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Introducing ionic mediators to construct peak-splitting manganese oxide structures for efficient oxygen reduction reactions

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Journal of Materials science & technology 2024年第34期201卷 139-148页

作者： Haoliang Ma Xiong Li Mengmeng Tian Lanlan Li Xiaofei Yu Xinghua Zhang Zunming Lu Xiaojing Yang School of Materials Science and Engineering Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control TechnologyHebei University of TechnologyTianjin 300130China School of Artificial Intelligence and Information Technology Nanjing University of Chinese MedicineNanjing 210023China

Manganese oxides have received extensive attention in Oxygen Reduction Reaction(ORR)***-ever,the poor electrical conductivity and oxygen adsorption capacity of manganese oxides limit their development,so it is necessary to prepare catalysts with highly active *** propose a method to introduce an ionically conductive medium and induce structural distortion via thermodynamics,re-sulting in novel peak-splitting highly active structures for enhanced oxygen reduction *** the aid of refined structural analysis,High-angle annular dark-field(HAADF-STEM)imaging,and theoretical calculations,it is elucidated that this peak-splitting structure results from thermodynamically induced structural distortions and atomic *** introduction of the ionically conductive medium promotes the formation of Ag-O-Mn conductive bond bridges,which regulate the energy level matching of manganese oxides and oxygen *** thermodynamically induced structural distortion ex-poses new atomic planes in the material,and the creation of this highly active surface is accompanied by a redistribution of surface charges,which modulates the adsorption of manganese active sites with oxy-gen intermediates and greatly enhances the oxygen reduction ***,this Ag-OMS-2 with a novel split structure exhibits excellent activity at 0.836 V,superior to Pt/C kinetics and good *** preparation of such highly active structures is instructive for the development of manganese oxides.

关键词： Fuel cells Electrochemistry Manganese Structural distortion Ionically conductive medium

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Whale optimisation algorithm with role labour division for multimodal optimisation

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International Journal of Innovative Computing and Applications 2024年第1期15卷 26-37页

作者： Wu, Bowen Xiao, Renbin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China Institute of Artificial Intelligence Huazhong University of Science and Technology Hubei Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education of China Huazhong University of Science and Technology Hubei Wuhan430074 China

The multimodal optimisation problem challenges the balance between diversity and convergence, which poses a great degree of challenge to traditional population-based intelligence optimisers. The whale optimisation algorithm has the limitation of easily falling into local optimum when facing multimodal optimisation. To address this problem, this paper proposes a whale optimisation algorithm with role labour division (WOA-RDL). In WOA-RDL, a multi-stage role division of labour mechanism is designed to divide the optimisation process into two stages of group hunting. In the first stage, the task allocation in the hunting process is considered as two types of search tasks: exploration and exploitation. The external environmental stimulus of the task and the internal response threshold of the individual were designed to allow individuals to switch between exploration and exploitation tasks flexibly. In the second stage, the distribution mechanism of different roles was designed to simulate the 'strong to weak' distribution rule for the distribution of results after the hunt. The elite roles are selected to further strengthen their capabilities or remain robust, while the inferior roles are predicted and eliminated to improve the overall optimisation of the population and reduce the risk of falling into a local optimum. The results of the optimisation experiments using multimodal, unimodal, CEC2017 combined functions and CEC2009 dynamic functions to validate the effectiveness of the algorithm. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Optimization algorithms

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A Non-Iterative Network Utilization Pricing Mechanism for P2P Energy Trading

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1301-1306页

作者： Yun Liu Yunqiang Gao Yuanzheng Li Jizhong Zhu Hoay Beng Gooi School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhan 430074China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore IEEE

To sustain operation and maintenance of an active distribution network(ADN),a network fee should be charged by the distribution network service provider(DNSP)for facilitating the P2P energy trading *** this end,this paper models the interaction among the DNSP and multiple prosumers as a Stackelberg game,and then develops a non-iterative and decentralized transactive mechanism to simultaneously achieve optimal network utilization pricing and peer-to-peer(P2P)*** results in an ADN with four prosumers connected to a common substation bus validate the effectiveness and efficiency of the proposed scheme.

