Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548552
Naqash Afzal;Irfan Hussain;Zejian Zhou;Domenico Prattichizzo;Lakmal Seneviratne;Yuru Zhang;Dangxiao Wang
Presenting information privately such as alertness levels and time on the wrist via vibrotactile feedback proves invaluable for visually impaired individuals. Additionally, in situations where the visual channel is occupied, this serves as a discreet solution for sighted users, allowing them to stay informed during meetings or tasks without the need to overtly check their watches, thus minimizing potential distractions. However, it is a challenging task to present time accurately and efficiently to the users using vibrotactile modality due to the perceptual limits of human’s haptic channel. Inspired by the metaphors of mechanical and digital watches that have been widely used in our daily lives, we proposed two novel spatial-temporal vibrotactile encoding strategies. By varying the location, number, and duration of the vibrotactile stimuli, these strategies are capable of presenting the exact information about the current time through a series of encoded tactile cues. A physical prototype was developed and fifteen participants were recruited to evaluate the two solutions. Two experiments were performed to evaluate the two encoding strategies. The results showed that the mechanical and digital encoding strategies achieved an average correct rate of $90.55 pm 5.2%$ and $95.22 pm 4.1%$ during the focused state, and $95.28 pm 3.3%$ and $97.78 pm 3.8%$ during the distracted state, respectively $(mean pm SD)$ . Experimental results provide deep insights into utilizing the spatial-temporal patterns of vibrotactile stimuli for developing industrial-scale wearable haptic devices to present time and quantitative information efficiently and privately to the users.
{"title":"Sensory Substitution Device for Time Presentation: Spatial–Temporal Vibrotactile Encoding for Presenting Time on the Human Wrist","authors":"Naqash Afzal;Irfan Hussain;Zejian Zhou;Domenico Prattichizzo;Lakmal Seneviratne;Yuru Zhang;Dangxiao Wang","doi":"10.1109/ACCESS.2025.3548552","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548552","url":null,"abstract":"Presenting information privately such as alertness levels and time on the wrist via vibrotactile feedback proves invaluable for visually impaired individuals. Additionally, in situations where the visual channel is occupied, this serves as a discreet solution for sighted users, allowing them to stay informed during meetings or tasks without the need to overtly check their watches, thus minimizing potential distractions. However, it is a challenging task to present time accurately and efficiently to the users using vibrotactile modality due to the perceptual limits of human’s haptic channel. Inspired by the metaphors of mechanical and digital watches that have been widely used in our daily lives, we proposed two novel spatial-temporal vibrotactile encoding strategies. By varying the location, number, and duration of the vibrotactile stimuli, these strategies are capable of presenting the exact information about the current time through a series of encoded tactile cues. A physical prototype was developed and fifteen participants were recruited to evaluate the two solutions. Two experiments were performed to evaluate the two encoding strategies. The results showed that the mechanical and digital encoding strategies achieved an average correct rate of <inline-formula> <tex-math>$90.55 pm 5.2%$ </tex-math></inline-formula> and <inline-formula> <tex-math>$95.22 pm 4.1%$ </tex-math></inline-formula> during the focused state, and <inline-formula> <tex-math>$95.28 pm 3.3%$ </tex-math></inline-formula> and <inline-formula> <tex-math>$97.78 pm 3.8%$ </tex-math></inline-formula> during the distracted state, respectively <inline-formula> <tex-math>$(mean pm SD)$ </tex-math></inline-formula>. Experimental results provide deep insights into utilizing the spatial-temporal patterns of vibrotactile stimuli for developing industrial-scale wearable haptic devices to present time and quantitative information efficiently and privately to the users.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"44385-44402"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621734","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}
Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548519
Khorshed Alam;Mahbubul Haq Bhuiyan;Mohammad Shafiqul Islam;Abul Hossain Chowdhury;Zaheed Ahmed Bhuiyan;Suman Ahmmed
