IEEE Access最新文献

{"title":"Performance Evaluation of Activation Functions in Deep Residual Networks for Short-Term Load Forecasting","authors":"Junchen Liu;Faisul Arif Ahmad;Khairulmizam Samsudin;Fazirulhisyam Hashim;Mohd Zainal Abidin Ab Kadir","doi":"10.1109/ACCESS.2025.3565798","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565798","url":null,"abstract":"Short-Term Load Forecasting (STLF) is essential for ensuring efficient and reliable power system operations, requiring accurate predictions of electricity demand. Deep Residual Networks (DRNs), with their ability to mitigate gradient vanishing and model complex nonlinear relationships in load data, have emerged as a powerful tool for STLF. This study evaluates the performance of various activation functions within DRN models, focusing on their impact on predictive precision and generalization. Experiments were conducted using the DRN architecture for STLF on two distinct datasets: ISO-NE and Malaysia. The findings demonstrate that activation functions significantly influence the predictive performance of DRN-based STLF models. Specifically, the DRN model using Swish achieved the best results on the ISO-NE dataset (Mean Absolute Percentage Error, MAPE = 1.3806%), while the DRN model with Hyperbolic Tangent (Tanh) excelled on the Malaysia dataset (MAPE = 4.9809%). These results underscore the importance of aligning activation function selection with dataset characteristics to optimize the performance of DRN models in STLF. This study provides valuable insights for advancing STLF research and guiding practical applications in load forecasting.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"78618-78633"},"PeriodicalIF":3.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925339","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":"Capturing Semantic Relationships Using Full Dependency Forests to Improve Consistency in Long Document Summarization","authors":"Yanjun Wu;Luong Vuong Nguyen;O-Joun Lee","doi":"10.1109/ACCESS.2025.3565823","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565823","url":null,"abstract":"There are complex discourse relationships between sentences, which can be viewed as a tree structure. This semantic structure provides important information for summarization and helps to generate concise and coherent summaries. However, current neural network-based models usually treat articles as simple sentence sequences, ignoring the intrinsic structure. To integrate discourse tree information, we propose a generative summarization model that incorporates tree structure. The article’s structure can be more accurately captured by this model, which can also produce succinct summaries by leveraging the semantic dependencies of the source material. Also, since large models are difficult to apply in downstream tasks, we try to add noise to the pre-training parameters to improve the performance of the model on the long document summarization task. Experimental results show that our model ROUGE scores outperform the state-of-the-art best models in both pubMed and arXiv datasets. We further performed human evaluation, and N-gram evaluation. The results show that our method also improves the cohesiveness and semantic coherence of abstracts.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"78895-78904"},"PeriodicalIF":3.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925057","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 PSO-CNN-LSTM Model for Seismic Facies Analysis: Methodology and Applications","authors":"Luyao Liao;Huailai Zhou;Junping Liu;Jie Zhou;Donghang Zhang;Jian Wang","doi":"10.1109/ACCESS.2025.3566005","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3566005","url":null,"abstract":"Seismic facies analysis, as a crucial step in the study of depositional facies, effectively delineates the distribution patterns of depositional facies between wells. To address the limitations of conventional manual interpretation methods, particularly their low efficiency and strong subjectivity, this study proposes a hybrid CNN-LSTM model integrated with Particle Swarm Optimization (PSO-CNN-LSTM). The model systematically extracts spatial features of seismic reflections through CNN architecture while capturing temporal waveform dependencies via LSTM networks, with PSO automatically optimizing critical parameters including initial learning rate and LSTM neuron count. Experimental results demonstrate that PSO-CNN-LSTM achieves a classification accuracy of 89.74%, surpassing CNN (81.48%), LSTM (81.63%), and basic CNN-LSTM (84.61%) models by 8.26%, 8.11%, and 5.13% respectively. The model exhibits superior performance on the SEG 2020 benchmark dataset, confirming that automated parameter optimization effectively reduces manual intervention while enhancing convergence stability. Practical applications reveal consistent interpretation outcomes between the model’s predictions (using limited training samples) and expert analyses, providing reliable evidence for identifying favorable zones in heterogeneous carbonate reservoirs. The established intelligent waveform classification workflow validates PSO-CNN-LSTM model’s robustness and offers an efficient solution for seismic facies analysis, particularly in complex geological settings.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"84094-84111"},"PeriodicalIF":3.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10981420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072969","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":"Phase-Shift Control for Enhancing Misalignment Tolerance in UCA-Based OAM-MIMO Systems","authors":"Norifumi Kamiya;Masahiro Kawai;Eisaku Sasaki","doi":"10.1109/ACCESS.2025.3565738","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565738","url":null,"abstract":"This paper presents a novel approach for achieving high-capacity transmission utilizing sub-terahertz (THz) radio orbital angular momentum (OAM) multiplexing, specifically in scenarios where the radio link is susceptible to antenna misalignment. We focus on a radio OAM multiplexing system incorporating uniform circular antenna arrays (UCAs) at both the transmitter and receiver. While it is established that UCA-based OAM multiplexing can significantly enhance transmission capacity, its performance is notably sensitive to antenna misalignment, leading to capacity degradation. To mitigate this issue, we propose a novel closed-loop OAM system featuring adaptive phase-shift control for transmission signals. We also introduce efficient methods for optimizing the phase shift. An experimental demonstration of the radio OAM system employing the proposed phase-shift control method is provided, showcasing its efficacy in compensating for the effects of misalignment.