Pub Date : 2025-03-05DOI: 10.1109/ACCESS.2025.3548128
Abdalrahman Alblwi;Saleh Makkawy;Kenneth E. Barner
The Denoising Diffusion Probabilistic Model (DDPM) has gained significant attention for its powerful image generation and segmentation capabilities, particularly in biomedical applications where accuracy is critical. In breast cancer detection, ultrasound imaging is widely used due to its safety, affordability, and non-ionizing nature. However, the inherent challenges of ultrasound data, such as noise and artifacts, make accurate tumor segmentation difficult, often leading to misdiagnosis. We propose a novel Deep Denoising Probabilistic Diffusion Model (D-DDPM) designed to enhance tumor segmentation in breast ultrasound images to address these limitations. Our model incorporates a nested U-Net architecture with Residual U-blocks (RSU), significantly improving feature learning and segmentation precision. In addition to performing segmentation, D-DDPM generates synthetic data, augmenting existing real datasets to improve data size with a diverse range of high-quality samples. We validated D-DDPM on several breast ultrasound datasets, comparing its performance to state-of-the-art methods. The proposed D-DDPM achieves a Dice score improvement of 2.26%, 4.24%, and 5% over the runner-up model, demonstrating superior performance on all BUS datasets. Both qualitative and quantitative results demonstrate the ability of D-DDPM to deliver more accurate and reliable segmentation results, offering promising potential to improve clinical decision-making in cancer diagnosis.
{"title":"D-DDPM: Deep Denoising Diffusion Probabilistic Models for Lesion Segmentation and Data Generation in Ultrasound Imaging","authors":"Abdalrahman Alblwi;Saleh Makkawy;Kenneth E. Barner","doi":"10.1109/ACCESS.2025.3548128","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548128","url":null,"abstract":"The Denoising Diffusion Probabilistic Model (DDPM) has gained significant attention for its powerful image generation and segmentation capabilities, particularly in biomedical applications where accuracy is critical. In breast cancer detection, ultrasound imaging is widely used due to its safety, affordability, and non-ionizing nature. However, the inherent challenges of ultrasound data, such as noise and artifacts, make accurate tumor segmentation difficult, often leading to misdiagnosis. We propose a novel Deep Denoising Probabilistic Diffusion Model (D-DDPM) designed to enhance tumor segmentation in breast ultrasound images to address these limitations. Our model incorporates a nested U-Net architecture with Residual U-blocks (RSU), significantly improving feature learning and segmentation precision. In addition to performing segmentation, D-DDPM generates synthetic data, augmenting existing real datasets to improve data size with a diverse range of high-quality samples. We validated D-DDPM on several breast ultrasound datasets, comparing its performance to state-of-the-art methods. The proposed D-DDPM achieves a Dice score improvement of 2.26%, 4.24%, and 5% over the runner-up model, demonstrating superior performance on all BUS datasets. Both qualitative and quantitative results demonstrate the ability of D-DDPM to deliver more accurate and reliable segmentation results, offering promising potential to improve clinical decision-making in cancer diagnosis.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"41194-41209"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621585","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.2025.3548424
Praveen B. Choppala;Ramoni Adeogun
The auxiliary particle filter, which is the popular extension of the standard bootstrap particle filter, is known to assist in drawing particles from regions of high probability mass of the posterior density by leveraging the incoming measurement information in the sampling process. The filter accomplishes this by looking ahead in time to determine those particles that become important when propagated forward, retract, and then propagate those particles forward in time. The key problem with this approach is that a particle determined to be important may not fall in regions of importance when actually propagated forward, either because of a large diffusion of the state transition kernel and/or a highly informative measurement, thus defeating the entire purpose of the filter. This problem leads to degeneracy. This paper proposes a method of sampling a multitude of particles for each particle to make such a decision. The key idea here is to use multiple disturbances, instead of one as does the auxiliary particle filter, as lookahead means to guide particles to regions of high probability in the posterior probability density. Through evaluation, we show that the proposed idea overcomes the said problem and exhibits less degeneracy and high tracking accuracy.
