IEEE Access最新文献

{"title":"Corrections to “Analysis and Modeling of Direct Ammonia Fuel Cells for Solar and Wind Power Leveling in Smart Grid Applications”","authors":"Miswar A. Syed;Osamah Siddiqui;Mehrdad Kazerani;Muhammad Khalid","doi":"10.1109/ACCESS.2025.3561984","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3561984","url":null,"abstract":"Presents corrections to the paper, (Corrections to “Analysis and Modeling of Direct Ammonia Fuel Cells for Solar and Wind Power Leveling in Smart Grid Applications”).","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"71081-71081"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888396","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":"Blockchain-Based Crowdsourcing Framework for Machine Learning Ground Truth","authors":"Asma S. Alzahrani;Dimah H. Alahmadi;Nesreen M. Alharbi;Hana A. Almagrabi","doi":"10.1109/ACCESS.2025.3564534","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3564534","url":null,"abstract":"Machine learning has evolved from a lab curiosity to a widely used technology that is fundamentally reliant on ground truth data for model training and evaluation. This research addresses the challenges in obtaining accurate ground truth data due to limited domain expertise, sparse and unrepresentative datasets, and the high costs associated with data acquisition. The quality of this data significantly influences the reliability of machine learning models, prompting research into methods to improve ground-truth reliability. This research proposes a framework that utilize blockchain-based crowdsourcing for ground-truth data annotation. Blockchain technology, with its decentralized immutable ledger system, offers a secure method for data verification and collection from decentralized entities. The proposed framework was implemented in an Ethereum network environment using blockchain technology and smart contracts. Next, we evaluated the collected ground truth by measuring the inter-rater agreement among the participants. The experimental results indicate that blockchain can enhance annotation consistency, showing a higher reliability of crowd-sourced data compared to expert opinions. Most annotator pairs demonstrated moderate to strong agreement, confirming the potential of blockchain technology in improving ground truth data annotation.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"73041-73055"},"PeriodicalIF":3.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896292","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":"Chip Implementation of Two New VCII-Based Voltage/Transimpedance-Mode KHN-Equivalent Biquads","authors":"Hua-Pin Chen;San-Fu Wang;Ming-Jin-Yu;Liang-Yen Chen;Yu-Hsi Chen","doi":"10.1109/ACCESS.2025.3564555","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3564555","url":null,"abstract":"This paper presents two new VCII-based Kerwin-Huelsman-Newcomb (KHN) equivalent biquad circuits, each comprising three second-generation voltage conveyors (VCIIs), two grounded capacitors, and five resistors. Either voltage mode (VM) or trans-impedance mode (TIM) can operate in each proposed circuit configuration. Transfer function analysis using a VM inverting bandpass (IBP) filter yields two additional non-inverting/inverting KHN biquad transfer functions for the two proposed VM/TIM KHN-equivalent biquads. The first proposed VM/TIM KHN-equivalent biquad can simultaneously implement an IBP filter, a non-inverting low-pass (NLP) filter, and an inverting high-pass (IHP) filter. In contrast, the second proposed VM/TIM KHN-equivalent biquad can simultaneously implement an IBP filter, an inverting low-pass (ILP) filter, and a non-inverting high-pass (NHP) filter. Each proposed VM/TIM KHN-equivalent biquad features three low-impedance voltage outputs in the designed circuit, eliminating the need for additional voltage buffers (VBs) in the circuit measurements. The two proposed KHN-equivalent biquads are integrated into a single chip, occupying a total area of 1.44 mm2. This technology uses the TSMC <inline-formula> <tex-math>$0.18~mu $ </tex-math></inline-formula>m 1P6M CMOS process, with the chip operating at a supply voltage of ±0.9 V. The measured power dissipation of the first KHN-equivalent biquad is 2.7 mW, while the measured power dissipation of the second one is 3.24 mW. The measured spurious-free dynamic range (SFDR) of the first KHN-equivalent biquad is 41.18 dBc, while the measured SFDR of the second one is 40.94 dBc. With an input voltage of 1.2 Vpp, the measured total harmonic distortion (THD) values for both KHN-equivalent biquads are below 1 %. The proposed two KHN-equivalent biquads have the advantages of high density, system integration, efficiency, low cost, low power consumption, and effective utilization of chip layout area. Simulations and on-chip measurements are carried out for both KHN-equivalent biquads to validate the theoretical design and demonstrate their on-chip feasibility.