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"76654-76664"},"PeriodicalIF":3.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913426","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":"Steel Defect Detection Based on YOLO-SAFD","authors":"Feihong Yu;Jinshan Zhang;Dingdiao Mu","doi":"10.1109/ACCESS.2025.3565587","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565587","url":null,"abstract":"In industrial production, defect detection of steel materials is critical for maintaining quality. However, traditional inspection methods are labor-intensive and error-prone, while existing deep learning-based detection approaches generally suffer from poor performance in industrial defect detection and insensitivity to small defects. This paper presents YOLO-SAFD, an advanced framework based on YOLOv5, designed to address these challenges. The proposed model incorporates two key innovations: 1) the Squeezed and Excited Asymptotic Feature Pyramid Network (SAFPN), which enhances multi-scale feature fusion and improves the detection of small defects, increasing the mean Average Precision (mAP) from 0.78 (YOLOv5 baseline) to 0.84; 2) the Diverse Branch Block (DBB), which replaces conventional convolutions to enrich feature diversity while reducing computational complexity, cutting the model parameters from 13.8M to 4.82M. Experimental results on the NEU-DET dataset demonstrate that YOLO-SAFD achieves a detection precision of 0.83, a recall of 0.75, and an mAP50:95 of 0.43, outperforming the baseline YOLOv5 and highlighting its superior detection accuracy and efficiency for real-time industrial applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"77291-77304"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979992","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925292","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":"Corporate Education for Digital Transformation in the Semiconductor Industry: Proactive Consideration in Early Implementation Phase","authors":"Hochan Lee;Sangyoon Yi","doi":"10.1109/ACCESS.2025.3565486","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565486","url":null,"abstract":"Contribution: This study addresses the gap in understanding the practical context of corporate education for Digital Transformation (DT) within the semiconductor industry. Background: The COVID-19 pandemic accelerated global digital transformation initiatives, with South Korea’s semiconductor industry implementing comprehensive DT education programs since 2020. However, its practical and empirical influence and limitations have never been rigorously examined. Research Question: What impact, barriers and improvement strategies do DT education programs have on individual and organizational DT competencies in the semiconductor industry? Methodology: Employing a qualitative research design, in-depth interviews were conducted with 20 employees from diverse job roles and educational backgrounds within the semiconductor industry. Findings: The research reveals that while general DT education improves digital literacy and fosters individual and team-level innovation, this is often realized through small-scale digital transformation initiatives. However, significant challenges hinder further potential, including short-term performance evaluation frameworks, restrictive IT policies, a lack of advanced, role-specific training, and insufficient understanding of DT concepts and strategies among organizational leaders. To address these challenges, the study recommends refining evaluation systems, easing policy constraints, and tailoring training programs to specific roles. Additionally, it emphasizes the importance of leadership-focused DT education to enhance leaders’ understanding of digital transformation, enabling them to better guide and support organizational initiatives. These measures aim to amplify the practical applications of DT efforts, fostering sustained organizational growth and innovation. Limitation and Future Work: This study faces several limitations, including an industry bias toward semiconductors, a short observation period, subjective self-reported data, inconsistent program implementation, and limited measures of organizational performance. Future research could address these gaps by conducting cross-industry comparisons, long-term assessments, quantitative ROI analyses, and investigations of leadership training and organizational culture.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"78107-78119"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979883","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925294","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":"OTFS-Based Physical Layer Authentication for UAV-Assisted Data Collection in Wireless Sensor Network","authors":"Agnes Francis Kawoya;Hua Wang","doi":"10.1109/ACCESS.2025.3565375","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565375","url":null,"abstract":"Autonomous aerial vehicle (commonly known as UAV) wireless communications have become widespread in military and civilian applications, partly due to its inherent line-of-sight (LoS) air-to-ground channels, easy deployment and high mobility. They have been adopted for data collection in the Internet of Things (IoT). For a communication scenario with a single UAV data collector in a delay-sensitive application, authentication is an important procedure which prevents illegal or malicious sensors from sending fake data to the data collector. However, traditional authentication schemes depend on cryptography, which is energy and time consuming. Thus, they are unsuitable for latency-intolerable applications and energy-constrained UAVs. This work aims to design a keyless, energy-efficient authentication scheme suitable for high-speed mobility scenarios. To this end, we propose an Orthogonal Time Frequency Space (OTFS)-based lightweight physical layer authentication (PLA) scheme. We begin by deriving the relationship between the location of the transmitter and normalized OTFS Doppler shift to develop the hypothesis test. Henceforth, we derive the probability density function (PDF) expressions of false alarm and missed detection. The PDF of the false alarm and missed detection are the basis for setting an optimal detection threshold for the authentication hypothesis test. Numerical results demonstrate that the proposed scheme holds well to fading effects for a robust and secure authentication scheme, and outperforms Orthogonal Frequency Division Multiplexing (OFDM) for PLA. The advantages of OTFS over OFDM for PLA are well elaborated.