IEEE Access最新文献_第7页

Unveiling the Physics Secrets of Bajiquan: A STEAM-Integrated Teaching Approach

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3535146

Haidong Chen;Weilei Yang;Jinyong Feng;Jiasheng Lv;Ran Chen

{"title":"Unveiling the Physics Secrets of Bajiquan: A STEAM-Integrated Teaching Approach","authors":"Haidong Chen;Weilei Yang;Jinyong Feng;Jiasheng Lv;Ran Chen","doi":"10.1109/ACCESS.2025.3535146","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3535146","url":null,"abstract":"A compelling vision is emerging in education - integrating the arts with STEM, forming a holistic approach known as STEAM. Leveraging recent advances in motion analysis technology, this study explores the potential of a STEAM-based pedagogy incorporating the Chinese martial art of Bajiquan to enhance college students’ understanding and application of physics principles. An empirical study was conducted with sophomore university students to investigate the feasibility of this approach. A three-phase intervention utilizing STEAM principles was implemented, focusing on Bajiquan movement practice and analysis, supplemented by instructional videos and images created using motion analysis tools to visualize the underlying physics concepts. These materials highlighted principles such as “Conservation of momentum,” “Linear dynamics,” and “Rotational dynamics” within the context of Bajiquan techniques. Learning interest questionnaires and physics knowledge tests were administered before and after the intervention to assess its impact. One-way repeated measures ANOVA and paired samples t-tests revealed significant improvements in learning interest and physics knowledge scores following the intervention. These findings suggest that integrating Bajiquan and motion analysis within a STEAM framework can promote a deeper understanding of physics concepts and enhance students’ overall engagement with the subject matter.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19438-19455"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183826","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}

引用次数: 0

A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL)

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3535103

Pedram Tabatabaeemoshiri;Narendra Kumar;Anis Salwa Mohd Khairuddin;Daniel Ting;Vivek Regeev

{"title":"A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL)","authors":"Pedram Tabatabaeemoshiri;Narendra Kumar;Anis Salwa Mohd Khairuddin;Daniel Ting;Vivek Regeev","doi":"10.1109/ACCESS.2025.3535103","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3535103","url":null,"abstract":"The semiconductor industry plays a vital role in modern technology, with semiconductor devices embedded in almost all electronic products. As these devices become increasingly complex, ensuring quality and reliability poses significant challenges. Electrical testing on semiconductor wafers for defects is crucial, but paradoxically, the testing process itself can introduce defects. These test-induced defects could remain undetected on the wafer, proceed through assembly, and may only be discovered later by customers, leading to returns and significant yield loss. This study proposes a novel graph-based semi-supervised learning (GSSL) algorithm to identify these test-induced hidden defects on the semiconductor wafer that escape conventional methods. The algorithm, which incorporates domain knowledge in creating a graph representation of wafer, and utilizing a weighted edge label propagation model, has demonstrated its effectiveness by achieving a 68% accuracy on a real-world dataset, offering a promising approach to enhance quality control in semiconductor manufacturing.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"21678-21694"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855443","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184330","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}

引用次数: 0

Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3535232

Ioannis Kansizoglou;Konstantinos A. Tsintotas;Daniel Bratanov;Antonios Gasteratos

{"title":"Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models","authors":"Ioannis Kansizoglou;Konstantinos A. Tsintotas;Daniel Bratanov;Antonios Gasteratos","doi":"10.1109/ACCESS.2025.3535232","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3535232","url":null,"abstract":"Parkinson’s disease (PD) is a chronic neurological disorder that progresses slowly and shares symptoms with other diseases. Early detection and diagnosis are vital for appropriate treatment through medication and/or occupational therapy, ensuring patients can lead productive and healthy lives. Key symptoms of PD include tremors, muscle rigidity, slow movement, and balance issues, along with psychiatric ones. Handwriting (HW) dynamics have been a prominent tool for detecting and assessing PD-associated symptoms. Still, many handcrafted feature extraction techniques suffer from low accuracy, which is rather than optimal for diagnosing such a serious condition. To that end, various machine learning (ML) and deep learning (DL) approaches have been explored for early detection. Meanwhile, concerning the latter, large models that introduce complex and difficult-to-understand architectures reduce the system’s recognition transparency and efficiency in terms of complexity and reliability. To tackle the above problem, an efficient hierarchical scheme based on simpler DL models is proposed for early PD detection. This way, we deliver a more transparent and efficient solution for PD detection from HW records. At the same time, we conclude that a careful implementation of each component of the introduced hierarchical pipeline enhances recognition rates. A rigorous 5-fold cross-validation strategy is adopted for evaluation, indicating our system’s robust behavior under different testing scenarios. By directly comparing it against a similar end-to-end classifier, the benefits of our technique are clearly illustrated during experiments. Finally, its performance is compared against several state-of-the-art ML- and DL-based PD detection methods, demonstrating the method’s supremacy.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"21880-21890"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184363","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}

