IEEE Access最新文献

{"title":"Message From the New Editor-in-Chief of IEEE Access","authors":"Mehrdad Saif","doi":"10.1109/ACCESS.2025.3540535","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3540535","url":null,"abstract":"","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"33682-33682"},"PeriodicalIF":3.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10903664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513027","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":"STE-NTP: A Long-Short Period Aware Network Traffic Prediction Model","authors":"Longfei Li;Kyungbaek Kim","doi":"10.1109/ACCESS.2025.3545117","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3545117","url":null,"abstract":"As the scale of network rapidly expands, the density and complexity of network connections have reached unprecedented levels, increasing the complexity of network management. Software-Defined Networking (SDN) enables efficient network modeling techniques by providing a controller interface, thereby implementing Network Traffic Prediction (NTP) and directly controlling underlying network hardware. However, existing NTP methods face challenges in handling the highly nonlinear and frequently bursty characteristics of network traffic, particularly in capturing and analyzing the spatiotemporal features of the traffic. To address this issue, this paper proposes an innovative NTP model, STE-NTP:Time-Space Encoding Based Network Traffic Prediction Model. This model utilizes advanced spatial and temporal encoding techniques to comprehensively process both spatial and temporal information, thereby improving prediction accuracy and efficiency. Additionally, an LTST-Extraction Block(long-term and short-term Extraction Block) is designed to enhance the model’s ability to predict long-term and short-term events in network traffic data through Long-term and Short-term feature extraction techniques.To further validate the model’s performance, 50,000 time units covering 200 routing schemes were simulated on the NSFNET and Geant2 network topologies using OMNeT++. The proposed STE-NTP model was then compared against other advanced prediction models in both short-term and long-term forecasting tasks.The results demonstrate that proposed STE-NTP exhibits significant advantages across multiple key performance metrics. These experiments not only validate the effectiveness of the STE-NTP model in predicting complex network traffic but also highlight its potential value in practical applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"35574-35587"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521562","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":"Optimized Flexible Microheater With Integrated Electrodes and Microchannel for On-Chip Bacterial Incubation and Electrochemical Detection","authors":"Sonal Fande;Khairunnisa Amreen;D. Sriram;Sanket Goel","doi":"10.1109/ACCESS.2025.3544132","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544132","url":null,"abstract":"The exponential rise in antimicrobial resistance (AMR) has gained growing interest in developing point-of-care devices capable of conducting inexpensive on-site bacterial infection testing. While these systems aim to incorporate more complex diagnostic methods into simple portable chips, they remain tethered to laboratory incubators requiring high-power inputs. Even though bacterial incubators play a vital role in providing optimal growth conditions for bacterial culture, they lack portability, which restricts their utility in real-time applications. A simple, flexible, portable incubation system holds significant potential for developing point-of-care kits. Herein, a flexible silver ink-based resistive microheater has been fabricated using inkjet printing. The fabricated microheater serves a multifaceted purpose, allowing for efficient growth conditions for culturing bacteria, their on-site quantification, and antibiotic susceptibility testing. The in-house fabricated microheater has been successfully employed for bacteria culturing at <inline-formula> <tex-math>$37~^{circ }$ </tex-math></inline-formula> C and developing a bacteria-on-chip platform for AMR detection. The microheater performance has been parametrically optimized using Multiphysics simulations and the design of experiments. The results obtained have been highlighted, achieving an appropriate incubation temperature of 37°C at an electric potential as low as 1.5 V with minimal thermal loss, ensuring superior temperature uniformity for 72-hour incubation and costing less than US 0.25 (₹ 20). Further, the fabricated microheater is reusable, stable, and water-resistant and has significant potential for developing affordable and turnkey point-of-care diagnostic kits for real-time applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36590-36600"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521445","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":"RepVGG-MEM: A Lightweight Model for Garbage Classification Achieving a Balance Between Accuracy and Speed","authors":"Qiuxin Si;Sang Ik Han","doi":"10.1109/ACCESS.2025.3544631","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544631","url":null,"abstract":"Currently, existing garbage image classification models predominantly operate on low-end devices and encounter significant challenges, including limitations in computing resources, storage capacity, and classification accuracy. This paper proposes an improved lightweight model, RepVGG-MEM, specifically designed to address the resource constraints of low-end devices. The backbone of this model is derived from the lightweight RepVGG architecture, augmented by the integration of a multi-scale convolutional attention module to enhance high-quality feature extraction. Experimental results demonstrate that the RepVGG-MEM model outperforms its counterparts, achieving an accuracy of 93.26%, with a parameter count of 7.2 million and a floating-point operations (FLOPs) of 1.41 billion. This performance reflects a commendable balance between accuracy and processing speed. Furthermore, the model’s redundancy is minimized through pruning techniques, which significantly reduce both complexity and computational overhead. The optimal pruned version of the model is designated as RepVGG-MEM5. In this iteration, the parameter count is further reduced to 1.2 million and the FLOPs decrease to 0.55 billion, with a minor accuracy decline of only 1.17%. These findings indicate that it is possible to significantly reduce the model’s parameters and FLOPs without a substantial loss in accuracy, thereby enhancing the overall performance of the model while achieving an optimal balance of accuracy and speed. As a result, this research contributes to the development of an efficient and lightweight convolutional neural network model for garbage classification.