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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Performance-Aware control of Modular Batteries For Fast Frequency Response

arXiv

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arXiv 2025年

作者： He, Yutong Ruan, Guangchun Zhong, Haiwang State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Lab for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States

Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the heterogeneous module-level characteristics such as dynamic operational efficiencies and battery degradation. In addition, real-time decision making within seconds is required to enable fast frequency response. In order to address these issues, this paper proposes a performance-aware scheduling approach for battery modules to deliver fast frequency response (FFR) support. In particular, the conduction loss and switching loss of battery packs as well as converters are captured within a mix-integer quadratic constrained program (MIQCP). The cycle-based aging model identifies the aging cost of battery modules during frequent cycling by introducing the aging subgradient calculation and linearization. Case studies based on real-world battery data show that the proposed scheduling approach can effectively reduce power loss cost by nearly 28%-57% and battery aging cost by 4%-15% compared to conventional methods, which can also enhance the SoC balance. Copyright © 2025, The Authors. All rights reserved.

关键词： Battery management systems

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Facile construction of N-doped TiO2 nanohybrids with enriched oxygen vacancies for enhanced photocatalytic green hydrogen production

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Ceramics International 2025年

作者： Tao, Zichen Zhang, Bin Chen, Wenxin Cao, Ruwen Liu, Jie Wang, Lidong Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China

A series of N-doped TiO2 nanohybrids (xN-TiO2) were successfully prepared via a facile solvothermal route by using oleylamine as N source, for enhancing photocatalytic hydrogen evolution reaction (HER). The doped-N atoms induced the generation of abundant oxygen vacancies (OVs). The resulted impurity and defect levels in xN-TiO2 contributed to their narrowed band gap, enhanced visible light response, and promoted separation and transfer of photogenerated carrier. Moreover, the N-doping optimized the distribution of OVs, and therefore affect the conduction band (CB) location of catalyst. Herein, the 3N-TiO2 catalyst with excellent photoelectric properties and appropriate CB location, showed the photocatalytic HER rate of 3984.71 μmol h−1 g−1 under the simulated sunlight irradiation, which was 5.5 and 29.0 times that of the undoped TiO2 and commercial P25, respectively. Meanwhile, the 3N-TiO2 catalyst exhibited the excellent stability and salt-resistance. This work provides a novel insight for the band alignment of photocatalysts by defects regulation, and opens the new possibility for synthesizing N-doped TiO2 catalyst by a more practical and safe method. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Hydrogen evolution reaction

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FAU zeolites from coal/rice husk co-combustion ash (co-ash) for CO2 adsorption: Synthesis, characterization, adsorption and regeneration performance

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Separation and Purification Technology 2025年 369卷

作者： Xu, Qing Lu, Jianyi Zhou, Zhiyong Jin, Yujia Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China

Synthesis of zeolite from industrial wastes has become an important environmental protection strategy. This study explored the synthesis of nano-sized NaX and NaY zeolites from coal/rice husk co-combustion ash (co-ash) using alkali-melting-hydrothermal synthesis and seed guiding methods, followed by modification with Li+ and K+ exchange. NaY zeolite, synthesized at a SiO2/Al2O3 ratio of 5 and crystallization temperature of 100 °C, exhibited the most complete crystal structure and optimal CO2 adsorption capacity. The specific surface area (SSA) of NaX and NaY zeolites (583.0 m2/g and 790.1 m2/g, respectively) are comparable to those of commercial zeolites, emphasizing their structural parity, and demonstrating their potential as effective CO2 adsorbents. NaY demonstrated a CO2 adsorption capacity of 5.31 mmol/g at 298.15 K and 1 bar, competitive with other conventionally synthesized and commercial zeolites. Cation exchange had a minimal impact on CO2 adsorption of NaY, with LiX improving capacity by 11.65 % compared to NaX, while LiY showed only a 2.94 % increase. K+ exchange negatively affected both zeolites. Kinetic and thermodynamic analyses, using the proposed first-order (PFO) and Langmuir-Freundlich models, indicated that CO2 adsorption is a rapid physisorption process with thermal effects ranging from 22 to 41 kJ/mol. Both zeolites exhibited excellent CO2/N2 selectivity and regeneration stability, with the loss of adsorption capacity retaining only 10.57 % and 9.63 % for NaX and NaY after fifteen cycles. This highlights their potential as efficient CO2 adsorbents. © 2025 Elsevier B.V.

关键词： Coal combustion

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Semi-supervised feature contrast incremental learning framework for bearing fault diagnosis with limited labeled samples

引用

Applied Soft Computing 2025年 176卷

作者： Tao, Xuyang Shen, Changqing Li, Lin Wang, Dong Shi, Juanjuan Zhu, Zhongkui School of Rail Transportation Soochow University Suzhou215131 China Space Optoelectronic Measurement and Perception Lab. Beijing Institute of Control Engineering Beijing100190 China The State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong University Shanghai200240 China

In real-world scenarios, rotating machinery consistently introduces new fault classes, but intelligent fault diagnosis methods mostly rely on the closed-world assumption, expecting only known fault classes during testing. Moreover, obtaining a sufficient number of labeled samples is often challenging. These challenges constrain the application and reliability of intelligent diagnosis models in real-world scenarios. Semi-supervised incremental learning enables continuous learning of new fault classes in an open environment, relying on a small number of labeled samples and a certain number of unlabeled samples. To address the semi-supervised incremental learning problem of fault classes, semi-supervised feature contrast (SSFC) is proposed, a new approach for bearing fault diagnosis with limited labeled samples. Specifically, a feature contrastive loss incorporating enhancement strategies is designed, independent of labeled sample information. This approach enables the model to retain knowledge of old classes while learning about new ones. A label reconstruction mechanism based on class centroids is utilized, effectively leveraging the structural information inherent in the samples to support supervised training. A dynamic class prototype cosine classifier initialized by class centroids is devised to mitigate interference between knowledge of fault classes. Finally, two incremental fault diagnosis case studies are designed to evaluate the effectiveness of the proposed method. The fault diagnosis results indicate that SSFC can continuously learn knowledge of new fault classes with limited labeled samples and effectively alleviate catastrophic forgetting. © 2025 Elsevier B.V.