引用次数: 0

YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534782

Xin Geng;Xiao Han;Xianghong Cao;Yixuan Su;Dongxue Shu

{"title":"YOLOV9-CBM: An Improved Fire Detection Algorithm Based on YOLOV9","authors":"Xin Geng;Xiao Han;Xianghong Cao;Yixuan Su;Dongxue Shu","doi":"10.1109/ACCESS.2025.3534782","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534782","url":null,"abstract":"Regarding the current problems of false alarms and missed detections in fire detection, we propose a high-precision fire detection algorithm, YOLOV9-CBM (C3-SE, BiFPN, MPDIoU), by optimizing YOLOV9. Firstly, to tackle the shortage of both quality and quantity in the existing fire datasets, we collected 2,000 fire and smoke images to establish a dataset named CBM-Fire. Secondly, the RepNCSPELAN4 module of the YOLOv9 backbone was replaced with the C3 module containing SE Attention to improve detection efficiency while guaranteeing accuracy. Besides, we transformed the multi-scale fusion network PANet in the baseline algorithm into a bidirectional feature network pyramid BiFPN to facilitate the bidirectional flow of features, enabling the algorithm to fuse information at different scales more effectively. Finally, instead of CIoU losses, we adopted MPDIoU losses in bounding box regression, which improved the accuracy of model regression and classification. Experimental results indicate that compared with YOLOV9, the recall rate of YOLOV9-CBM has increased by 7.6% and the mAP has risen by 3.8%. The revised model demonstrates good generalization performance and robustness. Code and dataset are at <uri>https://github.com/GengHan-123/yolov9-cbm.git</uri>.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19612-19623"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854439","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105501","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}

引用次数: 0

VisualSAF-A Novel Framework for Visual Semantic Analysis Tasks

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3535314

Antonio V. A. Lundgren;Byron L. D. Bezerra;Carmelo J. A. Bastos-Filho

{"title":"VisualSAF-A Novel Framework for Visual Semantic Analysis Tasks","authors":"Antonio V. A. Lundgren;Byron L. D. Bezerra;Carmelo J. A. Bastos-Filho","doi":"10.1109/ACCESS.2025.3535314","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3535314","url":null,"abstract":"We introduce VisualSAF, a novel Visual Semantic Analysis Framework designed to enhance the understanding of contextual characteristics in Visual Scene Analysis (VSA) tasks. The framework leverages semantic variables extracted using machine learning algorithms to provide additional high-level information, augmenting the capabilities of the primary task model. Comprising three main components – the General DL Model, Semantic Variables, and Output Branches – VisualSAF offers a modular and adaptable approach to addressing diverse VSA tasks. The General DL Model processes input images, extracting high-level features through a backbone network and detecting regions of interest. Semantic Variables are then extracted from these regions, incorporating a wide range of contextual information tailored to specific scenarios. Finally, the Output Branch integrates semantic variables and detections, generating high-level task information while allowing for flexible weighting of inputs to optimize task performance. The framework is demonstrated through experiments on the HOD Dataset, showcasing improvements in mean average precision and mean average recall compared to baseline models; the improvements are 0.05 in both mAP and 0.01 in mAR compared to the baseline. Future research directions include exploring multiple semantic variables, developing more complex output heads, and investigating the framework’s performance across context-shifting datasets.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"21052-21063"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105734","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}

引用次数: 0

UAV-NavS: Three-Dimensional Navigation System of Multiple Unmanned Aerial Vehicles Using Hybrid Optimization Algorithm