Our AI-driven PDF Chatbot is specialized for Project Management (PM) Automation and acts as a virtual Project Manager that offers continuous support to global teams. It interprets PDF data like SRS reports and interview transcripts by utilizing Open-Assistant’s SFT-1 12B Model. Insights from interviews of 15 project managers have enriched the knowledge base of our chatbot and ultimately enabled informative responses to the stakeholders of the project. Advanced AI techniques ensure efficient text preprocessing, including tokenization, numerical normalization, lowercasing, removing punctuation, removing extra spaces, recursive character text splitter, and lemmatization. It is primarily tailored for e-commerce project and provides precise guidance based on e-commerce data and risk management factors. With an average cosine similarity of 80.80% and semantic similarity score of 85.21%, it consistently aligns with PDF Contents and optimize the project management phases & methodologies. This innovation enhances Human-Robot Interaction, PM Automation, facilitates decision-making, and enables uninterrupted communication. While AI-driven PDF chatbots like ChatPDF and SciSummary exist, our chatbot is uniquely focused on automating project management tasks, providing tailored insights for e-commerce projects and decision-making, thus offering a breakthrough in PM automation. To ensure the chatbot’s robustness in context-aware responds, we compare our chatbot with ChatPDF and Sci-summary which are some PDF driven chatbots. Making our work available open-source on https://github.com/codewithkhurshed/SPM-project-repo can enhance its accessibility and promote future research opportunities in PDF driven chatbot development.
{"title":"Co-Pilot for Project Managers: Developing a PDF-Driven AI Chatbot for Facilitating Project Management","authors":"Khorshed Alam;Mahbubul Haq Bhuiyan;Mohammad Shafiqul Islam;Abul Hossain Chowdhury;Zaheed Ahmed Bhuiyan;Suman Ahmmed","doi":"10.1109/ACCESS.2025.3548519","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548519","url":null,"abstract":"Our AI-driven PDF Chatbot is specialized for Project Management (PM) Automation and acts as a virtual Project Manager that offers continuous support to global teams. It interprets PDF data like SRS reports and interview transcripts by utilizing Open-Assistant’s SFT-1 12B Model. Insights from interviews of 15 project managers have enriched the knowledge base of our chatbot and ultimately enabled informative responses to the stakeholders of the project. Advanced AI techniques ensure efficient text preprocessing, including tokenization, numerical normalization, lowercasing, removing punctuation, removing extra spaces, recursive character text splitter, and lemmatization. It is primarily tailored for e-commerce project and provides precise guidance based on e-commerce data and risk management factors. With an average cosine similarity of 80.80% and semantic similarity score of 85.21%, it consistently aligns with PDF Contents and optimize the project management phases & methodologies. This innovation enhances Human-Robot Interaction, PM Automation, facilitates decision-making, and enables uninterrupted communication. While AI-driven PDF chatbots like ChatPDF and SciSummary exist, our chatbot is uniquely focused on automating project management tasks, providing tailored insights for e-commerce projects and decision-making, thus offering a breakthrough in PM automation. To ensure the chatbot’s robustness in context-aware responds, we compare our chatbot with ChatPDF and Sci-summary which are some PDF driven chatbots. Making our work available open-source on <uri>https://github.com/codewithkhurshed/SPM-project-repo</uri> can enhance its accessibility and promote future research opportunities in PDF driven chatbot development.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"43079-43096"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912498","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632257","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}
Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548151
Tao Dai;Qi Wang;Yuancheng Shen;Shang Gao
Neural networks have been widely employed in the field of object detection. Transformers enable effective object detection through global context awareness, modular design, scalability, and adaptability to diverse target scales. However, small object detection requires careful consideration due to its comprehensive computations, data requirements, and real-time performance challenges. To address these issues, we present SwinVision, an innovative framework for small object detection in low-light environments. This research shows a balanced approach between computational efficiency and detection accuracy for advancing object detection in low-light scenarios. Firstly, a Swin Transformer-based computing network is introduced and optimized for object detection in large-scale areas. The framework balances computational power and resource efficiency, surpassing conventional transformers. Secondly, we present the STLE module, which enhances the features of low-light images for beneficial object detection. The last building block is a specialized Swin-based detection block for accurate detection of small, detailed objects in resource-constrained scenarios. Experiments conducted on the VisDrone dataset significantly ameliorated existing methods such as YOLOv8x, with a 6.31% increase in mAP and 12.55% in AP50. SwinVision’s effectiveness in low-light environments, especially with small objects, establishes a foundation for robust detection systems adapting to various environmental challenges.
