pwc-archive/papers-with-abstracts huggingface dataset:
Preventive Energy Management for Distribution Systems Under Uncertain Events: A Deep Reinforcement Learning Approach
Authors: Md Isfakul Anam, Tuyen Vu, Jianhua Zhang
Date: 2024-12-26 00:00:00
Tasks: Deep Reinforcement Learning, energy management, Management, Reinforcement Learning (RL)

As power systems become more complex with the continuous integration of intelligent distributed energy resources (DERs), new risks and uncertainties arise. Consequently, to enhance system resiliency, it is essential to account for various uncertain events when implementing the optimization problem for the energy management system (EMS). This paper presents a preventive EMS considering the probability of failure (PoF) of each system component across different scenarios. A conditional-value-at-risk (CVaR)-based framework is proposed to integrate the uncertainties of the distribution network. Loads are classified into critical, semi-critical, and non-critical categories to prioritize essential loads during generation resource shortages. A proximal policy optimization (PPO)-based reinforcement learning (RL) agent is used to solve the formulated problem and generate the control decisions. The proposed framework is evaluated on a notional MVDC ship system and a modified IEEE 30-bus system, where the results demonstrate that the PPO agent can successfully optimize the objective function while maintaining the network and operational constraints. For validation, the RL-based method is benchmarked against a traditional optimization approach, further highlighting its effectiveness and robustness. This comparison shows that RL agents can offer more resiliency against future uncertain events compared to the traditional solution methods due to their adaptability and learning capacity.
Multi-scale Latent Point Consistency Models for 3D Shape Generation
Authors: Bi'an Du, Wei Hu, Renjie Liao
Date: 2024-12-27 00:00:00
Tasks: 3D Shape Generation, Diversity

Consistency Models (CMs) have significantly accelerated the sampling process in diffusion models, yielding impressive results in synthesizing high-resolution images. To explore and extend these advancements to point-cloud-based 3D shape generation, we propose a novel Multi-scale Latent Point Consistency Model (MLPCM). Our MLPCM follows a latent diffusion framework and introduces hierarchical levels of latent representations, ranging from point-level to super-point levels, each corresponding to a different spatial resolution. We design a multi-scale latent integration module along with 3D spatial attention to effectively denoise the point-level latent representations conditioned on those from multiple super-point levels. Additionally, we propose a latent consistency model, learned through consistency distillation, that compresses the prior into a one-step generator. This significantly improves sampling efficiency while preserving the performance of the original teacher model. Extensive experiments on standard benchmarks ShapeNet and ShapeNet-Vol demonstrate that MLPCM achieves a 100x speedup in the generation process, while surpassing state-of-the-art diffusion models in terms of both shape quality and diversity.
A Review of Resilience Enhancement Measures for Hydrogen-penetrated Multi-energy Systems
Authors: Liang Yu, HaoYu Fang, Goran Strbac, Dawei Qiu, Dong Yue, Xiaohong Guan, Gerhard P. Hancke
Date: 2024-12-26 00:00:00
Tasks: Attribute

Energy supply for electricity and heat sectors accounts for more than 40% of global carbon emissions in 2023, which brings great pressure for achieving net-zero carbon emission targets in the future. Under the above background, hydrogen-penetrated multi-energy systems (HMESs) have received wide attention due to their potential low-carbon attribute. However, HMESs still face the following challenge, i.e., how to survive and quickly recover from extreme and unexpected events (e.g., natural disasters, extreme weather, and cyber-physical attacks). To enable the above resilience attribute, many existing works on HMES resilience enhancement have been done. However, there lacks a systematic overview of different resilience enhancement measures for HMESs. To fill the research gap, this paper provides a comprehensive overview of resilience enhancement strategies for HMESs from the perspective of hydrogen-related planning and operation. To be specific, we propose a comprehensive resilience enhancement framework for HEMSs. Under the proposed framework, the widely used resilience metrics and event-oriented contingency models in existing works are summarized. Then, we classify the hydrogen-related planning measures for HMES resilience enhancement according to the type of hydrogen-related facilities and provide some insights for planning problem formulation framework. Moreover, we categorize the hydrogen-related operation measures for HMES resilience enhancement according to the three kinds of operation response stages involved, including preventive response, emergency response, and restoration response. Finally, we identify some research gaps and point out possible future directions.
MLLM-SUL: Multimodal Large Language Model for Semantic Scene Understanding and Localization in Traffic Scenarios
Authors: Jiaqi Fan, Jianhua Wu, Jincheng Gao, Jianhao Yu, Yafei Wang, Hongqing Chu, Bingzhao Gao
Date: 2024-12-27 00:00:00
Tasks: Autonomous Driving, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, Scene Understanding, Text Generation