关键词： Self-supervised learning

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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Simultaneous estimations of quantum state and detector through multiple quantum processes

arXiv

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arXiv 2025年

作者： Xiao, Shuixin Liang, Weichao Wang, Yuanlong Dong, Daoyi Petersen, Ian R. Ugrinovskii, Valery School of Engineering and Technology University of New South Wales CanberraACT2600 Australia School of Engineering Australian National University CanberraACT2601 Australia Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China CIICADA Lab School of Engineering Australian National University ACT2601 Australia

The estimation of all the parameters in an unknown quantum state or measurement device, commonly known as quantum state tomography (QST) and quantum detector tomography (QDT), is crucial for comprehensively characterizing and controlling quantum systems. In this paper, we introduce a framework, in two different bases, that utilizes multiple quantum processes to simultaneously identify a quantum state and a detector. We develop a closed-form algorithm for this purpose and prove that the mean squared error (MSE) scales as O(1/N) for both QST and QDT, where N denotes the total number of state copies. This scaling aligns with established patterns observed in previous works that addressed QST and QDT as independent tasks. Furthermore, we formulate the problem as a sum of squares (SOS) optimization problem with semialgebraic constraints, where the physical constraints of the state and detector are characterized by polynomial equalities and inequalities. The effectiveness of our proposed methods is validated through numerical examples. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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Unintended side effect of the coal-to-gas policy in North China Plain: Migration of the sources and health risks of ambient PAHs

引用

Science of the Total Environment 2025年 958卷 178050页

作者： Li, Zhiyong Liu, Chen Ren, Zhuangzhuang Liu, Jinming Ma, Xiaohua Ning, Zhi Meng, Jianwei Liu, Aiqin Ma, Huichun Wang, Lei Chen, Lan Wang, Hao Kong, Shaofei Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China Hebei Key Lab of Mineral Resources and Ecological Environment Monitoring Hebei Research Center for Geoanalysis Baoding071051 China Department of Atmospheric Sciences School of Environmental Sciences China University of Geosciences Wuhan430074 China

Ongoing coal-to-gas (CTG) largely cut down both coal consumption and associated PM2.5. However, a knowledge gap still existed in CTG impacts on the other energy and organic pollutant emissions. Coupling on-site investigation with statistical yearbooks, we provided a more realistic energy evolutions before (BCTG), during (DCTG), and after (ACTG) the CTG for Hebei Province. Together, we examined the impacts of CTG derived energy conversion on PM2.5-bound PAHs at urban (UA)/suburban rural (SRA)/remote rural (RRA) sites in winter 2022. As expected, the consumptions of coal and natural gas (NG) far decreased and increased from BCTG to ACTG, respectively. Accidentally, biomass usage rose by 60.7%, and rural CTG acted as a main driver. Specially, SRA's NG-shortage and coal-stove demolition should be the main inducements, and RRA's coal-sale ban was another trigger in the early stage of CTG. ∑18PAHs and ∑8TPAHs stand for the sum of 18 PAHs and 8 toxic PAHs, respectively. ∑18PAHs (ng/m3) presented as SRA (81.8) > RRA (46.4) > UA (19.4). Biomass burning (BB) and NG combustion (NGC) contributed most to∑18PAHs of 31.0% and 23.1% at SRA, resulting in the highest ∑18PAHs, ∑18PAHs/PM2.5, and ∑8TPAHs/PM2.5, and incremental lifetime cancer risk values. Also, NGC has become the second largest contributor at UA. Variations in both diagnostic ratios and source-depend isomers further proved the prominence of NGC related PAHs at UA vs. SRA. Notably, RRA was least affected by the CTG, coal combustion (CC, 40.4%) and BB (32.6%) still occupied the top positions. In short, CTG gave rise to an upsurge in biomass usage, and the incremental PAHs emissions from BB vs. NGC. This study underlined that the priorities should be given to rural NG guarantee and subsidy retention, and biomass prohibition for further air quality improvement. © 2024 Elsevier B.V.

关键词： Coal

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Stochastic sub-gradient algorithm for distributed optimization with random sleep scheme

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控制理论与技术（英文版） 2015年第4期13卷 333-347页

作者： Peng YI Yiguang HONG Key Lab of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

In this paper, we consider a distributed convex optimization problem of a multi-agent system with the global objective function as the sum of agents’ individual objective functions. To solve such an optimization problem, we propose a distributed stochastic sub-gradient algorithm with random sleep scheme. In the random sleep scheme, each agent independently and randomly decides whether to inquire the sub-gradient information of its local objective function at each iteration. The algorithm not only generalizes distributed algorithms with variable working nodes and multi-step consensus-based algorithms, but also extends some existing randomized convex set intersection results. We investigate the algorithm convergence properties under two types of stepsizes: the randomized diminishing stepsize that is heterogeneous and calculated by individual agent, and the fixed stepsize that is homogeneous. Then we prove that the estimates of the agents reach consensus almost surely and in mean, and the consensus point is the optimal solution with probability 1, both under randomized stepsize. Moreover, we analyze the algorithm error bound under fixed homogeneous stepsize, and also show how the errors depend on the fixed stepsize and update rates.

关键词： Distributed optimization sub-gradient algorithm random sleep multi-agent systems randomized algorithm

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