{"title":"SwinVision: Detecting Small Objects in Low-Light Environments","authors":"Tao Dai;Qi Wang;Yuancheng Shen;Shang Gao","doi":"10.1109/ACCESS.2025.3548151","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548151","url":null,"abstract":"Neural networks have been widely employed in the field of object detection. Transformers enable effective object detection through global context awareness, modular design, scalability, and adaptability to diverse target scales. However, small object detection requires careful consideration due to its comprehensive computations, data requirements, and real-time performance challenges. To address these issues, we present SwinVision, an innovative framework for small object detection in low-light environments. This research shows a balanced approach between computational efficiency and detection accuracy for advancing object detection in low-light scenarios. Firstly, a Swin Transformer-based computing network is introduced and optimized for object detection in large-scale areas. The framework balances computational power and resource efficiency, surpassing conventional transformers. Secondly, we present the STLE module, which enhances the features of low-light images for beneficial object detection. The last building block is a specialized Swin-based detection block for accurate detection of small, detailed objects in resource-constrained scenarios. Experiments conducted on the VisDrone dataset significantly ameliorated existing methods such as YOLOv8x, with a 6.31% increase in mAP and 12.55% in AP50. SwinVision’s effectiveness in low-light environments, especially with small objects, establishes a foundation for robust detection systems adapting to various environmental challenges.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42797-42812"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601968","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}
Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548133
Mostafa Farouk Senussi;Mahmoud Abdalla;Mahmoud Salaheldin Kasem;Mohamed Mahmoud;Bilel Yagoub;Hyun-Soo Kang
Overcoming occlusions in light field (LF) imaging is a challenging yet complex task crucial for scene understanding, image quality enhancement, and restoring visual details in obstructed scenes. This review examines contemporary occlusion removal methods, spanning from classical techniques to advanced deep learning approaches that leverage LF data’s spatial and angular dimensions. We categorize these methods into two domains: (1) single-view inpainting methods often adapted for LF contexts, and (2) specialized LF occlusion removal techniques that exploit multi-view data. The review explores how these methods mitigate occlusion artifacts and also investigates LF acquisition technologies, representations, and the role of loss functions in optimizing model performance. A discussion of publicly available datasets and performance evaluation metrics addresses the challenges of handling large occlusions. The review concludes with future research directions, emphasizing hybrid approaches, refined loss functions, and scalable solutions for LF occlusion removal.
{"title":"A Comprehensive Review on Light Field Occlusion Removal: Trends, Challenges, and Future Directions","authors":"Mostafa Farouk Senussi;Mahmoud Abdalla;Mahmoud Salaheldin Kasem;Mohamed Mahmoud;Bilel Yagoub;Hyun-Soo Kang","doi":"10.1109/ACCESS.2025.3548133","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548133","url":null,"abstract":"Overcoming occlusions in light field (LF) imaging is a challenging yet complex task crucial for scene understanding, image quality enhancement, and restoring visual details in obstructed scenes. This review examines contemporary occlusion removal methods, spanning from classical techniques to advanced deep learning approaches that leverage LF data’s spatial and angular dimensions. We categorize these methods into two domains: (1) single-view inpainting methods often adapted for LF contexts, and (2) specialized LF occlusion removal techniques that exploit multi-view data. The review explores how these methods mitigate occlusion artifacts and also investigates LF acquisition technologies, representations, and the role of loss functions in optimizing model performance. A discussion of publicly available datasets and performance evaluation metrics addresses the challenges of handling large occlusions. The review concludes with future research directions, emphasizing hybrid approaches, refined loss functions, and scalable solutions for LF occlusion removal.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42472-42493"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601970","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}
Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548539
Moisés J. B. B. Davi;Mario Oleskovicz;Felipe V. Lopes
This paper presents a performance review of existing fault classifiers when applied to Inverter-Based Resource (IBR) interconnection lines and proposes a new high-sensitivity time-domain fault classification methodology. The proposed method is based on self-adjusting thresholds, and it is investigated regarding its performance when applied to IBR or conventional generator terminals, being validated for different grid short-circuit levels, various IBR topologies/controls, and considering several fault types, inception angles, resistances, and locations. A typical IBR interconnection system topology is modeled using the PSCAD software for such studies. Comparisons with the main state-of-the-art phase-selection and fault classification methods highlight the superiority of the proposed one that, besides overcoming the challenges presented by IBRs for this task, provides shorter operating times by not relying on phasor estimation techniques.
