Pub Date : 2025-08-29DOI: 10.1109/JPROC.2025.3599840
Daniel Glover;Gayathri Krishnamoorthy;Hongda Ren;Anamika Dubey;Assefaw Gebremedhin
The rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.
{"title":"Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey","authors":"Daniel Glover;Gayathri Krishnamoorthy;Hongda Ren;Anamika Dubey;Assefaw Gebremedhin","doi":"10.1109/JPROC.2025.3599840","DOIUrl":"10.1109/JPROC.2025.3599840","url":null,"abstract":"The rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 6","pages":"557-585"},"PeriodicalIF":25.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The brain–computer interface (BCI) establishes a direct communication system between the brain and a computer or other external devices. Since the inception of BCI technology half a century ago, it has advanced rapidly and developed into an active area of frontier research in modern applied science and technology. This article provides a comprehensive survey on BCI with respect to a brain-in-the-loop communication system. In the present work, we first introduce the underlying architecture of the BCI system from the theoretical and methodological perspectives of communication systems. The key technologies are then detailed, including the construction of BCI system, brain-to-computer (B2C) communication, computer-to-brain (C2B) communication, and multiuser BCI systems. Additionally, this article discusses the various applications of BCI and the challenges they face. Finally, this article discusses BCI’s future development, with an emphasis on the convergence of human intelligence (HI) and artificial intelligence (AI), and the interaction of BCI with wireless communication and the metaverse.
{"title":"Brain–Computer Interface—A Brain-in-the-Loop Communication System","authors":"Xiaorong Gao;Yijun Wang;Xiaogang Chen;Bingchuan Liu;Shangkai Gao","doi":"10.1109/JPROC.2025.3600389","DOIUrl":"10.1109/JPROC.2025.3600389","url":null,"abstract":"The brain–computer interface (BCI) establishes a direct communication system between the brain and a computer or other external devices. Since the inception of BCI technology half a century ago, it has advanced rapidly and developed into an active area of frontier research in modern applied science and technology. This article provides a comprehensive survey on BCI with respect to a brain-in-the-loop communication system. In the present work, we first introduce the underlying architecture of the BCI system from the theoretical and methodological perspectives of communication systems. The key technologies are then detailed, including the construction of BCI system, brain-to-computer (B2C) communication, computer-to-brain (C2B) communication, and multiuser BCI systems. Additionally, this article discusses the various applications of BCI and the challenges they face. Finally, this article discusses BCI’s future development, with an emphasis on the convergence of human intelligence (HI) and artificial intelligence (AI), and the interaction of BCI with wireless communication and the metaverse.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 5","pages":"478-511"},"PeriodicalIF":25.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-08-22DOI: 10.1109/JPROC.2025.3599126
Ali Hamdi;Balsam Alkouz;Babar Shahzaad;Athman Bouguettaya;Azadeh Ghari Neiat;Flora Salim;Du Yong Kim
We conduct a survey on drones used as a service, denoted as drone-as-a-service (DaaS). We develop a novel taxonomy based on DaaS functions, research tasks, and application domains. We provide a discussion on drones and their associated capabilities based on their type of use. We propose a three-layered DaaS system architecture that vertically integrates cloud computing, drones, and services as a reference framework to compare existing drone service implementations. Additionally, we propose a representative uncertainty-aware DaaS model for delivery scenarios, illustrating how service definitions can incorporate both functional and nonfunctional attributes under dynamic environmental conditions. Finally, we identify and discuss future research directions and open problems related to the use of drones for service delivery.