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"73056-73075"},"PeriodicalIF":3.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896260","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":"Real Estate Attribute Value Extraction Using Large Language Models","authors":"Michal Kvet;Miroslav Potočár;Slavomír Tatarka","doi":"10.1109/ACCESS.2025.3564511","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3564511","url":null,"abstract":"Attribute value extraction (AVE) is critical in transforming unstructured text into structured data for various applications. While existing datasets for AVE predominantly focus on e-commerce and English language data, there is a lack of publicly available datasets tailored to other domains. This paper introduces the Real Estate Attribute Value Extraction (RAVE) dataset, specifically designed for extracting structured attributes from unstructured real estate advertisements. The RAVE dataset consists of manually annotated Slovak real estate listings, which have been translated into English for broader applicability. The paper evaluates the performance of multiple publicly available large language models in solving the AVE task on RAVE. Through extensive experimentation, we analyse the impact of additional attribute descriptions, selecting relevant sentences, and using ground-truth-based attribute definition in structured output generation. The findings indicate that providing a schema with only relevant attributes (Oracle Attributes) significantly enhances performance and reduces computational overhead while improving the F1 score. Under basic conditions without modifications at the input, the largest model tested, Qwen2.5:32b, achieved a micro F1 score of 10.04%. Applying all tested input modifications (Oracle Attributes, Oracle Sentences, and Additional Descriptions) allowed the largest model tested to achieve a micro F1 score of 97.92%, demonstrating the effectiveness of these techniques in improving extraction accuracy and efficiency. The RAVE dataset is publicly available, facilitating further research in AVE and real estate information extraction.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"73076-73095"},"PeriodicalIF":3.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896550","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":"Fire and Smoke Detection Based on Improved YOLOV11","authors":"Zhipeng Xue;Lingyun Kong;Haiyang Wu;Jiale Chen","doi":"10.1109/ACCESS.2025.3564434","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3564434","url":null,"abstract":"Fire and smoke detection is an important measure to ensure the safety of people’s lives and property, as well as a crucial link in maintaining ecological balance and supporting scientific research. Traditional object detection methods rely more on manually designed features and rules. Although they are relatively simple to implement, their performance is limited in complex and variable practical applications. In contrast, deep learning-based methods can automatically learn deep features in data and have higher accuracy and stronger generalization ability. However, complex backgrounds, large environmental changes, and data requirements pose great challenges to high-precision outdoor smoke detection. To address these issues, this paper proposes an improved model, YOLOV11-DH3, based on YOLOV11. In this paper, the core DCN2 (Deformable Convolutional Networks2) of the YOLOV11 Head is replaced with the DCN3 module to form a new detection head. In addition, the loss function CIOU in YOLOV11 is replaced with IOU to consider the irregular shape of fire and smoke and the problem of multi-scale targets. To evaluate the performance of the algorithm, comprehensive experiments were conducted on two distinct datasets: a public fire and smoke dataset provided by Baidu Paddle featuring close-range views and a public wildfire smoke dataset from the YOLO official website with distant outdoor perspectives. The experimental results show that on the Baidu Paddle dataset, the average accuracy of the model is improved by 1.4% compared to the original model, reaching 58%, the F1 score is improved by 2%, reaching 58%, with a precision of 91.6% and recall of 90%. Our cross-dataset analysis provides valuable insights into model performance across different detection scenarios. The proposed model demonstrates the ability to accurately detect fire and smoke in complex backgrounds, and this progress is of great significance for protecting people’s lives and maintaining ecological balance.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"73022-73040"},"PeriodicalIF":3.