{"title":"A Novel High-Sensitivity Time-Domain Fault Classifier Applied to Inverter-Based Resource Interconnection Lines","authors":"Moisés J. B. B. Davi;Mario Oleskovicz;Felipe V. Lopes","doi":"10.1109/ACCESS.2025.3548539","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548539","url":null,"abstract":"This paper presents a performance review of existing fault classifiers when applied to Inverter-Based Resource (IBR) interconnection lines and proposes a new high-sensitivity time-domain fault classification methodology. The proposed method is based on self-adjusting thresholds, and it is investigated regarding its performance when applied to IBR or conventional generator terminals, being validated for different grid short-circuit levels, various IBR topologies/controls, and considering several fault types, inception angles, resistances, and locations. A typical IBR interconnection system topology is modeled using the PSCAD software for such studies. Comparisons with the main state-of-the-art phase-selection and fault classification methods highlight the superiority of the proposed one that, besides overcoming the challenges presented by IBRs for this task, provides shorter operating times by not relying on phasor estimation techniques.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"41590-41606"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601983","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}
Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548146
Xiaofang Li;Pei Hu;Jiulong Zhu
Supply chain design (SCD) is a complex optimization challenge that involves coordinating various elements of a supply chain to ensure efficient production, distribution, and fulfillment of customer demands. This paper proposes an improved equilibrium optimizer (IEO) algorithm to develop a supply chain network. The first novelty lies in considering the uncertainty of customer demands, the upper and lower product limits of manufacturers, and product discounts to minimize the total economic cost. The second novelty concerns the improvements to the EO algorithm in the equilibrium pool, control parameters, and position correction. Position correction ensures that solutions meet the various constraints of SCD, and improves the feasibility of the algorithm. For small-, medium-, and large-scale test cases, the proposed algorithm has been observed to outperform the original EO algorithm and four well-known algorithms, the imperialist competitive algorithm (ICA), a hybrid algorithm of grey wolf optimizer and particle swarm optimization (GWOPSO), the whale optimization algorithm (WOA), and the teaching-learning based optimization algorithm (TLBO), in terms of optimal solutions and operational efficiency. IEO demonstrates outstanding performance in solving SCD problems.
