IEEE Access最新文献

{"title":"Dynamic State Estimation in Power Electronics-Dominated Grids Using a Digital Twin Approach","authors":"Ildar Idrisov;Ilya Veretennikov;Federico M. Ibanez","doi":"10.1109/ACCESS.2025.3554258","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554258","url":null,"abstract":"This paper proposes a digital twin (DT) based approach to improve dynamic state estimation (DSE) in power electronics-dominated grids (PEDGs), ultimately contributing to a more reliable, efficient, and sustainable power system by enabling better integration of renewable energy sources and reducing grid instability. Traditional DSE methods struggle with the complexities of PEDGs, such as high penetration of distributed generation and energy storage, leading to inaccurate state estimations. Our approach leverages a real-time, high-fidelity virtual model of a microgrid, including detailed models of DG units, power electronics and load dynamics. This enables accurate state estimation even during transient events. The proposed approach is validated using a hardware-in-the-loop test bench which demonstrates the DT’s accuracy in tracking grid dynamics under various operating conditions. Furthermore, a sensitivity analysis highlights the importance of accurate parameter estimation and minimal communication delays for robust DSE. This research provides a framework for developing reliable and accurate DSE solutions for modern PEDGs, enabling advanced grid control, optimization, and predictive maintenance strategies.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53938-53948"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors","authors":"Simone Ferrari;Luigi Solimene;Riccardo Torchio;Costanza Anerdi;Fabio Freschi;Luca Giaccone;Gianmarco Lorenti;Francesco Lucchinizz;Piergiorgio Alotto;Gianmario Pellegrino;Maurizio Repetto","doi":"10.1109/ACCESS.2025.3554147","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554147","url":null,"abstract":"This paper presents the work done to address two main challenges in the simulation and design of electric machines for traction applications. On one hand, the modeling process is becoming increasingly complex as the demand for higher efficiency, high power density, and low cost pushes the speed and compactness of the motor to high levels. As a result, the interactions between multiple physical domains (e.g., electromagnetic, thermal, structural, etc.) can no longer be neglected, even in preliminary designs. Consequently, research into new modeling solutions in this area is currently active and widespread. On the other hand, new computational methodologies based on data-driven machine learning are becoming increasingly widespread as the computational power available for this task increases. However, to assess their performance and realize their potential in surrogate and meta-modeling electrical machines, a standardized benchmark for comparing these new approaches is needed. To address these challenges, the paper presents an open-source dataset that provides a reliable foundation for the multi-physical analysis of electric motors used in traction applications. One of the main novelties of this approach is that geometrical and physical data of the motor configuration are shared among different analysis codes. Attention is focused on tailoring the numerical discretization so that the same mesh can be used in different domains, avoiding data conversions and possible numerical inaccuracies. The paper thoroughly explains the workflow developed to create the database, detailing the methodological aspects. Ultimately, the resulting database is made available as an open resource for other researchers in the field. The resulting dataset represents a tool for benchmarking advanced computational methodologies and promoting reproducibility in research.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54534-54546"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937701","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary Synthesis of High-Capacity Reconfigurable Multilayer Road Networks Using a Multiagent Hybrid Clustering-Assisted Genetic Algorithm","authors":"Andranik S. Akopov;Levon A. Beklaryan","doi":"10.1109/ACCESS.2025.3554054","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554054","url":null,"abstract":"Modern requirements for urban traffic management and control call for the design of high-capacity reconfigurable multilayer road networks (RMRNs). This paper discusses the proposed evolutionary synthesis approach, a promising method for finding the best configurations of RMRNs, aiming to create road networks with optimized layouts that maximize vehicle outflow. As the complexity of RMRNs increases, due to the addition of overpasses and tunnels, the expenses for building these road networks also rise significantly. Therefore, it is essential to find a balance when choosing the optimal topological solution for an RMRN. These solutions need to maximize traffic flow while minimizing the complexity of the RMRN. To achieve this goal, a new multiagent hybrid clustering-assisted genetic algorithm (MA-HCAGA). The proposed algorithm combines the use of binary-coded crossovers and mutations as genetic operators, and biobjective discrete particle swarm optimization (BODPSO) techniques to improve the evolutionary search process. In addition, the algorithm combines the use of finite-state machines (FSMs) to control the transitions between the states of agent-processes and the fuzzy clustering technique (FCA) to estimate the swarm and select clusters for interaction among the groups of agent-processes and particle swarms. The superior performance of the MA-HCAGA algorithm in evolutionary synthesis of RMRNs has been demonstrated through comparisons with other well-known multiobjective optimization methods. MA-HCAGA has been successfully applied in the evolutionary synthesis of RMRNs, allowing a decision maker to select the optimal RMRN topologies along the approximate Pareto front by selecting specific solutions. A traffic flow simulation model, aggregated with the MA-HCAGA algorithm, has been developed to simulate vehicle flow at various configurations of RMRNs. The results of this study show the effectiveness of the proposed method for configuring RMRNs in order to optimize vehicle outflow and reduce the complexity of RMRNs.