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896381","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 Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches","authors":"Sam Ansari;Soliman Mahmoud;Sohaib Majzoub;Eqab Almajali;Anwar Jarndal;Talal Bonny","doi":"10.1109/ACCESS.2025.3564032","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3564032","url":null,"abstract":"The recognition of modulation types in received signals is essential for signal detection and demodulation, with broad applications in telecommunications, defense, and wireless communications. This paper introduces a pioneering approach to automatic modulation recognition (AMR) through the development of a highly optimized long short-term memory (LSTM) network. The proposed framework is engineered to capture intricate temporal dependencies within modulated signals, leveraging a gated architecture that effectively mitigates the vanishing gradient problem. This innovation markedly improves recognition accuracy, particularly in low-SNR conditions where traditional methods are often limited. A defining contribution of this work is the introduction of a novel, adaptive temporal-spectral feature learning mechanism, which seamlessly integrates both temporal and spectral characteristics of the signal. This paradigm eliminates the need for manual feature extraction, enhances interpretability, and significantly boosts classification efficiency. Furthermore, the proposed framework is designed for low-complexity deployment, ensuring its scalability and suitability for next-generation wireless networks and real-time communication systems. The proposed architecture is capable of distinguishing between seven modulation classes: BASK, 4-ASK, BFSK, 4-FSK, BPSK, 4-PSK, and 16-QAM. Performance is evaluated across a broad range of signal-to-noise ratios (SNR), from −10 dB to +30 dB, through extensive simulations. Experimental results demonstrate that the model achieves a recognition accuracy of 99.87% at an SNR of -5 dB, outperforming several conventional machine learning techniques, including multi-layer perceptron (MLP), radial basis function (RBF) networks, adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), naïve Bayes (NB), support vector machines (SVM), probabilistic neural networks (PNN), k-nearest neighbors (KNN), and ensemble learning models, as well as recurrent neural networks (RNNs). Comparative analysis reveals that the proposed framework outperforms conventional machine learning techniques, with accuracy improvements ranging from 1.77% to 34.03% over the best- and worst-performing methods. Additionally, the proposed model achieves a performance gain of 2.02% over the deep learning (DL)-based RNN, further highlighting its superior capability in AMR.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"72526-72543"},"PeriodicalIF":3.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896150","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":"Monte Carlo Integration With Efficient Importance Sampling for Underwater Wireless Optical Communication Simulation","authors":"Ruqin Xiao;Pierre Combeau;Lilian Aveneau","doi":"10.1109/ACCESS.2025.3564070","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3564070","url":null,"abstract":"Underwater optical communications have been proposed for various applications, ranging from coastal protection to short-range submarine communications. The development of dedicated communication systems requires intensive simulation of use cases with efficient methods, both in terms of accuracy and computational time. However, these simulations are challenging due to the complexity of the physical mechanisms of light propagation in water, which involves numerous scattering events on the various particles constituting the propagation medium. Previous tools have primarily relied on the Prahl algorithm, based on Monte Carlo simulation, and are therefore difficult to improve. Recently, a new framework, hereafter referred to as Xiao1, has been developed using an integral formalization of the propagation and Monte Carlo integration for its computation, achieving improved computational times compared to older Prahl techniques for the same level of accuracy. This paper builds upon this framework and proposes to incorporate further importance sampling into the Monte Carlo integration algorithm. It calculates a sub-domain around the receiver for each scattering point and selects a connecting sample with importance within this sub-domain. This paper presents the complete derivation of this new method. It then presents several case studies in which the simulations demonstrate that this new method performs significantly better. Depending on the configuration, these simulations exhibit a reduction in computational times by a factor ranging from 1.09 to 4048 compared with Prahl and from 1.07 to 2134 compared with Xiao1.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"73652-73670"},"PeriodicalIF":3.