{"title":"Equilibrium Optimizer for Supply Chain Design Under Demand Uncertainty","authors":"Xiaofang Li;Pei Hu;Jiulong Zhu","doi":"10.1109/ACCESS.2025.3548146","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548146","url":null,"abstract":"Supply chain design (SCD) is a complex optimization challenge that involves coordinating various elements of a supply chain to ensure efficient production, distribution, and fulfillment of customer demands. This paper proposes an improved equilibrium optimizer (IEO) algorithm to develop a supply chain network. The first novelty lies in considering the uncertainty of customer demands, the upper and lower product limits of manufacturers, and product discounts to minimize the total economic cost. The second novelty concerns the improvements to the EO algorithm in the equilibrium pool, control parameters, and position correction. Position correction ensures that solutions meet the various constraints of SCD, and improves the feasibility of the algorithm. For small-, medium-, and large-scale test cases, the proposed algorithm has been observed to outperform the original EO algorithm and four well-known algorithms, the imperialist competitive algorithm (ICA), a hybrid algorithm of grey wolf optimizer and particle swarm optimization (GWOPSO), the whale optimization algorithm (WOA), and the teaching-learning based optimization algorithm (TLBO), in terms of optimal solutions and operational efficiency. IEO demonstrates outstanding performance in solving SCD problems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42285-42295"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594344","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}
Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548096
Jianbin Chen;Chengyu Yang;Jianjun Zou;Kai Chen
Power factor correction (PFC) plays a crucial role in power electronics, particularly in enhancing the efficiency and stability of power systems. As continuous conduction mode (CCM) boost PFC converters become increasingly prevalent, research into their control strategies has deepened. While average current mode (ACM) control is commonly used, it faces several challenges in practical applications. Traditional ACM control methods struggle with input impedance regulation, especially within certain frequency ranges, where maintaining constant input impedance is difficult. This leads to issues such as current distortion and reduced efficiency. Additionally, bandwidth adjustment and ripple disturbance suppression with standard control procedures remain significant challenges. To address these issues, this paper proposes a novel multiplier-operated controller specifically designed for CCM boost PFC converters. The controller employs an innovative methodology to effectively modify and maintain input impedance at a defined constant within a specific frequency range. Furthermore, a specialized compensator is integrated to optimize bandwidth and minimize ripple disturbances. Experimental results from a 600W prototype demonstrate that the proposed controller achieves a power factor of up to 0.98 and significantly enhances efficiency and other performance metrics. This study provides new insights and technical approaches for improving the control strategies of CCM boost PFC converters.
{"title":"Multiplier Operated Controller for CCM Boost PFC Converter With Regulated Input Impedance and Improved Power Factor","authors":"Jianbin Chen;Chengyu Yang;Jianjun Zou;Kai Chen","doi":"10.1109/ACCESS.2025.3548096","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548096","url":null,"abstract":"Power factor correction (PFC) plays a crucial role in power electronics, particularly in enhancing the efficiency and stability of power systems. As continuous conduction mode (CCM) boost PFC converters become increasingly prevalent, research into their control strategies has deepened. While average current mode (ACM) control is commonly used, it faces several challenges in practical applications. Traditional ACM control methods struggle with input impedance regulation, especially within certain frequency ranges, where maintaining constant input impedance is difficult. This leads to issues such as current distortion and reduced efficiency. Additionally, bandwidth adjustment and ripple disturbance suppression with standard control procedures remain significant challenges. To address these issues, this paper proposes a novel multiplier-operated controller specifically designed for CCM boost PFC converters. The controller employs an innovative methodology to effectively modify and maintain input impedance at a defined constant within a specific frequency range. Furthermore, a specialized compensator is integrated to optimize bandwidth and minimize ripple disturbances. Experimental results from a 600W prototype demonstrate that the proposed controller achieves a power factor of up to 0.98 and significantly enhances efficiency and other performance metrics. This study provides new insights and technical approaches for improving the control strategies of CCM boost PFC converters.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"44750-44759"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621584","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}
Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548033
Mona Esmaeili;Sameer D. Hemmady;Oameed Noakoasteen;Edl Schamiloglu;Christos Christodoulou;Payman Zarkesh-Ha
This study advances Electromagnetic Compatibility (EMC) by investigating how electromagnetic interference (EMI) from Radio Frequency (RF) sources affects digital interconnects. Unlike traditional analyses centered on Continuous Wave (CW) signals, we adopt an RF-focused approach using S-parameter data and consistent RF power to emphasize steady-state responses. This method eliminates the need for time-domain conversions, allowing for more accurate analysis. Our research introduces a novel image-based classification system that accurately assesses signal safety based on steady-state responses. By leveraging a Generative Adversarial Network (GAN) trained on ‘safe’ and ‘unsafe’ signal images, our system can effectively recognize and distinguish between these two states. The GAN’s ability to generate realistic signal patterns enhances classification accuracy, especially when empirical data is limited. This approach has been validated through multiple transformations to ensure robustness and reliability. The findings offer significant improvements in EMC analysis and provide practical guidelines for designing robust digital interconnects. These advancements contribute to enhancing the reliability and security of electronic devices in environments with high RF interference, making them better suited for real-world commercial applications where signal integrity is critical.