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"78541-78554"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979952","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925185","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":"Anomaly Detection and Root Cause Analysis in Cloud-Native Environments Using Large Language Models and Bayesian Networks","authors":"Diego Frazatto Pedroso;Luís Almeida;Lucas Eduardo Gulka Pulcinelli;William Akihiro Alves Aisawa;Inês Dutra;Sarita Mazzini Bruschi","doi":"10.1109/ACCESS.2025.3565220","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565220","url":null,"abstract":"Cloud computing technologies offer significant advantages in scalability and performance, enabling rapid deployment of applications. The adoption of microservices-oriented architectures has introduced an ecosystem characterized by an increased number of applications, frameworks, abstraction layers, orchestrators, and hypervisors, all operating within distributed systems. This complexity results in the generation of vast quantities of logs from diverse sources, making the analysis of these events an inherently challenging task, particularly in the absence of automation. To address this issue, Machine Learning techniques leveraging Large Language Models (LLMs) offer a promising approach for dynamically identifying patterns within these events. In this study, we propose a novel anomaly detection framework utilizing a microservices architecture deployed on Kubernetes and Istio, enhanced by an LLM model. The model was trained on various error scenarios, with Chaos Mesh employed as an error injection tool to simulate faults of different natures, and Locust used as a load generator to create workload stress conditions. After an anomaly is detected by the LLM model, we employ a dynamic Bayesian network to provide probabilistic inferences about the incident, proving the relationships between components and assessing the degree of impact among them. Additionally, a ChatBot powered by the same LLM model allows users to interact with the AI, ask questions about the detected incident, and gain deeper insights. The experimental results demonstrated the model’s effectiveness, reliably identifying all error events across various test scenarios. While it successfully avoided missing any anomalies, it did produce some false positives, which remain within acceptable limits.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"77550-77564"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943821","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":"Enhancing Smart Microgrid Resilience and Virtual Power Plant Profitability Through Hybrid IGWO-PSO Optimization With a Three-Phase Bidding Strategy","authors":"T. Yuvaraj;T. Sengolrajan;Natarajan Prabaharan;K. R. Devabalaji;Akie Uehara;Tomonobu Senjyu","doi":"10.1109/ACCESS.2025.3565460","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565460","url":null,"abstract":"The increasing energy demand and rising fossil fuel prices are accelerating the transition to renewable energy, supported by government initiatives due to their environmental and economic advantages. However, challenges such as limited capacity and stability constraints hinder the widespread adoption of distributed energy resources (DERs). Virtual Power Plants (VPPs) enhance market participation by aggregating DERs, while electric vehicles (EVs) contribute to environmental sustainability by reducing emissions. Additionally, integrating distribution static compensators (DSTATCOMs) within VPPs improves microgrid stability and reactive power support. This study proposes a two-stage optimization approach to enhance network resilience and VPP profitability in a radial distribution network (RDN). The first stage focuses on minimizing resilience-related costs and energy not supplied (ENS) during natural disasters, while the second stage optimizes VPP profit using a three-phase bidding strategy, which includes the day-ahead market, real-time market, and overall market. A hybrid improved grey wolf optimization-particle swarm optimization (IGWO-PSO) algorithm is developed to solve this complex optimization problem. To demonstrate the effectiveness of the proposed approach, IGWO-PSO is compared with other hybrid optimization algorithms. Validation on a modified IEEE 33-bus RDN confirms that the proposed model enhances VPP placement and sizing, leading to improved economic, operational, and resilience metrics. Furthermore, the model accounts for uncertainties in load demand, renewable generation, energy prices, and equipment availability, ensuring a robust and adaptable energy management strategy.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"80796-80820"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943957","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":"SymbolNet: Bridging Latent Neural Representations and Symbolic Reasoning via Intermediate Feature Interpretation","authors":"Sungheon Jeong;Hyungjoon Kim;Yoojeong Song","doi":"10.1109/ACCESS.2025.3565638","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3565638","url":null,"abstract":"The interpretation of intermediate representations in deep neural networks is critical for enhancing the transparency, trustworthiness, and applicability of artificial intelligence (AI) systems. In this paper, we propose SymbolNet, a framework that extracts mid-level features from trained models and transforms them into human-interpretable symbolic representations. SymbolNet constructs a symbolic graph composed of nodes and edges that capture both the semantic meaning and relational structure within the model’s internal reasoning process. This symbolic decoding bridges the model’s internal computations with human cognitive understanding, enabling structured and meaningful interpretation of AI behavior. Experimental results on the GTSRB dataset demonstrate that SymbolNet improves classification accuracy by 4% over the baseline and significantly enhances robustness against various noise conditions and adversarial attacks. Our work contributes to the field of explainable AI by introducing a novel approach that reveals the internal learning dynamics of non-interpretable models through symbolic reasoning.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"78221-78230"},"PeriodicalIF":3.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918657","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}