{"title":"Auxiliary Particle Filtering With Multitudinous Lookahead Sampling for Accurate Target Tracking","authors":"Praveen B. Choppala;Ramoni Adeogun","doi":"10.1109/ACCESS.2025.3548424","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548424","url":null,"abstract":"The auxiliary particle filter, which is the popular extension of the standard bootstrap particle filter, is known to assist in drawing particles from regions of high probability mass of the posterior density by leveraging the incoming measurement information in the sampling process. The filter accomplishes this by looking ahead in time to determine those particles that become important when propagated forward, retract, and then propagate those particles forward in time. The key problem with this approach is that a particle determined to be important may not fall in regions of importance when actually propagated forward, either because of a large diffusion of the state transition kernel and/or a highly informative measurement, thus defeating the entire purpose of the filter. This problem leads to degeneracy. This paper proposes a method of sampling a multitude of particles for each particle to make such a decision. The key idea here is to use multiple disturbances, instead of one as does the auxiliary particle filter, as lookahead means to guide particles to regions of high probability in the posterior probability density. Through evaluation, we show that the proposed idea overcomes the said problem and exhibits less degeneracy and high tracking accuracy.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42874-42886"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594327","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}
Prediction of myocardial infarction (MI) is crucial for early intervention and treatment. Machine learning has increasingly been applied in the realm of disease prediction. This study explores the feasibility of utilizing easily obtainable heart rate (HR) and respiratory rate (RR) data collected during nocturnal sleep, in conjunction with clinical characteristics and medical domain knowledge, to predict MI. Data for this investigation were sourced from the Sleep Heart Health Study (SHHS) program in the United States, which was categorized into MI and non-MI groups based on the occurrence or absence of MI during follow-up, involving a total of 488 participants. Multiple features related to HR and RR were extracted and integrated with clinical features; four algorithms—MLP, SVM, XGBoost, and CNN—were employed for model construction. The findings indicated that the MLP model exhibited superior performance, achieving an accuracy rate 71.1%. Furthermore, three medical rules age, HR, and RR were incorporated into the MLP model to mitigate the limitations of small sample sizes. The experiments demonstrate that the model’s accuracy reaches its optimal level by combining the age rule, improving to 73.1%. The findings indicate that leveraging non-cardiac electrophysiological data obtained during sleep alongside medical domain knowledge can significantly enhance the accuracy of early predictions regarding cardiac MI while offering novel insights for its prevention and diagnosis.
{"title":"Prediction of Myocardial Infarction Based on Non-ECG Sleep Data Combined With Domain Knowledge","authors":"Changyun Li;Yonghan Zhao;Qihui Mo;Zhibing Wang;Xi Xu","doi":"10.1109/ACCESS.2025.3548118","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548118","url":null,"abstract":"Prediction of myocardial infarction (MI) is crucial for early intervention and treatment. Machine learning has increasingly been applied in the realm of disease prediction. This study explores the feasibility of utilizing easily obtainable heart rate (HR) and respiratory rate (RR) data collected during nocturnal sleep, in conjunction with clinical characteristics and medical domain knowledge, to predict MI. Data for this investigation were sourced from the Sleep Heart Health Study (SHHS) program in the United States, which was categorized into MI and non-MI groups based on the occurrence or absence of MI during follow-up, involving a total of 488 participants. Multiple features related to HR and RR were extracted and integrated with clinical features; four algorithms—MLP, SVM, XGBoost, and CNN—were employed for model construction. The findings indicated that the MLP model exhibited superior performance, achieving an accuracy rate 71.1%. Furthermore, three medical rules age, HR, and RR were incorporated into the MLP model to mitigate the limitations of small sample sizes. The experiments demonstrate that the model’s accuracy reaches its optimal level by combining the age rule, improving to 73.1%. The findings indicate that leveraging non-cardiac electrophysiological data obtained during sleep alongside medical domain knowledge can significantly enhance the accuracy of early predictions regarding cardiac MI while offering novel insights for its prevention and diagnosis.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42262-42271"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621603","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}
Stunting, a critical health issue affecting child growth and development, is prevalent in developing countries and is characterized by significantly reduced height for age. Traditional height measurement methods often require physical contact, which can lead to measurement inaccuracies and potential discomfort for infants. This study introduces a contactless method for measuring infant height using advanced computer vision techniques and the MediaPipe Pose library. By detecting key body points and applying Euclidean distance calculations, the proposed approach offers precise height estimation. Validation uses baby dolls (38 cm and 49 cm) and real infants (n =12) under varying body postures and lighting conditions. A fixed-size green mat (100 cm) was used as a reference for converting pixel distances into actual measurements. The method achieved an average accuracy of 99.76% for the 38 cm doll and 99.67% for the 49 cm doll. For real infants, the system demonstrated an average accuracy of 98.48%. This confirms that the system performs effectively in measuring infant height, even under conditions of non-ideal body posture. Furthermore, these results suggest that the proposed system is an effective and practical alternative for infant height measurements, supporting the early detection of stunting in diverse real-world settings.