{"title":"Advancing EMC Analysis With GAN-Driven Signal Classification and Waveform Modulation","authors":"Mona Esmaeili;Sameer D. Hemmady;Oameed Noakoasteen;Edl Schamiloglu;Christos Christodoulou;Payman Zarkesh-Ha","doi":"10.1109/ACCESS.2025.3548033","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548033","url":null,"abstract":"This study advances Electromagnetic Compatibility (EMC) by investigating how electromagnetic interference (EMI) from Radio Frequency (RF) sources affects digital interconnects. Unlike traditional analyses centered on Continuous Wave (CW) signals, we adopt an RF-focused approach using S-parameter data and consistent RF power to emphasize steady-state responses. This method eliminates the need for time-domain conversions, allowing for more accurate analysis. Our research introduces a novel image-based classification system that accurately assesses signal safety based on steady-state responses. By leveraging a Generative Adversarial Network (GAN) trained on ‘safe’ and ‘unsafe’ signal images, our system can effectively recognize and distinguish between these two states. The GAN’s ability to generate realistic signal patterns enhances classification accuracy, especially when empirical data is limited. This approach has been validated through multiple transformations to ensure robustness and reliability. The findings offer significant improvements in EMC analysis and provide practical guidelines for designing robust digital interconnects. These advancements contribute to enhancing the reliability and security of electronic devices in environments with high RF interference, making them better suited for real-world commercial applications where signal integrity is critical.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"44789-44799"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637872","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}
Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548125
Qi Zhuang;Zhengjie Chu;Jun Li
The comments on ice and snow tourism are characterized by high levels of noise, unstructured content, and complex information. However, existing sentiment analysis methods exhibit significant limitations in terms of accuracy and the depth of feature extraction. To address these challenges, this study proposes an intelligent sentiment analysis algorithm based on a multi-model fusion approach: the Improved Dynamic Convolutional and Attention-based Bidirectional Long Short-Term Memory Model (IDCAN-BiLSTM). The aim is to enhance the effectiveness of sentiment analysis for ice and snow tourism reviews. Firstly, the review data is cleaned, denoised, and segmented. High-quality text vector embeddings are then generated using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to capture the deep semantic features of the review text. Subsequently, the IDCAN-BiLSTM model employs a Dynamic Convolutional Neural Network (DCNN) to extract the local features of reviews, thereby increasing sensitivity to specific sentiment words. Following this, a Multi-Head Attention (MHA) mechanism is utilized to focus on key sentiment information within the reviews, effectively addressing the challenges posed by complex and lengthy texts. Finally, the Bidirectional Long Short-Term Memory (BiLSTM) module comprehensively captures the global contextual information in the reviews, improving both the sentiment classification accuracy and the contextual recognition capabilities of the model. Experimental results demonstrate that the IDCAN-BiLSTM model achieves outstanding performance in the sentiment classification of ice and snow tourism reviews, with an accuracy of 92.17% and an F1 score of 0.93. These results significantly surpass those of traditional sentiment analysis methods. In particular, the model shows superior performance in the sentiment classification of long review texts, effectively enhancing the accuracy and granularity of sentiment recognition through dynamic convolution and the self-attention mechanism. Moreover, the model distinguishes sentiment tendencies across different user groups regarding their experiences in ice and snow tourism. This capability provides valuable data support for optimizing services and enabling precision marketing strategies in the ice and snow tourism sector.