{"title":"Drone-as-a-Service: Research Challenges and Directions","authors":"Ali Hamdi;Balsam Alkouz;Babar Shahzaad;Athman Bouguettaya;Azadeh Ghari Neiat;Flora Salim;Du Yong Kim","doi":"10.1109/JPROC.2025.3599126","DOIUrl":"10.1109/JPROC.2025.3599126","url":null,"abstract":"We conduct a survey on drones used as a service, denoted as drone-as-a-service (DaaS). We develop a novel taxonomy based on DaaS functions, research tasks, and application domains. We provide a discussion on drones and their associated capabilities based on their type of use. We propose a three-layered DaaS system architecture that vertically integrates cloud computing, drones, and services as a reference framework to compare existing drone service implementations. Additionally, we propose a representative uncertainty-aware DaaS model for delivery scenarios, illustrating how service definitions can incorporate both functional and nonfunctional attributes under dynamic environmental conditions. Finally, we identify and discuss future research directions and open problems related to the use of drones for service delivery.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 5","pages":"416-442"},"PeriodicalIF":25.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine learning (ML) typically relies on the assumption that training and testing distributions are identical and that data are centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly, and data are often distributed across different devices, organizations, or edge nodes. Consequently, it is to develop models capable of effectively generalizing across unseen distributions in data spanning various domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG synergizes federated learning (FL) and domain generalization (DG) techniques, facilitating collaborative model development across diverse source domains for effective generalization to unseen domains, all while maintaining data privacy. However, generalizing the federated model under domain shifts remains a complex, underexplored issue. This article provides a comprehensive survey of the latest advancements in this field. Initially, we discuss the development process from traditional ML to domain adaptation (DA) and DG, leading to FDG, as well as provide the corresponding formal definition. Subsequently, we classify recent methodologies into four distinct categories: federated domain alignment (FDAL), data manipulation (DM), learning strategies (LSs), and aggregation optimization (AO), detailing appropriate algorithms for each. We then overview commonly utilized datasets, applications, evaluations, and benchmarks. Conclusively, this survey outlines potential future research directions.
{"title":"Federated Domain Generalization: A Survey","authors":"Ying Li;Xingwei Wang;Rongfei Zeng;Praveen Kumar Donta;Ilir Murturi;Min Huang;Schahram Dustdar","doi":"10.1109/JPROC.2025.3596173","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3596173","url":null,"abstract":"Machine learning (ML) typically relies on the assumption that training and testing distributions are identical and that data are centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly, and data are often distributed across different devices, organizations, or edge nodes. Consequently, it is to develop models capable of effectively generalizing across unseen distributions in data spanning various domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG synergizes federated learning (FL) and domain generalization (DG) techniques, facilitating collaborative model development across diverse source domains for effective generalization to unseen domains, all while maintaining data privacy. However, generalizing the federated model under domain shifts remains a complex, underexplored issue. This article provides a comprehensive survey of the latest advancements in this field. Initially, we discuss the development process from traditional ML to domain adaptation (DA) and DG, leading to FDG, as well as provide the corresponding formal definition. Subsequently, we classify recent methodologies into four distinct categories: federated domain alignment (FDAL), data manipulation (DM), learning strategies (LSs), and aggregation optimization (AO), detailing appropriate algorithms for each. We then overview commonly utilized datasets, applications, evaluations, and benchmarks. Conclusively, this survey outlines potential future research directions.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 4","pages":"370-410"},"PeriodicalIF":25.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-13DOI: 10.1109/JPROC.2025.3593952
Andreas Triantafyllopoulos;Iosif Tsangko;Alexander Gebhard;Annamaria Mesaros;Tuomas Virtanen;Björn W. Schuller
Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition—i.e., the use of machines to understand sounds. They feature several advantages over traditional pipelines: among others, the ability to consolidate multiple tasks in a single model, the option to leverage knowledge from other modalities, and the readily available interaction with human users. Naturally, these promises have created substantial excitement in the audio community and have led to a wave of early attempts to build new, generalpurpose FMs for audio. In the present contribution, we give an overview of computational audio analysis as it transitions from traditional pipelines toward auditory FMs. Our work highlights the key operating principles that underpin those models and showcases how they can accommodate multiple tasks that the audio community previously tackled separately.