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36451-36469"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521499","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":"Assessing the Suitability of Distributed Energy Resources in Distribution Systems Based on the Voltage Margin: A Case Study of Jeju, South Korea","authors":"Jun-Hyuk Nam;Seong-Jun Park;Dong-Il Cho;Yun-Jin Cho;Won-Sik Moon","doi":"10.1109/ACCESS.2025.3544744","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544744","url":null,"abstract":"With the growing need for transitioning to clean energy, increasing the share of renewable energy sources has become a global priority. The integration of distributed energy resources (DERs), such as photovoltaic (PV) and wind turbine (WT) systems, is crucial for optimizing the current distribution systems. Despite its benefits, DER integration presents technical challenges. DERs can cause rapid power output changes, leading to voltage instability and overvoltage, thereby impacting the power quality and equipment reliability. Mitigating this instability often requires costly grid infrastructure upgrades. Therefore, optimizing existing networks is essential to reduce these costs. To achieve this, the voltage profile of the target area must be thoroughly assessed and locations suitable for DER integration must be identified to ensure stable operation and voltage stability. This study proposes a new evaluation index, the Voltage Margin Evaluation Index (VMEI), to address the limitations of the existing indices. This index evaluates voltage stability and promotes DER integration in a region. Using the VMEI, we assessed the suitability of DER integration in Jeju, South Korea. Results demonstrate that integrating a 0.5 MW DER at Node 37 in Feeder 5, the optimal point with a VMEI of 1.00066, maintained the maximum voltage at 22.97 kV within the regulated range and improved voltage stability. Conversely, Node 29 in Feeder 4, with the lowest VMEI of 0.97262, resulted in a maximum voltage of 23.84 kV, exceeding the voltage regulation range, demonstrating that VMEI effectively identifies suitable DER integration points. This study provides valuable insights for improving DER integration into distribution systems, aiding the global transition to sustainable energy.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36263-36272"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527592","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":"Improving Power Distribution Resilience Through Optimal PV and BES Allocation With a Cost-Based Optimization Framework for Normal and Emergency Conditions","authors":"Mohammad Taghi Ettehad;Reza Mohammadi Chabanloo;Mohammad Taghi Ameli","doi":"10.1109/ACCESS.2025.3544662","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544662","url":null,"abstract":"Recent natural disasters and man-made attacks have imposed substantial challenges on power distribution companies and consumers. The integration of photovoltaic (PV) systems into power distribution networks has risen due to environmental, technical, and economic factors. Additionally, technological advancements have made it possible to provide reactive power using PV systems and battery energy storage (BES) systems. This article proposes a comprehensive framework for the optimal allocation of PV and BES systems within the power distribution system to minimize energy losses and energy not served (ENS) during normal conditions, as well as load interruption under emergency conditions. The framework models the formation of small microgrids, accounting for operational and physical limitations, coordinating them with the network recovery process, and considering various production and load scenarios to maximize the restoration of interrupted loads during emergency conditions. An analysis has been conducted to determine the penetration levels of BES in power distribution systems under these conditions. A Mixed-Integer Quadratic Programming (MIQP) formulation is employed for cost optimization, with the model coded in MATLAB and implemented on a modified IEEE 33-bus network. Results demonstrate that the proposed method significantly enhances the distribution network’s resilience during emergencies, achieving a 22.3% reduction in load interruptions and a 26.5% decrease in associated costs. Additionally, energy losses are reduced by 6.7%, while ENS improves by 7.2% compared to configurations optimized solely for normal conditions. This research underscores the importance of strategically integrating PV and BES systems to improve performance metrics in normal and emergency scenarios within power distribution networks.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36436-36450"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900375","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521561","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 Heuristic Approach for the Nationwide Charging Station Location Problem for Intercity Trips","authors":"Yavuz Delice;Ayyuce Aydemir-Karadag;Halit Ozen","doi":"10.1109/ACCESS.2025.3544976","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544976","url":null,"abstract":"Electric vehicles (EVs) are vital in achieving a sustainable and eco-friendly transportation system. However, the transition to EVs faces significant challenges, mainly range anxiety, which is the fear of running out of battery power without access to charging facilities. Identifying locations for Electric Vehicle Charging Stations (EVCS) to address this issue and promote EV adoption is crucial, especially for intercity travel. The study proposes a three-stage solution methodology utilizing the Variable Transportation Demand Model (VTDM) established for nationwide transportation planning. The proposed approach includes identifying EVCS locations, with path-based traffic demands derived from the VTDM’s generalized cost-based shortest path methodology for different trip purposes. The proposed approach was validated using real-world big data from the Türkiye National Transportation Master Plan (TNTMP). The results revealed that 2,161 charging stations are required to ensure uninterrupted travel throughout the network. Furthermore, the study identifies a discrepancy in the availability of charging station infrastructure across different regions, with the majority of new facilities concentrated in the eastern region of Turkey. Addressing this infrastructure gap is crucial for accelerating the adoption of electric vehicles (EVs), reducing carbon emissions, and promoting sustainable transportation systems at the national level.