{"title":"Contactless Infant Height Measurement for Enhanced Early Detection of Stunting Using Computer Vision Techniques","authors":"Risfendra;Aripriharta;Suherman;Gheri Febri Ananda;Dwi Sudarno Putra;Fahmi","doi":"10.1109/ACCESS.2025.3548159","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548159","url":null,"abstract":"Stunting, a critical health issue affecting child growth and development, is prevalent in developing countries and is characterized by significantly reduced height for age. Traditional height measurement methods often require physical contact, which can lead to measurement inaccuracies and potential discomfort for infants. This study introduces a contactless method for measuring infant height using advanced computer vision techniques and the MediaPipe Pose library. By detecting key body points and applying Euclidean distance calculations, the proposed approach offers precise height estimation. Validation uses baby dolls (38 cm and 49 cm) and real infants (n =12) under varying body postures and lighting conditions. A fixed-size green mat (100 cm) was used as a reference for converting pixel distances into actual measurements. The method achieved an average accuracy of 99.76% for the 38 cm doll and 99.67% for the 49 cm doll. For real infants, the system demonstrated an average accuracy of 98.48%. This confirms that the system performs effectively in measuring infant height, even under conditions of non-ideal body posture. Furthermore, these results suggest that the proposed system is an effective and practical alternative for infant height measurements, supporting the early detection of stunting in diverse real-world settings.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42364-42376"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594278","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.2025.3548591
Ramteja Sajja;Vinay Pursnani;Yusuf Sermet;Ibrahim Demir
Floodplain management is critical for mitigating flood risks and safeguarding communities. The FloodPlain Manager (FPM) certification is essential for professionals in this field, but current preparation methods often fall short in providing comprehensive, accessible, and engaging study resources. This research introduces a novel AI-assisted educational tool designed specifically for FPM certification preparation and training process. Leveraging advanced natural language processing and machine learning techniques, this tool offers personalized learning experiences, interactive question-and-answer sessions, and real-time feedback to aspiring floodplain managers. The system architecture integrates certification-specific content through a sophisticated document parsing process, ensuring relevance and accuracy. Evaluation of the tool, conducted through text similarity analysis, demonstrates its effectiveness in preparing candidates for the FPM certification exam. With 91.7% accuracy for open-ended questions and 95.12% for multiple-choice questions, the tool offers a personalized learning experience through dynamic flashcards and adaptive quizzes, highlighting its potential to enhance vocational training and exam readiness. This study underscores the transformative role of AI in professional education and suggests future directions for expanding the tool’s capabilities and application to other certifications.