{"title":"Computer Audition: From Task-Specific Machine Learning to Foundation Models","authors":"Andreas Triantafyllopoulos;Iosif Tsangko;Alexander Gebhard;Annamaria Mesaros;Tuomas Virtanen;Björn W. Schuller","doi":"10.1109/JPROC.2025.3593952","DOIUrl":"10.1109/JPROC.2025.3593952","url":null,"abstract":"Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition—i.e., the use of machines to understand sounds. They feature several advantages over traditional pipelines: among others, the ability to consolidate multiple tasks in a single model, the option to leverage knowledge from other modalities, and the readily available interaction with human users. Naturally, these promises have created substantial excitement in the audio community and have led to a wave of early attempts to build new, generalpurpose FMs for audio. In the present contribution, we give an overview of computational audio analysis as it transitions from traditional pipelines toward auditory FMs. Our work highlights the key operating principles that underpin those models and showcases how they can accommodate multiple tasks that the audio community previously tackled separately.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 4","pages":"317-343"},"PeriodicalIF":25.9,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11124350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-07-30DOI: 10.1109/jproc.2025.3582502
William J. Blackwell, Scott A. Braun, George R. Alvey, Robert Atlas, Ralf Bennartz, Jessica Braun, Kerri Cahoy, Ruiyao Chen, Galina Chirokova, Brittany Dahl, James Darlow, Mark DeMaria, Michael Diliberto, Jason P. Dunion, Patrick Duran, Thomas J. Greenwald, Sarah Griffin, Zachary Griffith, Derrick Herndon, Jeffrey D. Hawkins, Satya Kalluri, C. Kidd, Min-Jeong Kim, R. Vincent Leslie, Frank Marks, Toshi Matsui, W. McCarty, Adam Milstein, Glenn Perras, Michael L. Pieper, Robert Rogers, Christopher Velden, Yalei You, Nick V. Zorn
{"title":"High Revisit-Rate Tropical Cyclone Observations From the NASA TROPICS Satellite Constellation Mission","authors":"William J. Blackwell, Scott A. Braun, George R. Alvey, Robert Atlas, Ralf Bennartz, Jessica Braun, Kerri Cahoy, Ruiyao Chen, Galina Chirokova, Brittany Dahl, James Darlow, Mark DeMaria, Michael Diliberto, Jason P. Dunion, Patrick Duran, Thomas J. Greenwald, Sarah Griffin, Zachary Griffith, Derrick Herndon, Jeffrey D. Hawkins, Satya Kalluri, C. Kidd, Min-Jeong Kim, R. Vincent Leslie, Frank Marks, Toshi Matsui, W. McCarty, Adam Milstein, Glenn Perras, Michael L. Pieper, Robert Rogers, Christopher Velden, Yalei You, Nick V. Zorn","doi":"10.1109/jproc.2025.3582502","DOIUrl":"https://doi.org/10.1109/jproc.2025.3582502","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"26 1","pages":""},"PeriodicalIF":20.6,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3587420
{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2025.3587420","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3587420","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"312-312"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-07-28DOI: 10.1109/JPROC.2025.3587416
{"title":"Proceedings of the IEEE Publication Information","authors":"","doi":"10.1109/JPROC.2025.3587416","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3587416","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"C2-C2"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098583","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-07-28DOI: 10.1109/JPROC.2025.3583866
Summary form only: Abstracts of articles presented in this issue of the publication.
仅以摘要形式提供：本刊发表的文章摘要。
{"title":"Scanning the Issue","authors":"","doi":"10.1109/JPROC.2025.3583866","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3583866","url":null,"abstract":"Summary form only: Abstracts of articles presented in this issue of the publication.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"210-212"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-07-28DOI: 10.1109/JPROC.2025.3587422
{"title":"IEEE Membership","authors":"","doi":"10.1109/JPROC.2025.3587422","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3587422","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"C3-C3"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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