Multimodal large language models (MLLMs) have shown satisfactory effects in many autonomous driving tasks. In this paper, MLLMs are utilized to solve joint semantic scene understanding and risk localization tasks, while only relying on front-view images. In the proposed MLLM-SUL framework, a dual-branch visual encoder is first designed to extract features from two resolutions, and rich visual information is conducive to the language model describing risk objects of different sizes accurately. Then for the language generation, LLaMA model is fine-tuned to predict scene descriptions, containing the type of driving scenario, actions of risk objects, and driving intentions and suggestions of ego-vehicle. Ultimately, a transformer-based network incorporating a regression token is trained to locate the risk objects. Extensive experiments on the existing DRAMA-ROLISP dataset and the extended DRAMA-SRIS dataset demonstrate that our method is efficient, surpassing many state-of-the-art image-based and video-based methods. Specifically, our method achieves 80.1% BLEU-1 score and 298.5% CIDEr score in the scene understanding task, and 59.6% accuracy in the localization task. Codes and datasets are available at https://github.com/fjq-tongji/MLLM-SUL.
Advanced Scheduling of Electrolyzer Modules for Grid Flexibility
Authors: Angelina Lesniak, Andrea Gloppen Johnsen, Noah Rhodes, Line Roald
Date: 2024-12-26 00:00:00
Tasks: Scheduling

As the transition to sustainable power generation progresses, green hydrogen production via electrolysis is expected to gain importance as a means for energy storage and flexible load to complement variable renewable generation. With the increasing need for cost-effective and efficient hydrogen production, electrolyzer optimization is essential to improve both energy efficiency and profitability. This paper analyzes how the efficiency and modular setup of alkaline hydrogen electrolyzers can improve hydrogen output of systems linked to a fluctuating renewable power supply. To explore this, we propose a day-ahead optimal scheduling problem of a hybrid wind and electrolyzer system. The novelty of our approach lies in modeling the number and capacity of electrolyzer modules, and capturing the modules' impact on the hydrogen production and efficiency. We solve the resulting mixed-integer optimization problem with several different combinations of number of modules, efficiency and operating range parameters, using day-ahead market data from a wind farm generator in the ERCOT system as an input. Our results demonstrate that the proposed approach ensures that electrolyzer owners can better optimize the operation of their systems, achieving greater hydrogen production and higher revenue. Key findings include that as the number of modules in a system with the same overall capacity increases, hydrogen production and revenue increases.
Spectral-Temporal Fusion Representation for Person-in-Bed Detection
Authors: Xuefeng Yang, Shiheng Zhang, Jian Guan, Feiyang Xiao, Wei Lu, Qiaoxi Zhu
Date: 2024-12-27 00:00:00
Tasks: Data Augmentation

This study is based on the ICASSP 2025 Signal Processing Grand Challenge's Accelerometer-Based Person-in-Bed Detection Challenge, which aims to determine bed occupancy using accelerometer signals. The task is divided into two tracks: "in bed" and "not in bed" segmented detection, and streaming detection, facing challenges such as individual differences, posture variations, and external disturbances. We propose a spectral-temporal fusion-based feature representation method with mixup data augmentation, and adopt Intersection over Union (IoU) loss to optimize detection accuracy. In the two tracks, our method achieved outstanding results of 100.00% and 95.55% in detection scores, securing first place and third place, respectively.
Parametrizations of All Stable Closed-loop Responses: From Theory to Neural Network Control Design
Authors: Clara Lucía Galimberti, Luca Furieri, Giancarlo Ferrari-Trecate
Date: 2024-12-26 00:00:00
Tasks: All

The complexity of modern control systems necessitates architectures that achieve high performance while ensuring robust stability, particularly for nonlinear systems. In this work, we tackle the challenge of designing output-feedback controllers to boost the performance of $\ell_p$-stable discrete-time nonlinear systems while preserving closed-loop stability from external disturbances to input and output channels. Leveraging operator theory and neural network representations, we parametrize the achievable closed-loop maps for a given system and propose novel parametrizations of all $\ell_p$-stabilizing controllers, unifying frameworks such as nonlinear Youla parametrization and internal model control. Contributing to a rapidly growing research line, our approach enables unconstrained optimization exclusively over stabilizing controllers and provides sufficient conditions to ensure robustness against model mismatch. Additionally, our methods reveal that stronger notions of stability can be imposed on the closed-loop maps if disturbance realizations are available after one time step. Last, our approaches are compatible with the design of nonlinear distributed controllers. Numerical experiments on cooperative robotics demonstrate the flexibility of the proposed framework, allowing cost functions to be freely designed for achieving complex behaviors while preserving stability.
BeSplat: Gaussian Splatting from a Single Blurry Image and Event Stream
Authors: Gopi Raju Matta, Reddypalli Trisha, Kaushik Mitra
Date: 2024-12-26 00:00:00
Tasks: 3DGS, NeRF, Novel View Synthesis, Single Image Deblurring