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36348-36358"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10901946","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521320","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":"ATT-BLKAN: A Hybrid Deep Learning Model Combining Attention is Used to Enhance Business Process Prediction","authors":"Junyi Xu;Xianwen Fang","doi":"10.1109/ACCESS.2025.3545071","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3545071","url":null,"abstract":"The role of predictive business process tasks in business process management is significant, as they are capable of anticipating potential process events and implementing timely interventions to address discrepancies between the anticipated and actual workflow. Nevertheless, existing deep learning-based predictive methods are unable to adequately address the current problem due to shortcomings in the training data, the model itself, or the architectures employed. In this paper, we propose a novel training framework for business process prediction based on improved BiLSTM-KAN, which addresses the issue of adaptability to continuous time data. This is achieved by enhancing the BiLSTM model’s ability to capture long-term dependencies through the addition of Agent Attention, while utilising KAN in place of the traditional Multi-Layer Perceptron (MLP) to improve prediction performance and mechanism interpretability. The results demonstrate that the proposed method outperforms all baseline methods in terms of prediction accuracy. This is evidenced by experiments conducted on five real publicly available event logs, which yielded improvements in accuracy of 12.4%, 7.16%, 9.77%, 12.27%, and 5.98%, respectively. The proposed method offers novel insights into the domain of predictive business processes and demonstrates the considerable potential of KAN in the field of predictive analytics.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36175-36189"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527587","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":"Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems","authors":"C. Swetha Priya;F. Sagayaraj Francis","doi":"10.1109/ACCESS.2025.3544864","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544864","url":null,"abstract":"Over the past decade, major cities have faced significant traffic congestion, accidents, and pollution due to increased vehicle usage, urbanization, and migration. An Intelligent Transportation System (ITS) can enhance transportation planning and alleviate congestion. ITS utilizes traffic prediction models to help prevent traffic bottlenecks, improve mobility and safety, and reduce environmental impacts. However, developing these models involves several challenges, including understanding spatiotemporal nonlinearities, making accurate predictions, minimizing prediction time, and reducing model complexity. Many existing approaches integrate Convolutional Neural Networks (CNNs) and variants of Recurrent Neural Networks (RNNs) to analyze spatially correlated traffic data over time. Nevertheless, these hybrid models often require significant storage space, contain numerous learnable parameters, and involve extensive training, validation, and testing times. To address these challenges, we propose a novel methodology that combines a genetic algorithm (GA) with Random Forest Cross-Validation (RF-CV) to evaluate input features and select the most relevant subset. Additionally, we developed a Multi-Objective Genetic Algorithm (MOGA)-enhanced RNN model to optimize hyperparameters and achieve accurate traffic speed predictions. Our proposed methodology balances the trade-offs between prediction accuracy, model size, and computational efficiency by identifying an optimal set of relevant features and hyperparameters. We evaluated our model using the Performance Measurement System (PeMS)-10 dataset and compared its performance against baseline and advanced models from existing literature. Our model achieved a Mean Absolute Error (MAE) of 0.028993, an <inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula> score of 0.999490, and training, validation, and testing times of 81.64 seconds, 0.15 seconds, and 0.18 seconds, respectively. Additionally, the model size was 203,118 bytes, with 14,617 parameters. A comprehensive comparative study demonstrates that our approach outperforms state-of-the-art models in both prediction accuracy and computational efficiency.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"35688-35706"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521507","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":"DPF-Bi-RRT*: An Improved Path Planning Algorithm for Complex 3D Environments With Adaptive Sampling and Dual Potential Field Strategy","authors":"Lin Ge;Swee King Phang;Nohaidda Sariff","doi":"10.1109/ACCESS.2025.3544546","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544546","url":null,"abstract":"This research work, we introduce a path planning algorithm called DPF-Bi-RRT* which integrates a Dual Potential Field mechanism with a Targeted Sampling Strategy to address path planning issues in complex 3D spaces. The algorithm achieves a good trade off between the global path optimization and precise local obstacle avoidance by combining dual-attraction and dual-repulsion mechanisms. The algorithm achieves effective exploration of path regions of high quality by dynamically adjusting the sampling distribution. Additionally, a biased random sampling strategy improves computational efficiency by directing sampling resources toward sections with higher promise of optimal paths, dramatically reducing computational cost. The dual potential field model lead to more flexible method in collision avoidance and can improve the precision of collision avoidance, especially in cluttered dynamical spaces. We perform comparative simulations of DPF-Bi-RRT* versus RRT*, Bi-RRT*, and APF-Bi-RRT* across three defined environments to demonstrate that DPF-Bi-RRT* results in lower average node counts, less computational time, and longer path lengths than all three. Results validate its capability of generating smooth, collision free and globally optimized paths making it especially suitable for autonomous aerial vehicle (AAV) navigation in complex 3D environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"35958-35972"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527607","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}