Pub Date : 2025-12-29DOI: 10.1007/s40747-025-02176-1
Jiansheng Jin, Xinfu Pang, Baoshi Wang, Duo Wang, Zhedong Zheng
The integration of renewable energy into modern power systems requires scheduling strategies that balance economic efficiency with low-carbon objectives. This study develops an optimal scheduling framework for virtual power plant (VPP) operating under carbon trading and green certificate mechanisms. First, the model coordinates the dispatch of gas turbines, wind power, photovoltaic units, and energy storage systems, incorporating market incentives and emission constraints into a unified optimization problem. Second, a Q-learning enhanced particle swarm optimization algorithm (QPSO) is designed, which adaptively adjusts inertia weights and learning factors according to search states to improve convergence stability and solution quality. Finally, comparative analyses with the standard particle swarm optimization algorithm and independent Q-learning demonstrate significant improvements: under the dual-market scenario, net profit increases by 89.9%, renewable energy utilization rises by 19.9%, and carbon emissions are reduced by 39.4%. These results indicate that combining dual-market participation with adaptive optimization provides a feasible and effective approach to enhancing both the economic and environmental performance of VPP operations.
{"title":"Optimal scheduling method of carbon-green certificate trading virtual power plant via Q-learning-enhanced particle swarm algorithm","authors":"Jiansheng Jin, Xinfu Pang, Baoshi Wang, Duo Wang, Zhedong Zheng","doi":"10.1007/s40747-025-02176-1","DOIUrl":"https://doi.org/10.1007/s40747-025-02176-1","url":null,"abstract":"The integration of renewable energy into modern power systems requires scheduling strategies that balance economic efficiency with low-carbon objectives. This study develops an optimal scheduling framework for virtual power plant (VPP) operating under carbon trading and green certificate mechanisms. First, the model coordinates the dispatch of gas turbines, wind power, photovoltaic units, and energy storage systems, incorporating market incentives and emission constraints into a unified optimization problem. Second, a Q-learning enhanced particle swarm optimization algorithm (QPSO) is designed, which adaptively adjusts inertia weights and learning factors according to search states to improve convergence stability and solution quality. Finally, comparative analyses with the standard particle swarm optimization algorithm and independent Q-learning demonstrate significant improvements: under the dual-market scenario, net profit increases by 89.9%, renewable energy utilization rises by 19.9%, and carbon emissions are reduced by 39.4%. These results indicate that combining dual-market participation with adaptive optimization provides a feasible and effective approach to enhancing both the economic and environmental performance of VPP operations.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"23 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing task-oriented grasping approaches often rely on 2D pixel-wise affordance segmentation or predefined part annotations, limiting their applicability in unstructured 3D environments and constraining the grasp planning space. To overcome these limitations, we introduce a novel affordance-labeled grasp dataset constructed on simulation, capturing diverse functional interactions across object categories in a 6-DoF space. Building on this foundation, we propose a unified, language-guided grasping framework that takes partial point clouds and natural language instructions as input to generate semantically meaningful and geometrically feasible grasp poses. Specifically, a vision-language affordance grounding module produces dense 3D affordance maps aligned with task semantics, and a task-oriented grasp pipeline predicts coarse grasp candidates with implicit affordance cues. The coarse grasp proposals are subsequently refined based on visual affordance guidance, significantly enhancing both semantic alignment and grasp practicality. Extensive experiments in synthetic and real-world scenarios demonstrate that our method outperforms state-of-the-art approaches, effectively generalizing across diverse objects and tasks.
{"title":"Enhancing task-oriented robotic grasping via 3D affordance grounding from vision-language models","authors":"Wenkai Chen, Shang-Ching Liu, Qingdu Li, Yung-Hui Li, Jianwei Zhang","doi":"10.1007/s40747-025-02169-0","DOIUrl":"https://doi.org/10.1007/s40747-025-02169-0","url":null,"abstract":"Existing task-oriented grasping approaches often rely on 2D pixel-wise affordance segmentation or predefined part annotations, limiting their applicability in unstructured 3D environments and constraining the grasp planning space. To overcome these limitations, we introduce a novel affordance-labeled grasp dataset constructed on simulation, capturing diverse functional interactions across object categories in a 6-DoF space. Building on this foundation, we propose a unified, language-guided grasping framework that takes partial point clouds and natural language instructions as input to generate semantically meaningful and geometrically feasible grasp poses. Specifically, a vision-language affordance grounding module produces dense 3D affordance maps aligned with task semantics, and a task-oriented grasp pipeline predicts coarse grasp candidates with implicit affordance cues. The coarse grasp proposals are subsequently refined based on visual affordance guidance, significantly enhancing both semantic alignment and grasp practicality. Extensive experiments in synthetic and real-world scenarios demonstrate that our method outperforms state-of-the-art approaches, effectively generalizing across diverse objects and tasks.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"31 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-29DOI: 10.1007/s40747-025-02162-7
Nannan Huang, Abdul Hadi Abd Rahman, Kauthar Mohd Daud, Liantao Shi, Hongqing Wang
{"title":"Dynamic feature scale and multi-scale fusion networks for polyp segmentation","authors":"Nannan Huang, Abdul Hadi Abd Rahman, Kauthar Mohd Daud, Liantao Shi, Hongqing Wang","doi":"10.1007/s40747-025-02162-7","DOIUrl":"https://doi.org/10.1007/s40747-025-02162-7","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"86 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-29DOI: 10.1007/s40747-025-02207-x
Chang Cai, Yuchen Liu, Dan Chen, Lei Cai
Replanning paths in emergencies is essential for the successful completion of coverage tasks. In this context, this study specifically focuses on centralized path replanning for multiple autonomous underwater vehicles (AUVs) equipped with side-scan sonar, aiming to efficiently allocate uncovered regions and plan optimal paths for covering these assigned areas. The issue is formulated as a customized multi-robot multi-regional coverage path planning (M $$ ^{2} $$2 CPP) problem. Taking account of the limited AUV energies, vulnerable imaging quality and paths’ structure, this study proposes a novel lawn-mower and cooperative co-evolution (LMCC) method. First, the lawnmower method is adopted to determine the intra-region paths as well as the entrance and exit locations of each region. Then, a customized cooperative co-evolution method is proposed to solve optimal region assignment, visiting order, and entrance positions. Additionally, a novel and simple population division strategy is designed for coding the area assignment results efficiently. According to simulation results, the LMCC method can balance AUV workloads and generate optimal paths based on positions and energies. In addition, fewer paths connect different regions to ensure that there is an adequate supply of energy to cover them which is an innovation abstracted from real task scenarios.