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534630

Monia Digra;Upma Jain;Ram Kishan Dewangan;Himanshu Suyal

引用次数: 0

Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service Orchestration

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534282

Muhammad Asif;Talha Ahmed Khan;Wang-Cheol Song

{"title":"Leveraging Cognitive Machine Reasoning and NLP for Automated Intent-Based Networking and e2e Service Orchestration","authors":"Muhammad Asif;Talha Ahmed Khan;Wang-Cheol Song","doi":"10.1109/ACCESS.2025.3534282","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534282","url":null,"abstract":"Modern networks are increasingly complex, necessitating dynamic and automated solutions to connect user intents with network actions effectively. This study presents a new framework for automating Intent Based Networking (IBN) by combining cognitive Machine Reasoning (MR) with Natural Language Processing (NLP) and utilizing the RASA (Robust Automated Speech Assistant) architecture. RASA is a flexible open-source framework for building conversational AI, adapted for end-to-end (e2e) network orchestration. In contrast to traditional static methods, this innovative system empowers network operators to manage and optimize networks dynamically through intuitive voice commands or a Graphical User Interface (GUI). The system identifies user intents, converts them into actionable network policies, and ensures they align with real-time network states and Quality of Service (QoS) requirements via a feedback loop. Cognitive MR and AI-based optimization techniques are integrated to enhance system performance, enabling intelligent adaptation to network conditions and ensuring optimal resource allocation. A simulated testbed was created to assess the system’s performance using Containernet, a lightweight Container-Based Network Emulator, and Open Networking Operating System (ONOS) Software Defined Networking (SDN) controllers. The results of the testbed indicated a 25% reduction in latency, a 30% increase in throughput, and a 40% enhancement in real-time response times, demonstrating the system’s effectiveness in a controlled environment. These impressive results underscore the system’s potential to enhance network performance, efficiency, and responsiveness. By effectively addressing modern networks’ challenges, this solution proves its ability to confidently and seamlessly convert user intents into automated network actions without manual intervention, providing adaptability and scalability for today’s network environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19456-19468"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184409","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}

引用次数: 0

Hybrid subQUBO Annealing With a Correction Process for Multi-Day Intermodal Trip Planning

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534529

Tatsuya Noguchi;Keisuke Fukada;Siya Bao;Nozomu Togawa

{"title":"Hybrid subQUBO Annealing With a Correction Process for Multi-Day Intermodal Trip Planning","authors":"Tatsuya Noguchi;Keisuke Fukada;Siya Bao;Nozomu Togawa","doi":"10.1109/ACCESS.2025.3534529","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534529","url":null,"abstract":"The multi-day intermodal trip planning problem (MITPP) is an optimization problem that seeks to create the optimal route to visit Point-of-Interest (POI) and hotels over days. This problem involves coordinating intermodal transportation, such as walking, public transportation, to create a well-crafted travel itinerary. Quantum annealers have recently been explored as a powerful tool for solving combinatorial optimization problems by converting the problems into Quadratic Unconstrained Binary Optimization (QUBO). However, current quantum annealers have a small QUBO input size so that they cannot directly solve large-scale MITPPs. In this paper, we address this issue by extracting a subQUBO from the original large QUBO based on variable (spin) deviations and randomness. Then, we iteratively solve the subQUBOs by the quantum annealer and update the (quasi-)optimal solution. As the obtained (quasi-)optimal solution may violate constraints, we apply the correction processing till all constraints are satisfied. According to the experiment results using a real quantum annealer, our proposed method obtained high-quality solutions for large-scale MITPPs in the Tokyo area, and compared to the full QUBO method, we achieve a maximum spin reduction of 98.9%. Especially, compared to the method by a conventional computer and two conventional subQUBO methods, POI satisfaction is improved by 10.2%, and travel costs are improved by 23.2% respectively.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19716-19727"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854423","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105854","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}

引用次数: 0

Smart GNSS Integrity Monitoring for Road Vehicles: An Overview of AI Methods

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534659

Inês Viveiros;Hélder Silva;Yuri Andrade;Cristiano Pendão

引用次数: 0

Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534880

Muhammad Asif;Talha Ahmed Khan;Wang-Cheol Song

{"title":"Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN","authors":"Muhammad Asif;Talha Ahmed Khan;Wang-Cheol Song","doi":"10.1109/ACCESS.2025.3534880","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534880","url":null,"abstract":"Intent-Based Networking (IBN) simplifies network management by enabling users to express high-level intents in natural language, but existing approaches often fail to ensure alignment with network policies, leading to misconfigurations. Moreover, many methods lack robust validation mechanisms, reducing their reliability in dynamic environments. This research addresses these gaps by evaluating advanced Large Language Models (LLMs) such as BERT-base uncased (BERT-bu), GPT2, LLaMA3, Claude2 and small deep learning model BiLSTM with attention for translating intents and detecting contradictions. Using a curated dataset of 10,000 intent pairs, the proposed hybrid framework integrates a K-Nearest Neighbors (KNN) classifier to validate translations and recalibrate erroneous outputs. Experimental results demonstrate up to 5% higher accuracy (88%) and F1 scores compared to existing methods, ensuring precise intent translation and reliable network orchestration. This approach significantly enhances scalability and policy compliance in automated network environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"20316-20327"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855447","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106098","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}

引用次数: 0