{"title":"AI-Assisted Educational Framework for Floodplain Manager Certification: Enhancing Vocational Education and Training Through Personalized Learning","authors":"Ramteja Sajja;Vinay Pursnani;Yusuf Sermet;Ibrahim Demir","doi":"10.1109/ACCESS.2025.3548591","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548591","url":null,"abstract":"Floodplain management is critical for mitigating flood risks and safeguarding communities. The FloodPlain Manager (FPM) certification is essential for professionals in this field, but current preparation methods often fall short in providing comprehensive, accessible, and engaging study resources. This research introduces a novel AI-assisted educational tool designed specifically for FPM certification preparation and training process. Leveraging advanced natural language processing and machine learning techniques, this tool offers personalized learning experiences, interactive question-and-answer sessions, and real-time feedback to aspiring floodplain managers. The system architecture integrates certification-specific content through a sophisticated document parsing process, ensuring relevance and accuracy. Evaluation of the tool, conducted through text similarity analysis, demonstrates its effectiveness in preparing candidates for the FPM certification exam. With 91.7% accuracy for open-ended questions and 95.12% for multiple-choice questions, the tool offers a personalized learning experience through dynamic flashcards and adaptive quizzes, highlighting its potential to enhance vocational training and exam readiness. This study underscores the transformative role of AI in professional education and suggests future directions for expanding the tool’s capabilities and application to other certifications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42401-42413"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594411","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}
This note considers the active secure event-triggered control(ETC) problem of hybrid power system under DoS attack. A combination of load frequency control (LFC) and virtual inertia control (VIC) is adopted to deal with the influence of uncertainty and lower inertia induced by renewable energy like wind and solar power. To active defend DoS attack interrupting communication of measurement and control, an interval secure event-triggered mechanism (ISETM) is proposed under software defined network (SDN). Both a triggering transmission and a secure triggering interval are generated where the triggering packet is transmitted over SDN data plane and the secure triggering interval is sent to SDN control plane regulating SDN cybersecurity mechanism. Under ISETM, multi-area hybrid power system is modeled by a delay system with two triggering conditions. Furthermore, interval secure event-triggered LFC-VIC of hybrid power system is formulated by a $H_{infty } $ control problem. A sufficient criterion of hybrid power system with the prescribed $H_{infty } $ performance level is derived by using Lyapunov-Krasovskii functional method. A co-designed method of ISETM and LFC-VIC gains is given by linear matrix inequalities (LMIs). Finally, a two-area hybrid power system is simulated to verify the validness of the proposed interval secure event-triggered control (ISETC) method.
{"title":"Interval Secure Event-Triggered Control of Hybrid Power System Under DoS Attack","authors":"Dashuang Chong;Tongshu Si;Zihao Cheng;Feng Yang;Jigang Liu;Zongwang Lv","doi":"10.1109/ACCESS.2025.3548455","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548455","url":null,"abstract":"This note considers the active secure event-triggered control(ETC) problem of hybrid power system under DoS attack. A combination of load frequency control (LFC) and virtual inertia control (VIC) is adopted to deal with the influence of uncertainty and lower inertia induced by renewable energy like wind and solar power. To active defend DoS attack interrupting communication of measurement and control, an interval secure event-triggered mechanism (ISETM) is proposed under software defined network (SDN). Both a triggering transmission and a secure triggering interval are generated where the triggering packet is transmitted over SDN data plane and the secure triggering interval is sent to SDN control plane regulating SDN cybersecurity mechanism. Under ISETM, multi-area hybrid power system is modeled by a delay system with two triggering conditions. Furthermore, interval secure event-triggered LFC-VIC of hybrid power system is formulated by a <inline-formula> <tex-math>$H_{infty } $ </tex-math></inline-formula> control problem. A sufficient criterion of hybrid power system with the prescribed <inline-formula> <tex-math>$H_{infty } $ </tex-math></inline-formula> performance level is derived by using Lyapunov-Krasovskii functional method. A co-designed method of ISETM and LFC-VIC gains is given by linear matrix inequalities (LMIs). Finally, a two-area hybrid power system is simulated to verify the validness of the proposed interval secure event-triggered control (ISETC) method.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42574-42586"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912443","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601966","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.2025.3548135
Asaad Ahmed;Guangmin Sun;Anas Bilal;Yu Li;Shouki A. Ebad
Skin cancer poses a significant global health challenge due to its increasing incidence rates. Accurate segmentation of skin lesions is essential for early detection and successful treatment, yet many current techniques struggle to balance computational efficiency with the ability to capture complex lesion features. This paper aims to develop an advanced deep learning model that improves segmentation accuracy while maintaining computational efficiency, offering a solution to the limitations of existing methods. We propose a novel dual-encoder deep learning architecture incorporating Squeeze-and-Excitation (SE) attention blocks. The model integrates two encoders: a pre-trained ResNet-50 for extracting local features efficiently and a Vision Transformer (ViT) to capture high-level features and long-range dependencies. The fusion of these features, enhanced by SE attention blocks, is processed through a CNN decoder, ensuring the model captures both local and global contextual information. The proposed model was evaluated on three benchmark datasets, ISIC 2016, ISIC 2017, and ISIC 2018, achieving Intersection over Union (IoU) scores of 89.53%, 87.02%, and 84.56%, respectively. These results highlight the model’s ability to outperform current methods in balancing segmentation accuracy and computational efficiency. The findings demonstrate that the proposed model enhances medical image analysis in dermatology, providing a promising tool for improving the early detection of skin cancer.
