting system operational constraints.
Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation
We propose a Generator structure consisting of a coarse network and a fine-tuning network.
An Iterative Bidirectional Gradient Boosting Approach for CVR Baseline Estimation
In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem.
SigT: An Efficient End-to-End MIMO-OFDM Receiver Framework Based on Transformer
Multiple-input multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) are the key technologies in 4G and subsequent wireless communication systems.
MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations
This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously.
A Novel Power-Band based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification
To ensure the credibility of the identification results, utility engineers conduct field verification for all 13 feeders.
An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data
The residual load profiles are processed using ICA for HVAC load extraction.
Feeder Microgrid Management on an Active Distribution System during a Severe Outage
The management of such feeder-level microgrid has however many challenges, such as limited resources that can be deployed on the feeder quickly, and the limited real-time monitoring and control on the distribution system.
On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks
On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic.
SA-HMTS: A Secure and Adaptive Hierarchical Multi-timescale Framework for Resilient Load Restoration Using A Community Microgrid
Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages.
Training Quantized Deep Neural Networks via Cooperative Coevolution
This work considers a challenging Deep Neural Network(DNN) quantization task that seeks to train quantized DNNs without involving any full-precision operations.
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy
In Stage 1(individual training), while holding all the other agents inactive, we separately train each agent to obtain its own optimal VVC actions in the action space: {consume, generate, do-nothing}.
A Novel Data Segmentation Method for Data-driven Phase Identification
To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase are weakened.
Novel Real-Time EMT-TS Modeling Architecture for Feeder Blackstart Simulations
This paper presents the development and benchmarking of a novel real-time electromagnetic-transient and transient-stability (EMT-TS) modeling architecture for distribution feeder restoration studies.
A Novel Grid-forming Voltage Control Strategy for Supplying Unbalanced Microgrid Loads Using Inverter-based Resources
Compared with the conventional rotating reference frame ($dq$) based control scheme, the proposed scheme shows better dynamic performance.
A TCN-based Hybrid Forecasting Framework for Hours-ahead Utility-scale PV Forecasting
The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven cloud-event forecasting (CF) model.
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization
The field of adversarial textual attack has significantly grown over the last few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model.
A Data-driven Probabilistic-based Flexibility Region Estimation Method for Aggregated Distributed Energy Resources
At the DER-level, a two-dimensional flexibility region can be formed based on the real and reactive power regulating limits of each DER considering forecast uncertainty.
Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack
Over the past few years, various word-level textual attack approaches have been proposed to reveal the vulnerability of deep neural networks used in natural language processing.
ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles
When training the ProfileSR-GAN generator network, to make the generated profiles more realistic, we introduce two new shape-related losses in addition to conventionally used content loss: adversarial loss and feature-matching loss.
Generative Adversarial Network
Non-Intrusive Load Monitoring
+3
A Unified Power-Setpoint Tracking Algorithm for Utility-Scale PV Systems with Power Reserves and Fast Frequency Response Capabilities
By using MPPE to decouple the impact of irradiance changes on the measured PV output power, we develop a fast convergence technique for tracking power-reference changes within three FPPT iterations.
A Two-Stage Coordinative Zonal Volt/VAR Control Scheme for Distribution Systems with High Inverter-based Resources
IBR are used in the first stage to regulate voltage changes continuously and VR are used in the second stage to regulate large voltage deviations.
A Zonal Volt/VAR Control Mechanism for High PV Penetration Distribution Systems
Because each zone is weakly coupled, voltage of each zone can be controlled independently.
Hierarchical Multi-timescale Framework For Operation of Dynamic Community Microgrid
The intermediate near real-time scheduling stage updates the DA schedule closer to the dispatch time, followed by the RT dispatch stage.
ACDER: Augmented Curiosity-Driven Experience Replay
Exploration in environments with sparse feedback remains a challenging research problem in reinforcement learning (RL).
A Meta-learning based Distribution System Load Forecasting Model Selection Framework
This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework.
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild
To address the severe domain distribution mismatch, we propose a synthetic-to-real domain adaptation method for scene text detection, which transfers knowledge from synthetic data (source domain) to real data (target domain).
PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks
Computer vision with state-of-the-art deep learning models has achieved huge success in the field of Optical Character Recognition (OCR) including text detection and recognition tasks recently.
FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets
This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN.
MASTER: Multi-Aspect Non-local Network for Scene Text Recognition
Attention-based scene text recognizers have gained huge success, which leverages a more compact intermediate representation to learn 1d- or 2d- attention by a RNN-based encoder-decoder architecture.