关键词： Distribution network service provider network fee non-iterative peer-to-peer trading prosumer

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Teachers cooperation:team-knowledge distillation for multiple cross-domain few-shot learning

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Frontiers of Computer science 2023年第2期17卷 91-99页

作者： Zhong JI Jingwei NI Xiyao LIU Yanwei PANG School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China Tianjin Key Laboratory of Brain-Inspired Intelligence Technology Tianjin 300072China

Although few-shot learning(FSL)has achieved great progress,it is still an enormous challenge especially when the source and target set are from different domains,which is also known as cross-domain few-shot learning(CD-FSL).Utilizing more source domain data is an effective way to improve the performance of ***,knowledge from different source domains may entangle and confuse with each other,which hurts the performance on the target ***,we propose team-knowledge distllation networks(TKD-Net)to tackle this problem,which explores a strategy to help the cooperation of multiple ***,we distill knowledge from the cooperation of teacher networks to a single student network in a meta-learning *** incorporates task-oriented knowledge distillation and multiple cooperation among teachers to train an efficient student with better generalization ability on unseen ***,our TKD-Net employs both response-based knowledge and relation-based knowledge to transfer more comprehensive and effective *** experimental results on four fine-grained datasets have demonstrated the effectiveness and superiority of our proposed TKD-Net approach.

关键词： cross-domain few-shot learning meta-learning knowledge distillation multiple teachers

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Meter-Scale Thin-Walled Structure with Lattice Infill for Fuel Tank Supporting Component of Satellite:Multiscale Design and Experimental Verification

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Computer Modeling in Engineering & sciences 2024年第1期138卷 201-220页

作者： Xiaoyu Zhang Huizhong Zeng Shaohui Zhang Yan Zhang Mi Xiao Liping Liu Hao Zhou Hongyou Chai Liang Gao Beijing Key Laboratory of Lightweight Multi-Functional Composite Materials and Structures Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System EngineeringChina Academy of Space TechnologyBeijing100094China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of EducationWuhan University of Science and TechnologyWuhan430081China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan430074China

Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich *** structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other *** this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the *** designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding ***-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled *** experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.

关键词： Thin-walled structure lattice infill supporting component selective laser melting satellite

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Power Switching Based on Trajectory Planning and Sliding Mode control for Solid Oxide Fuel Cell Systems

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1968-1979页

作者： Zhen Wang Guoqiang Liu Xingbo Liu Jie Wang Zhiyang Jin Xiaowei Fu Zhuo Wang Bing Jin Zhonghua Deng Xi Li Key Laboratory of Imaging Processing and Intelligent Control of Ministry of Education School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Research Institute of Huazhong University of Science and Technology in Shenzhen ShenzhenChina Hubei Huazhong Electric Power Technology Development Co. Ltd.WuhanChina School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhanChinaand he is also with the Wuhan Huamao Automation Co.Ltd.WuhanChina

To improve the safety of the solid oxide fuel cell(SOFC)systems and avoid the generation of large amounts of pollutants during power switching,this paper designs a power switching strategy based on trajectory planning and sliding mode control(TP-SMC).The design elements of the power switching strategy are proposed through simulation analysis at ***,based on the gas transmission delay time and the change of gas flow obtained from testing,trajectory planning(TP)is *** with other power switching strategies,it has been proven that the power switching strategy based on TP has significantly better control ***-thermore,considering the shortcomings and problems of TP in practical application,this paper introduces sliding mode control(SMC)on the basis of TP to improve the power switching *** final simulation results also prove that the TP-SMC can effectively suppress the impact of uncertainty in gas flow and gas transmission delay *** with TP,TP-SMC can ensure that under uncertain conditions,the SOFC system does not experience fuel starvation and temperature exceeding limit during power ***,the NOx emissions are also within the normal and acceptable *** paper can guide the power switching process of the actual SOFC sys-tems to avoid safety issues and excessive generation of NOx,which is very helpful for improving the performance and ser-vice lifeof the SOFCsystems.