{"title":"A Fast Multi-AUV Multi-Regional Coverage Path Planner in Coverage Tasks Based on Co-evolution","authors":"Chang Cai, Yuchen Liu, Dan Chen, Lei Cai","doi":"10.1007/s40747-025-02207-x","DOIUrl":"https://doi.org/10.1007/s40747-025-02207-x","url":null,"abstract":"Replanning paths in emergencies is essential for the successful completion of coverage tasks. In this context, this study specifically focuses on centralized path replanning for multiple autonomous underwater vehicles (AUVs) equipped with side-scan sonar, aiming to efficiently allocate uncovered regions and plan optimal paths for covering these assigned areas. The issue is formulated as a customized multi-robot multi-regional coverage path planning (M <jats:inline-formula> <jats:alternatives> <jats:tex-math>$$ ^{2} $$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mn>2</mml:mn> </mml:mmultiscripts> </mml:math> </jats:alternatives> </jats:inline-formula> CPP) problem. Taking account of the limited AUV energies, vulnerable imaging quality and paths’ structure, this study proposes a novel lawn-mower and cooperative co-evolution (LMCC) method. First, the lawnmower method is adopted to determine the intra-region paths as well as the entrance and exit locations of each region. Then, a customized cooperative co-evolution method is proposed to solve optimal region assignment, visiting order, and entrance positions. Additionally, a novel and simple population division strategy is designed for coding the area assignment results efficiently. According to simulation results, the LMCC method can balance AUV workloads and generate optimal paths based on positions and energies. In addition, fewer paths connect different regions to ensure that there is an adequate supply of energy to cover them which is an innovation abstracted from real task scenarios.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-29DOI: 10.1007/s40747-025-02166-3
Li Tan, Haixia Zhao
Deep reinforcement learning shows broad prospects in multi-unmanned aerial vehicle(UAV) collaborative search and rescue tasks. However, in the face of high-dimensional collaborative decision-making spaces and limited computing resources, its performance is vulnerable to limitations. This paper proposes a deep deterministic policy gradient method based on linear attention. By introducing the linear attention mechanism based on random feature mapping, while effectively modeling the interaction among UAVs, the computational and storage overcosts caused by the increase in the number of UAVs have been significantly reduced. Furthermore, by combining smooth experience replay and adaptive importance sampling mechanism, the training efficiency and strategy stability have been further improved. The simulation experiments on both post-disaster response search and dynamic containment tasks demonstrate that the proposed algorithm consistently outperforms existing methods. In small-scale scenarios, it maintains nearly perfect success rates, while in medium- and large-scale settings it achieves up to 90.6% and 85.2% success rates in the post-disaster response search task and up to 90.1% and 80.2% in the containment task, corresponding to relative improvements of 15–21% over baselines. These results highlight both the robustness of the method in simple cases and its clear advantage under more challenging multi-UAV conditions.
{"title":"A multi-UAV rapid post-disaster search and rescue method based on deep reinforcement learning","authors":"Li Tan, Haixia Zhao","doi":"10.1007/s40747-025-02166-3","DOIUrl":"https://doi.org/10.1007/s40747-025-02166-3","url":null,"abstract":"Deep reinforcement learning shows broad prospects in multi-unmanned aerial vehicle(UAV) collaborative search and rescue tasks. However, in the face of high-dimensional collaborative decision-making spaces and limited computing resources, its performance is vulnerable to limitations. This paper proposes a deep deterministic policy gradient method based on linear attention. By introducing the linear attention mechanism based on random feature mapping, while effectively modeling the interaction among UAVs, the computational and storage overcosts caused by the increase in the number of UAVs have been significantly reduced. Furthermore, by combining smooth experience replay and adaptive importance sampling mechanism, the training efficiency and strategy stability have been further improved. The simulation experiments on both post-disaster response search and dynamic containment tasks demonstrate that the proposed algorithm consistently outperforms existing methods. In small-scale scenarios, it maintains nearly perfect success rates, while in medium- and large-scale settings it achieves up to 90.6% and 85.2% success rates in the post-disaster response search task and up to 90.1% and 80.2% in the containment task, corresponding to relative improvements of 15–21% over baselines. These results highlight both the robustness of the method in simple cases and its clear advantage under more challenging multi-UAV conditions.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"30 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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