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975749","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896257","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":"Wake Homing Torpedo Guidance Using a Hierarchical Deep Reinforcement Learning Framework","authors":"Kunchul Hwang;Jinwhan Kim","doi":"10.1109/ACCESS.2025.3563906","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3563906","url":null,"abstract":"This paper proposes a novel Hierarchical Deep Reinforcement Learning (HRL) framework for wake homing torpedo guidance, applying the Discrete Event System Specification (DEVS) formalism to design high-level policies and reward shaping functions. Wake homing torpedo guidance generates course commands to enable the torpedo to follow the wake trajectory of a target ship. When the target ship evades the incoming torpedo, the wake trajectory becomes curved, often causing the torpedo to lose track due to the narrow detection range of the wake detection sensor. This necessitates a sophisticated algorithm to consistently track the target ship, particularly in scenarios where the torpedo exits and re-enters the wake trajectory in noisy environments. While heuristic algorithms can handle typical wake trajectories, developing a robust solution for unknown trajectories remains a significant challenge. To address this, we apply a novel reinforcement learning approach to develop the guidance logic and compare its performance with a conventional algorithm-based method. The performance and effectiveness of the proposed approach are demonstrated through numerical simulations.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"72938-72952"},"PeriodicalIF":3.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975769","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896237","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":"Boundary Control Behaviors of Multiple Low-Cost AUVs Using Acoustic Communication","authors":"Mohammed Tarnini;Saverio Iacoponi;Andrea Infanti;Cesare Stefanini;Giulia de Masi;Federico Renda","doi":"10.1109/ACCESS.2025.3564077","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3564077","url":null,"abstract":"This study presents acoustic-based methods for the formation control of multiple autonomous underwater vehicles (AUVs). This study proposes two different models for implementing boundary and path control on low-cost AUVs using acoustic communication and a single central acoustic beacon. Both models are based on the history of relative range and do not rely on the full knowledge of the AUVs states based on a centralized beacon system. Two methods are presented: the Range Variation-Based (RVB) model completely relies on range data obtained by acoustic modems, whereas the Heading Estimation-Based (HEB) model uses ranges and range rates to estimate the position of the central boundary beacon and perform assigned behaviors. The models are tested on two formation control behaviors: Fencing and Milling. Fencing behavior ensures AUVs return within predefined boundaries, while Milling enables the AUVs to move cyclically on a predefined path around the beacon. Models are validated by successfully performing the boundary control behaviors in simulations, pool tests, including artificial underwater currents, and field tests conducted in the ocean. All tests were performed with fully autonomous platforms, and no external input or sensor was provided to the AUVs during validation. Quantitative and qualitative analyses are presented in the study, focusing on the effect and application of a multi-robot system.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"72506-72525"},"PeriodicalIF":3.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975816","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896553","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":"Efficient Computation of the K Nearest Neighbors Query Using Incremental Radius on a k²-tree","authors":"Rodrigo Torres-Avilés;Mónica Caniupán","doi":"10.1109/ACCESS.2025.3564185","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3564185","url":null,"abstract":"Proximity searches within metric spaces are critical for numerous real world applications, including pattern recognition, multimedia information retrieval, and spatial data analysis, among others. With the exponential increase in data volume, the demand for memory efficient structures to store and process information has become increasingly important. In this paper, we present an alternative algorithm for efficient computation of the K-nearest neighbors (KNN) query using the <inline-formula> <tex-math>$k^{2}$ </tex-math></inline-formula>-tree compact data structure, using the incremental radius technique. This approach offers an alternative to the existing algorithm that utilizes a priority queue over <inline-formula> <tex-math>$k^{2}$ </tex-math></inline-formula>-trees. Through both theoretical and experimental analysis, we demonstrate that our proposed algorithm is up to 2 times faster compared to the priority queue based solution, while also providing substantial improvements in memory efficiency.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"72778-72789"},"PeriodicalIF":3.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892491","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}