{"title":"A Hybrid Deep Learning Approach for Skin Lesion Segmentation With Dual Encoders and Channel-Wise Attention","authors":"Asaad Ahmed;Guangmin Sun;Anas Bilal;Yu Li;Shouki A. Ebad","doi":"10.1109/ACCESS.2025.3548135","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548135","url":null,"abstract":"Skin cancer poses a significant global health challenge due to its increasing incidence rates. Accurate segmentation of skin lesions is essential for early detection and successful treatment, yet many current techniques struggle to balance computational efficiency with the ability to capture complex lesion features. This paper aims to develop an advanced deep learning model that improves segmentation accuracy while maintaining computational efficiency, offering a solution to the limitations of existing methods. We propose a novel dual-encoder deep learning architecture incorporating Squeeze-and-Excitation (SE) attention blocks. The model integrates two encoders: a pre-trained ResNet-50 for extracting local features efficiently and a Vision Transformer (ViT) to capture high-level features and long-range dependencies. The fusion of these features, enhanced by SE attention blocks, is processed through a CNN decoder, ensuring the model captures both local and global contextual information. The proposed model was evaluated on three benchmark datasets, ISIC 2016, ISIC 2017, and ISIC 2018, achieving Intersection over Union (IoU) scores of 89.53%, 87.02%, and 84.56%, respectively. These results highlight the model’s ability to outperform current methods in balancing segmentation accuracy and computational efficiency. The findings demonstrate that the proposed model enhances medical image analysis in dermatology, providing a promising tool for improving the early detection of skin cancer.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42608-42621"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601905","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.2025.3548580
Chunhui Lv;Yuliang Cai;Hanguang Su;Qiang He
This study presents an edge-based event-triggered control method to address the practical bipartite output consensus problem for heterogeneous multi-agent systems (MASs) under actuator attacks and external disturbances. Initially, a fully distributed edge-triggered compensator is introduced to efficiently observe the bipartite state of the leader. Zeno behavior is then eliminated for each agent. To address the negative consequences caused by actuator attacks, a distributed state predictor is designed. And a distributed resilient controller is proposed to mitigate the effects of actuator attacks and external disturbances, ensuring practical bipartite output consensus. Finally, the effectiveness of the approach is validated through a numerical example and a comparative example. Compared to existing methods, the proposed approach offers four key advantages: it removes dependence on global topology, eliminates continuous communication, and supports signed communication topologies; it reduces unnecessary communication through an edge-based event-triggered mechanism; it introduces a distributed state predictor to handle actuator attacks and complex external disturbances, improving robustness; and the resilient control strategy ensures practical consensus, providing a more practical and robust solution.