关键词： Solid oxide fuel cell(SOFC)system power switching safety NOx emission trajectory planning sliding mode control

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Performance Evaluation and Transmitter Optimization for AN-Aided FH Communication Against Time Misalignment

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China Communications 2025年第3期22卷 86-103页

作者： Song Changqing Lin Lang Qin Lizhi Xiao Fei Zhao Hongzhi Shao Shihai National Key Laboratory of Wireless Communications University of Electronic Science and Technology of ChinaChengdu 611731China The Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing 100089China The Thirtieth Research Institude of China Electronic Science and Technology Group Corporation Chengdu 610093China

To guarantee secure communication against eavesdropping and malicious attack,an artificial noise(AN)-aided frequency-hopping(FH)architecture is adopted in this *** the inevitable time misalignment between the received signal and locally reconstructed AN will deteriorate the AN cancellation performance,yielding significant secrecy degradation at the FH *** view of this,first,the AN cancellation performance under time misalignment is evaluated via signal to AN-plus-noise ratio,and the system secrecy is analyzed via secrecy ***,to mitigate the performance degradation raised by time misalignment,the transmitting power allocation scheme for AN and confidential signal(CS)is optimized,and the optimal hopping period is ***,the obtained conclusions in both the performance evaluation and transmitter optimization sections hold no matter whether the eavesdropper can realize FH synchronization or *** verify that time misalignment will raise non-negligible performance ***,the power ratio of AN to CS should decrease as time misalignment increases,and for perfect time synchronization,the transmitting power of AN and CS should be *** addition,a longer hopping period is preferred for secrecy enhancement when time misalignment gets exacerbated.

关键词： artificial noise(AN)cancellation hopping period design power allocation time misalignment

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A Memristive Spiking Neural Network Circuit for Bio-Inspired Navigation Based on Spatial Cognitive Mechanisms

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IEEE Transactions on Biomedical Circuits and Systems 2025年第3期19卷 686-698页

作者： Chen, Zhanfei Wang, Xiaoping Wang, Zilu Yang, Chao Huang, Tingwen Lai, Jingang Zeng, Zhigang School of Mechanical Engineering and Automation Shenzhen518055 China Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei Key Laboratory of Brain-Inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Shenzhen University of Advanced Technology School of Computer Science and Control Engineering Shenzhen518055 China

Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking;the action cell module determines the direction of subsequent movement;and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application, the entire SNN is quantized and deployed on a real memristive hardware platform, achieving about a 21 × reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios. © 2007-2012 IEEE.

关键词： Intelligent robots

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Bagging and Boosting Fine-Tuning for Ensemble Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2024年第4期5卷 1728-1742页

作者： Zhao, Changming Peng, Ruimin Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 China and alsowith the ShenzhenHuazhong University of Science and Technology Research Institute Shenzhen 518029 China

Ensemble learning aggregates outputs from multiple base learners for better performance. Bootstrap aggregating (bagging) and boosting are two popular such approaches. They are suitable for integrating unstable base learners with large variance and weak base learners with large bias, respectively, but not base learners with small variance and/or bias, e.g., support vector machine, regularized logistic regression, and ridge regression. This article proposes two novel ensemble-learning-based fine-tuning approaches, boosting fine-tuning (BF) and bagging and boosting fine-tuning (BBF), to fine-tune learners with small variance and/or bias for better performance. BF embeds boosting in a single hidden layer neural network. In each iteration, BF first uses the Newton's method to generate a temporary training set, and then trains a boosting learner on it. BBF combines BF and bagging. It first uses bootstrap to obtain multiple replicas of the training set, and then trains a BF learner on each replica. Extensive experiments on 46 real-world datasets demonstrated that BBF is flexible, robust, and effective, and can fine-tune many popular classifiers to achieve better generalization performance. © 2020 IEEE.

关键词： Support vector machines

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