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53448-53474"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937695","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification and Time-Frequency Localization of Arbitrary LPWAN Signals With Radial Deformable DETR","authors":"Chun Ho Kong;Haibo Hu","doi":"10.1109/ACCESS.2025.3554080","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554080","url":null,"abstract":"With the increasing adoption of Internet-of-Things (IoT) technologies, numerous devices utilizing protocols such as Sigfox and LoRa are now widely available inexpensively and operate in unlicensed ISM bands. However, challenges such as inventory management, unauthorized usage, and network performance must be addressed. Future adoption of emerging IoT protocols with various modulation schemes, bandwidth, and data rates can further complicate this. Therefore, it is important not only to classify but also to localize the frequency, bandwidth, and time of these LPWAN signals on the air for management, security, or band planning purposes. SOTA algorithms usually look through the whole received signal on the time domain or frequency domain only to perform classification tasks, without finding out the corresponding time-frequency location of the signal. This paper proposes to classify and localize time-frequency locations of LPWAN signals by an enhanced version of Deformable DEtection TRansformer (Deformable DETR). We devise an attention radius suitable for processing Low Power Wide Area Network (LPWAN) Spectrogram traces extracted from Software Defined Radios (SDRs) IQ data with Short-Time Fourier Transform (STFT). Inspired by Large Language Models (LLMs), sequences of STFT vectors from SDR IQs can leverage attention mechanisms, and finding out LPWAN signals in spectrograms is similar to object detection tasks in computer vision. Our method eliminates the need for hand-crafting CNN layers or signal processing pipelines for different LPWAN protocols provided that sufficient training samples are available. Therefore, we build a fully annotated dataset for Lora and Sigfox in multiple frequencies, bandwidths, packet data, and time, as well as data augmentation techniques that serve both training and validation datasets for our modified Deformable DETR model. The experimental results demonstrate an average precision of over 89.5% for LoRa signals and over 79.8% when mixed with ultra-narrow-band signals.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53065-53083"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Solar Angle and Cloud Shadows on 3D Reconstruction of Rolling Stock Cargo","authors":"Carlina M. Ostrand;Adam D. Reiman;Frank W. Ciarallo;Scott L. Nykl;Clark N. Taylor;Joshua F. Krutz","doi":"10.1109/ACCESS.2025.3554409","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554409","url":null,"abstract":"Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth measurements. The high accuracy of such systems offers detailed, real-time insights into optimized cargo loading under unpredictable outdoor conditions. The findings, informed by both quantitative and qualitative analyses, reveal the impact of solar position, cloud coverage, and camera placement on the completeness of the point clouds, quality of the depth points, and accuracy of the measurements. This study provides insights to push the boundaries of cargo logistics possibilities in challenging outdoor environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53581-53593"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Evolutionary Toolchain for Morphological Filter Mapping on Many-Core Architectures","authors":"Emerson C. Pedrino;Denis P. Lima;Igor F. Gallon;Valentin O. Roda;Naijia Liu;Gianluca Tempesti","doi":"10.1109/ACCESS.2025.3554478","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554478","url":null,"abstract":"Many-core systems are systolic architectures consisting of an arbitrarily large number of processing nodes connected by a point-to-point communication network. Their architecture makes them ideally suited for the implementation of data-flow algorithms, of which Mathematical Morphology (MM) filters are a typical example. However, the performance of data-flow applications on many-core systems is highly dependent on the quality of the mapping of the application tasks to the computational cores. Decomposing the structuring elements of morphological operations improves their performance, however, performing such decomposition on many-core systems leads to increased communication. The need to find a balance between performance and communication is a representative example of the general problem of mapping optimizations. The approach presented in this paper explores a two-phase design-time optimization toolchain based on evolutionary algorithms: a front-end single-objective algorithm decomposes a MM filter using smaller operators, while a back-end multi-objective (fault-tolerance, energy, and communication) algorithm searches for optimal mappings of the filter on a specific many-core system, taking into account the architectural parameters of the hardware. The output of the toolchain is a Pareto front of mapping solutions, allowing the designer to select an implementation that matches application-specific requirements. A set of standard benchmark applications was used to determine the optimal parameters for the algorithms, which were then validated on two real-world application examples involving the detection of features in high-resolution PCB images. Two application mapping experiments focusing on energy constraints were conducted, in which the proposed procedure was compared to deterministic mapping techniques. The evolutionary procedure was observed to offer a significant advantage over the deterministic approach, with percentage gains of up to 73.78% for smaller grids.