{"title":"Distributed Practical Bipartite Output Consensus for Heterogeneous Multi-Agent Systems With Actuator Attacks and External Disturbances Under Edge-Based Event-Triggered Mechanism","authors":"Chunhui Lv;Yuliang Cai;Hanguang Su;Qiang He","doi":"10.1109/ACCESS.2025.3548580","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548580","url":null,"abstract":"This study presents an edge-based event-triggered control method to address the practical bipartite output consensus problem for heterogeneous multi-agent systems (MASs) under actuator attacks and external disturbances. Initially, a fully distributed edge-triggered compensator is introduced to efficiently observe the bipartite state of the leader. Zeno behavior is then eliminated for each agent. To address the negative consequences caused by actuator attacks, a distributed state predictor is designed. And a distributed resilient controller is proposed to mitigate the effects of actuator attacks and external disturbances, ensuring practical bipartite output consensus. Finally, the effectiveness of the approach is validated through a numerical example and a comparative example. Compared to existing methods, the proposed approach offers four key advantages: it removes dependence on global topology, eliminates continuous communication, and supports signed communication topologies; it reduces unnecessary communication through an edge-based event-triggered mechanism; it introduces a distributed state predictor to handle actuator attacks and complex external disturbances, improving robustness; and the resilient control strategy ensures practical consensus, providing a more practical and robust solution.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"43259-43274"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621856","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.2025.3548044
Jiahua Ni;Yuwei Chen;Arman Goudarzi;Tong Wang;Lingang Yang;Shengwei Mei
Advanced adiabatic compressed air energy storage (AA-CAES) is a promising large-scale energy storage technology, offering a long lifespan, low maintenance, and high safety. However, its slower response speed limits its ability to handle the rapid fluctuations of wind and solar power. Combining AA-CAES with battery storage in a hybrid system provides an optimal solution for integrated energy bases, prompting the need for robust capacity planning. Existing AA-CAES planning strategies, developed primarily for grid-connected applications, often neglect AA-CAES’s dynamic characteristics, making them unsuitable for hybrid contexts. To address this issue, this paper proposes a capacity optimization strategy that incorporates AA-CAES’s dynamic behavior into a cost-minimization model with operational constraints. Using historical wind, solar, and load data, the proposed approach is compared with conventional battery-only configurations. The case study demonstrates that the proposed CAES-Li hybrid energy storage system achieves 30-45% annualized cost reductions compared to traditional Li-ES configurations, with sensitivity analyses revealing critical optimization pathways through efficiency enhancements and technology cost reductions.
{"title":"Dynamic Characteristics-Based Capacity Optimization Strategy for Hybrid AA-CAES and Battery Storage Systems in Source-Grid-Load-Storage Integrated Base","authors":"Jiahua Ni;Yuwei Chen;Arman Goudarzi;Tong Wang;Lingang Yang;Shengwei Mei","doi":"10.1109/ACCESS.2025.3548044","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548044","url":null,"abstract":"Advanced adiabatic compressed air energy storage (AA-CAES) is a promising large-scale energy storage technology, offering a long lifespan, low maintenance, and high safety. However, its slower response speed limits its ability to handle the rapid fluctuations of wind and solar power. Combining AA-CAES with battery storage in a hybrid system provides an optimal solution for integrated energy bases, prompting the need for robust capacity planning. Existing AA-CAES planning strategies, developed primarily for grid-connected applications, often neglect AA-CAES’s dynamic characteristics, making them unsuitable for hybrid contexts. To address this issue, this paper proposes a capacity optimization strategy that incorporates AA-CAES’s dynamic behavior into a cost-minimization model with operational constraints. Using historical wind, solar, and load data, the proposed approach is compared with conventional battery-only configurations. The case study demonstrates that the proposed CAES-Li hybrid energy storage system achieves 30-45% annualized cost reductions compared to traditional Li-ES configurations, with sensitivity analyses revealing critical optimization pathways through efficiency enhancements and technology cost reductions.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"42336-42349"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594326","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}
This study evaluated the seasonal dynamics of $H/bar {alpha }$ target decomposition parameters derived from the spatially and temporally averaged coherence matrix of fully polarimetric Ku-band GB-SAR time series data over a one-year analysis period across three distinct land cover types in a residential area in Hokkaido, Japan. Overall, mean values of $H_{spatial}/bar {alpha }_{spatial}$ and $H_{temp}/bar {alpha }_{temp} $ parameters were identified across different $H/bar {alpha }~2$ D space zones for each land cover type, indicating that they exhibited a specific characteristic associated with their scattering mechanism. Subsequently, seasonal changes were observed to shift their seasonal mean values, with the transitions occurring within the same $H/bar {alpha }~2$ D space zone for the built-up land cover type. As for tree land cover type, zonal alterations were more prevalent in $H_{spatial}/bar {alpha }_{spatial}$ than in $H_{temp}/bar {alpha }_{temp}$ for each seasonal transition. However, only the $H_{spatial}/bar {alpha }_{spatial}$ seasonal mean values for the shrub land cover type demonstrated zone changes. Afterward, correlations between precipitation and $H_{spatial}/bar {alpha }_{spatial}$ parameters were more pronounced in vegetated-related land cover types than in man-made ones. Meanwhile, snowfall had stronger relationships with built-up and tree areas, whereas shrub areas showed weaker correlations. Concurrently, statistically significant positive correlations were identified between $H_{temp}$ and precipitation across all land cover types. Nevertheless, the $bar {alpha }_{temp}$ yielded mixed results. For snowfall, built-up areas demonstrated more pronounced responses than trees and shrubs. Finally, outputs of this work could enhance knowledge of the underexplored yet compelling implementation of Ku-band GB-SAR time series data for continuous land cover monitoring activities.