{"title":"Text Analysis of Digital Commentary on Ice and Snow Tourism Based on Artificial Intelligence and Long Short-Term Memory Neural Network","authors":"Qi Zhuang;Zhengjie Chu;Jun Li","doi":"10.1109/ACCESS.2025.3548125","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548125","url":null,"abstract":"The comments on ice and snow tourism are characterized by high levels of noise, unstructured content, and complex information. However, existing sentiment analysis methods exhibit significant limitations in terms of accuracy and the depth of feature extraction. To address these challenges, this study proposes an intelligent sentiment analysis algorithm based on a multi-model fusion approach: the Improved Dynamic Convolutional and Attention-based Bidirectional Long Short-Term Memory Model (IDCAN-BiLSTM). The aim is to enhance the effectiveness of sentiment analysis for ice and snow tourism reviews. Firstly, the review data is cleaned, denoised, and segmented. High-quality text vector embeddings are then generated using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to capture the deep semantic features of the review text. Subsequently, the IDCAN-BiLSTM model employs a Dynamic Convolutional Neural Network (DCNN) to extract the local features of reviews, thereby increasing sensitivity to specific sentiment words. Following this, a Multi-Head Attention (MHA) mechanism is utilized to focus on key sentiment information within the reviews, effectively addressing the challenges posed by complex and lengthy texts. Finally, the Bidirectional Long Short-Term Memory (BiLSTM) module comprehensively captures the global contextual information in the reviews, improving both the sentiment classification accuracy and the contextual recognition capabilities of the model. Experimental results demonstrate that the IDCAN-BiLSTM model achieves outstanding performance in the sentiment classification of ice and snow tourism reviews, with an accuracy of 92.17% and an F1 score of 0.93. These results significantly surpass those of traditional sentiment analysis methods. In particular, the model shows superior performance in the sentiment classification of long review texts, effectively enhancing the accuracy and granularity of sentiment recognition through dynamic convolution and the self-attention mechanism. Moreover, the model distinguishes sentiment tendencies across different user groups regarding their experiences in ice and snow tourism. This capability provides valuable data support for optimizing services and enabling precision marketing strategies in the ice and snow tourism sector.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"41259-41269"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621728","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}
Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548031
Eyob Solomon Getachew;Beakal Gizachew Assefa
The Collatz conjecture, which posits that any positive integer will eventually reach 1 through a specific iterative process, is a classic unsolved problem in mathematics. This research focuses on designing an efficient algorithm to compute the stopping time of numbers in the Collatz sequence, achieving significant computational improvements. By leveraging structural patterns in the Collatz tree, the proposed algorithm minimizes redundant operations and optimizes computational steps. Unlike prior methods, it efficiently handles extremely large numbers without requiring advanced techniques such as memoization or parallelization. Experimental evaluations confirm computational efficiency improvements of approximately 28% over state-of-the-art methods. These findings underscore the algorithm’s scalability and robustness, providing a foundation for future large-scale verification of the conjecture and potential applications in computational mathematics.
{"title":"Efficient Computation of Collatz Sequence Stopping Times: A Novel Algorithmic Approach","authors":"Eyob Solomon Getachew;Beakal Gizachew Assefa","doi":"10.1109/ACCESS.2025.3548031","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548031","url":null,"abstract":"The Collatz conjecture, which posits that any positive integer will eventually reach 1 through a specific iterative process, is a classic unsolved problem in mathematics. This research focuses on designing an efficient algorithm to compute the stopping time of numbers in the Collatz sequence, achieving significant computational improvements. By leveraging structural patterns in the Collatz tree, the proposed algorithm minimizes redundant operations and optimizes computational steps. Unlike prior methods, it efficiently handles extremely large numbers without requiring advanced techniques such as memoization or parallelization. Experimental evaluations confirm computational efficiency improvements of approximately 28% over state-of-the-art methods. These findings underscore the algorithm’s scalability and robustness, providing a foundation for future large-scale verification of the conjecture and potential applications in computational mathematics.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"41210-41220"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621690","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}
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