Novel view synthesis has been greatly enhanced by the development of radiance field methods. The introduction of 3D Gaussian Splatting (3DGS) has effectively addressed key challenges, such as long training times and slow rendering speeds, typically associated with Neural Radiance Fields (NeRF), while maintaining high-quality reconstructions. In this work (BeSplat), we demonstrate the recovery of sharp radiance field (Gaussian splats) from a single motion-blurred image and its corresponding event stream. Our method jointly learns the scene representation via Gaussian Splatting and recovers the camera motion through Bezier SE(3) formulation effectively, minimizing discrepancies between synthesized and real-world measurements of both blurry image and corresponding event stream. We evaluate our approach on both synthetic and real datasets, showcasing its ability to render view-consistent, sharp images from the learned radiance field and the estimated camera trajectory. To the best of our knowledge, ours is the first work to address this highly challenging ill-posed problem in a Gaussian Splatting framework with the effective incorporation of temporal information captured using the event stream.
Robust $H_{\infty}$ Position Controller for Steering Systems
Authors: Tushar Chugh, Fredrik Bruzelius, Balázs Kulcsár
Date: 2024-12-26 00:00:00
Tasks: Position

This paper presents a robust position controller for electric power assisted steering and steer-by-wire force-feedback systems. A position controller is required in steering systems for haptic feedback control, advanced driver assistance systems and automated driving. However, the driver's \textit{physical} arm impedance causes an inertial uncertainty during coupling. Consequently, a typical position controller, i.e., based on single variable, becomes less robust and suffers tracking performance loss. Therefore, a robust position controller is investigated. The proposed solution is based on the multi-variable concept such that the sensed driver torque signal is also included in the position controller. The subsequent solution is obtained by solving the LMI$-H_{\infty}$ optimization problem. As a result, the desired loop gain shape is achieved, i.e., large gain at low frequencies for performance and small gain at high frequencies for robustness. Finally, frequency response comparison of different position controllers on real hardware is presented. Experiments and simulation results clearly illustrate the improvements in reference tracking and robustness with the proposed $H_\infty$ controller.
Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images
Authors: Leonardo Gabriel Ferreira Rodrigues, Danilo Ferreira da Silva, Larissa Ferreira Rodrigues, João Fernando Mari
Date: 2024-12-26 00:00:00
Tasks: Data Augmentation

Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding this quickly disease spread. The need for automated and scalable methods has increased due to the unavailability of accurate automated toolkits. Recent researches using chest X-ray images suggest they include relevant information about the COVID-19 virus. Hence, applying machine learning techniques combined with radiological imaging promises to identify this disease accurately. It is straightforward to collect these images once it is spreadly shared and analyzed in the world. This paper presents a method for automatic COVID-19 detection using chest Xray images through four convolutional neural networks, namely: AlexNet, VGG-11, SqueezeNet, and DenseNet-121. This method had been providing accurate diagnostics for positive or negative COVID-19 classification. We validate our experiments using a ten-fold cross-validation procedure over the training and test sets. Our findings include the shallow fine-tuning and data augmentation strategies that can assist in dealing with the low number of positive COVID-19 images publicly available. The accuracy for all CNNs is higher than 97.00%, and the SqueezeNet model achieved the best result with 99.20%.
Synthetic Discrete Inertia
Authors: A. Vaca, F. Milano
Date: 2024-12-26 00:00:00

This letter demonstrates how synthetic inertia can be obtained with the control of flexible discrete devices to keep the power balance of power systems, even if the system does not include any synchronous generator or conventional grid-forming converter. The letter also discusses solutions to cycling issues, which can arise due to the interaction of uncoordinated discrete inertia controllers. The effectiveness, dynamic performance, and challenges of the proposed approach are validated through simulations using modified versions of the WSCC 9-bus test system and of the all-island Irish transmission system.
An Approximated Model of Wildfire Propagation on Slope
Authors: Hengameh R. Dehkordi
Date: 2024-12-25 00:00:00
Tasks: Management, model

The increasing frequency and intensity of wildfires underscore the need for accurate predictive models to enhance wildfire management. Traditional models, such as Rothermel and FARSITE, provide foundational insights but often oversimplify the complex dynamics of wildfire spread. Advanced methods, employing sophisticated mathematical techniques, offer more precise modeling by accounting for real-world complexities and dynamic environmental factors. This paper focuses on wildfire propagation over inclined terrains and combines the Rothermel model, Huygens' principle, and advanced mathematical techniques to provide a more precise model of propagation. Environmental parameters and vegetation factors are directly incorporated into formulas and equations to improve the reliability and effectiveness of wildfire management strategies. The practical application of these results is demonstrated through MATLAB simulations, specifically examining wildfire spread under wind conditions that do not impede upwind fire advancement. The findings of this work contribute to both wildfire research and the development of more effective management strategies.
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks
Authors: Yu Qiao, Apurba Adhikary, Kitae Kim, Eui-Nam Huh, Zhu Han, Choong Seon Hong
Date: 2024-12-26 00:00:00
Tasks: Data Augmentation, Federated Learning

Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks by allowing data owners to collaborate without transmitting raw data to the edge server. However, data heterogeneity and adversarial attacks pose challenges to develop an unbiased and robust global model for edge deployment. To address this, we propose Federated hyBrid Adversarial training and self-adversarial disTillation (FedBAT), a new framework designed to improve both robustness and generalization of the global model. FedBAT seamlessly integrates hybrid adversarial training and self-adversarial distillation into the conventional FL framework from data augmentation and feature distillation perspectives. From a data augmentation perspective, we propose hybrid adversarial training to defend against adversarial attacks by balancing accuracy and robustness through a weighted combination of standard and adversarial training. From a feature distillation perspective, we introduce a novel augmentation-invariant adversarial distillation method that aligns local adversarial features of augmented images with their corresponding unbiased global clean features. This alignment can effectively mitigate bias from data heterogeneity while enhancing both the robustness and generalization of the global model. Extensive experimental results across multiple datasets demonstrate that FedBAT yields comparable or superior performance gains in improving robustness while maintaining accuracy compared to several baselines.
External Bias and Opinion Clustering in Cooperative Networks
Authors: Akshay Nagesh Kamthe, Vishnudatta Thota, Aashi Shrinate, Twinkle Tripathy
Date: 2024-12-25 00:00:00
Tasks: Clustering

In this work, we consider a group of n agents which interact with each other in a cooperative framework. A Laplacian-based model is proposed to govern the evolution of opinions in the group when the agents are subjected to external biases like agents' traits, news, etc. The objective of the paper is to design a control input which leads to any desired opinion clustering even in the presence of external bias factors. Further, we also determine the conditions which ensure the reachability to any arbitrary opinion states. Note that all of these results hold for any kind of graph structure. Finally, some numerical simulations are discussed to validate these results.
Preventing output saturation in active noise control: An output-constrained Kalman filter approach
Authors: Junwei Ji, Dongyuan Shi, Boxiang Wang, Xiaoyi Shen, Zhengding Luo, Woon-Seng Gan
Date: 2024-12-25 00:00:00

The Kalman filter (KF)-based active noise control (ANC) system demonstrates superior tracking and faster convergence compared to the least mean square (LMS) method, particularly in dynamic noise cancellation scenarios. However, in environments with extremely high noise levels, the power of the control signal can exceed the system's rated output power due to hardware limitations, leading to output saturation and subsequent non-linearity. To mitigate this issue, a modified KF with an output constraint is proposed. In this approach, the disturbance treated as an measurement is re-scaled by a constraint factor, which is determined by the system's rated power, the secondary path gain, and the disturbance power. As a result, the output power of the system, i.e. the control signal, is indirectly constrained within the maximum output of the system, ensuring stability. Simulation results indicate that the proposed algorithm not only achieves rapid suppression of dynamic noise but also effectively prevents non-linearity due to output saturation, highlighting its practical significance.
Resolving the Ambiguity of Complete-to-Partial Point Cloud Registration for Image-Guided Liver Surgery with Patches-to-Partial Matching
Authors: Zixin Yang, Jon S. Heiselman, Cheng Han, Kelly Merrell, Richard Simon, Cristian. A. Linte
Date: 2024-12-26 00:00:00
Tasks: Point Cloud Registration

In image-guided liver surgery, the initial rigid alignment between preoperative and intraoperative data, often represented as point clouds, is crucial for providing sub-surface information from preoperative CT/MRI images to the surgeon during the procedure. Currently, this alignment is typically performed using semi-automatic methods, which, while effective to some extent, are prone to errors that demand manual correction. Point cloud correspondence-based registration methods are promising to serve as a fully automatic solution. However, they may struggle in scenarios with limited intraoperative surface visibility, a common challenge in liver surgery, particularly in laparoscopic procedures, which we refer to as complete-to-partial ambiguity. We first illustrate this ambiguity by evaluating the performance of state-of-the-art learning-based point cloud registration methods on our carefully constructed in silico and in vitro datasets. Then, we propose a patches-to-partial matching strategy as a plug-and-play module to resolve the ambiguity, which can be seamlessly integrated into learning-based registration methods without disrupting their end-to-end structure. It has proven effective and efficient in improving registration performance for cases with limited intraoperative visibility. The constructed benchmark and the proposed module establish a solid foundation for advancing applications of point cloud correspondence-based registration methods in image-guided liver surgery.