{"title":"Seasonal Analysis of Polarimetric Responses Utilizing Ku-Band GB-SAR Time Series Data","authors":"Dandy Aditya Novresiandi;Yuta Izumi;Motoyuki Sato;Fathin Nurzaman;Koki Urano","doi":"10.1109/ACCESS.2025.3548139","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3548139","url":null,"abstract":"This study evaluated the seasonal dynamics of <inline-formula> <tex-math>$H/bar {alpha }$ </tex-math></inline-formula> target decomposition parameters derived from the spatially and temporally averaged coherence matrix of fully polarimetric Ku-band GB-SAR time series data over a one-year analysis period across three distinct land cover types in a residential area in Hokkaido, Japan. Overall, mean values of <inline-formula> <tex-math>$H_{spatial}/bar {alpha }_{spatial}$ </tex-math></inline-formula> and <inline-formula> <tex-math>$H_{temp}/bar {alpha }_{temp} $ </tex-math></inline-formula>parameters were identified across different <inline-formula> <tex-math>$H/bar {alpha }~2$ </tex-math></inline-formula>D space zones for each land cover type, indicating that they exhibited a specific characteristic associated with their scattering mechanism. Subsequently, seasonal changes were observed to shift their seasonal mean values, with the transitions occurring within the same <inline-formula> <tex-math>$H/bar {alpha }~2$ </tex-math></inline-formula>D space zone for the built-up land cover type. As for tree land cover type, zonal alterations were more prevalent in <inline-formula> <tex-math>$H_{spatial}/bar {alpha }_{spatial}$ </tex-math></inline-formula> than in <inline-formula> <tex-math>$H_{temp}/bar {alpha }_{temp}$ </tex-math></inline-formula> for each seasonal transition. However, only the <inline-formula> <tex-math>$H_{spatial}/bar {alpha }_{spatial}$ </tex-math></inline-formula> seasonal mean values for the shrub land cover type demonstrated zone changes. Afterward, correlations between precipitation and <inline-formula> <tex-math>$H_{spatial}/bar {alpha }_{spatial}$ </tex-math></inline-formula> parameters were more pronounced in vegetated-related land cover types than in man-made ones. Meanwhile, snowfall had stronger relationships with built-up and tree areas, whereas shrub areas showed weaker correlations. Concurrently, statistically significant positive correlations were identified between <inline-formula> <tex-math>$H_{temp}$ </tex-math></inline-formula> and precipitation across all land cover types. Nevertheless, the <inline-formula> <tex-math>$bar {alpha }_{temp}$ </tex-math></inline-formula> yielded mixed results. For snowfall, built-up areas demonstrated more pronounced responses than trees and shrubs. Finally, outputs of this work could enhance knowledge of the underexplored yet compelling implementation of Ku-band GB-SAR time series data for continuous land cover monitoring activities.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"41708-41722"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583208","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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