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54350-54366"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing","authors":"Andreas Andreou;Constandinos X. Mavromoustakis;Evangelos K. Markakis;Athina Bourdena;George Mastorakis","doi":"10.1109/ACCESS.2025.3554024","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554024","url":null,"abstract":"The rapid advancement of Artificial Intelligence (AI) is reshaping industries and driving global innovation. However, the increasing complexity of AI models demands substantial data and computational resources, leading to significant energy consumption and environmental impact. This article explores the integration of quantum computing and end-to-end automation strategies in cloud-edge architectures. It proposes a hybrid quantum-classical AI framework that enhances training efficiency and reduces data and processing intensity by minimizing energy consumption. The framework leverages automated model orchestration, adaptive resource allocation, and intelligent data processing at the edge to improve system efficiency. In addition, it addresses ethical considerations, including privacy, fairness, and trustworthiness, to ensure alignment with human values. This approach significantly improves AI performance while fostering a sustainable and ethical AI ecosystem.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54622-54635"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG Multi-Mode Oscillatory Brain State Allocation Using Switching Spectral Gaussian Processes","authors":"Yunier Prieur-Coloma;Felipe A. Torres;Nelson J. Trujillo-Barreto;Wael El-Deredy","doi":"10.1109/ACCESS.2025.3554137","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554137","url":null,"abstract":"We propose a new model for the non-stationary brain state allocation problem from electroencephalography (EEG) data, based on spectral features and their interaction. Spontaneous EEG data are modeled as continuous Gaussian Processes (GPs) emissions governed by discrete states, represented by a hidden semi-Markov model, that switch in time (HsMM-SGP). The GPs are defined by multivariate spectral kernels, covariance functions parameterized in the frequency domain. The multivariate spectral kernels describe oscillatory modes at specific frequencies and their interactions across channels, encapsulating periodicity, amplitude, and spread. Multivariate spectral kernels enable the GPs to represent temporal patterns with fine-grained frequency-specific structures and interactions, a unique spectral “fingerprint” per state, making it particularly suited for capturing non-stationary oscillatory behaviour in the neural time series. The model parameters were estimated using the Expectation-Maximization approach. The inference scheme was validated on data generated from the HsMM-SGP generative model to evaluate the accuracy in recovering the ground truth parameters. Next, we generated time-series from a metastable connectome-connected whole brain network to demonstrate the HsMM-SGP’s capability to infer meaningful oscillatory modes that reflect the changes in the underlying dynamics due to varying structural connectivity parameters. Finally, a practical application of the HsMM-SGP is illustrated using EEG data from a healthy control and an AD patient. We show that the inferred brain states exhibit distinct spectral properties across both conditions, with the AD states marked slower frequencies. We conclude that the proposed HsMM-SGP offers a method for estimating physiologically meaningful dynamical brain states.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"56053-56066"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937758","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Cost Multifunctional Assistive Device for Visually Impaired Individuals","authors":"Richard Král;Patrik Jacko;Tibor Vince","doi":"10.1109/ACCESS.2025.3554366","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554366","url":null,"abstract":"Visually impaired individuals face challenges in mobility, object recognition, and text reading. Existing assistive tools provide partial solutions but lack full integration. This paper presents a low-cost, multifunctional assistive device in the form of wearable glasses, integrating real-time text recognition, obstacle detection, and remote assistance. Built around a Raspberry Pi Zero 2W, the system features an Arducam IMX519 autofocus camera and a tactile button interface for user-friendly control. Real-time audio feedback enhances situational awareness, promoting greater independence. The device was tested at the Rehabilitation Center for the Visually Impaired in Levoča. Performance evaluations confirm high text recognition accuracy and improved navigation support. A comparative analysis highlights its affordability and functionality over commercial alternatives. While effective, the device requires processing speed optimization and hardware miniaturization. Future improvements will explore haptic feedback and AI-driven scene analysis to enhance usability. This research contributes to affordable and inclusive assistive technology, improving accessibility for visually impaired individuals.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"56326-56337"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Level Cell Structure for Capacitor-Less 1T DRAM With SiGe-Based Separated Data Storing Regions","authors":"Eungi Hwang;Jang Hyun Kim;Sangwan Kim;Garam Kim","doi":"10.1109/ACCESS.2025.3553802","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553802","url":null,"abstract":"One-transistor dynamic random-access memory (1T DRAM) offers significant advantages in fabrication process and scalability over the traditional one-transistor one-capacitor (1T-1C) DRAM due to its simplified structure that eliminates the need for capacitors. However, a limitation arises from its single-bit data storage capability, which necessitates scaling down to improve integration density. In this paper, we propose a multi-level cell structure for 1T DRAM to overcome and improve upon these limitations. Through technology computer-aided design (TCAD) simulations, the memory operation of the proposed device is validated, and it is confirmed that using Si0.8Ge0.2 in the data storing region significantly enhances the sensing margin compared to Si. Additionally, the proposed structure is shown to offer advantages over the conventional structure in terms of current variation.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52528-52537"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}