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Ru-Ni alloy nanosheets as tandem catalysts for electrochemical reduction of nitrate to ammonia

Nano Research 2024年第6期17卷 4815-4824页

作者： Xingchao You Jiawei Xu Zechao Zhuang Junkai Xia Suwen Wang Haiyan Wei Yongfu Li Yanjiang Cai Hai Xiang Bing Yu State Key Laboratory of Subtropical Silviculture Zhejiang A&F UniversityHangzhou 311300China Ecological-Environment&Health College(EEHC) Zhejiang A&F UniversityHangzhou 311300China Jiangsu Key Laboratory of Numerical Simulation of Large Scale Complex Systems and School of Chemistry and Materials Science Nanjing Normal UniversityNanjing 210023China Department of Chemistry Tsinghua UniversityBeijing 100084China Department of Chemical Engineering Columbia UniversityNew YorkNY 10027USA

Developing electrocatalysts that exhibit both high activity and ammonia selectivity for nitrate reduction is a significant and demanding challenge,primarily due to the complex nature of the multiple-electron reduction process *** encouraging approach involves coupling highly active precious metals with transition metals to enhance catalytic performance through ***,we report a ruthenium-nickel alloy catalyst with nanosheets(Ru-Ni NSs)structure that achieves a remarkable ammonia Faradaic efficiency of approximately 95.93%,alongside a yield rate of up to 6.11 g·h^(−1)·cm^(−2).Moreover,the prepared Ru-Ni NSs exhibit exceptional stability during continuous nitrate reduction in a flow reactor for 100 h,maintaining a Faradaic efficiency of approximately 90%and an ammonia yield of 37.4 mg·L^(−1)·h^(−1)using 0.05 M nitrate alkaline *** studies reveal that the catalytic process follows a two-step pathway,in which HONO serves as a migration *** presence of a partially oxidized Ru(002)surface enhances the adsorption of nitrate and facilitates the release of the migration intermediate by adjusting the strength of the electrostatic and covalent interactions between the adsorbate and the surface,*** the other hand,the Ni(111)surface promotes the utilization of the migration intermediate and requires less energy for NH_(3)*** tandem process contributes to a high catalytic activity of Ru-Ni NSs towards nitrate reduction.

关键词： alloy catalyst nitrate reduction ammonia synthesis Faradaic efficiency

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A Grey Wolf Optimizer Variant with Weighted-leader Strategy 24

A Grey Wolf Optimizer Variant with Weighted-leader Strategy

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3rd International Conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Liang, Yajun Yan, Su Ma, Qian Huang, Shunlun Liu, Xin China Aerodynamics Research & Development Center Sichuan Mianyang China Science and Technology on Complex Aviation Systems Simulation Laboratory Beijing China State Key Laboratory of Air Traffic Management System Beijing China Beijing Aviation Meteorological Institute Beijing China

ISBN: (纸本)9798400710834

To enhance the convergence capability of grey wolf optimizer (GWO), this research investigates an evolved GWO using weighted-leader strategy (WLS), namely WLSGWO. The key issue of WLS is realizing the adaptive adjustment of position weighting according to the control parameters, thus well overcoming the drawback of premature convergence in the basic GWO. The newly developed method’s performance is assessed through experiments conducted on a standardized test suite. Additionally, comparisons are made by utilizing GWO alongside other advanced optimization techniques. The results of non-parametric studies validate the outperformance of the WLSGWO, thus proving the efficiency of the proposed strategy. © 2024 Copyright held by the owner/author(s).

关键词： Swarm intelligence

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ISAR Image-based Spatial Target Recognition 3

ISAR Image-based Spatial Target Recognition

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3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Zhou, Yongjing Zhu, Weigang He, Yonghua Li, Yonggang University of Aerospace Engineering Department of Electronics and Optics Beijing China Key Laboratory of Complex Electronic Systems Simulation Beijing China Graduate School of Aerospace Engineering Beijing China

ISBN: (纸本)9781665490788

With the increasing demand for space situational awareness and space environment monitoring, space target discovery, tracking, identification and classification technologies have been developed accordingly. At present, the integrated development of machine learning, artificial intelligence and other multidisciplinary disciplines has greatly promoted the progress of space target detection and identification technology. The inverse synthetic aperture radar image can reflect the fine structural features of space targets, which is important for feature extraction and recognition and classification of space targets. In this paper, three aspects of target recognition technologies based on RCS, HRRP and ISAR imaging are reviewed respectively, and the target recognition methods based on ISAR images are mainly discussed and validated. First, the basic problems and common methods of current RCS, HRRP, and ISAR image target recognition are introduced;then, a summary is made for ISAR image target recognition techniques;finally, the still existing problems of current ISAR image target recognition and the next development prospects are discussed. © 2023 IEEE.

关键词： Inverse synthetic aperture radar

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Mask-guided Multi-scale Spatial-Spectral Transformer for Snapshot Compressive Imaging

Mask-guided Multi-scale Spatial-Spectral Transformer for Sna...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yin, Heyuan Yang, Jingxiang Liu, Jia Xiao, Liang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Analysis of Mathematical Theory and Modeling of Complex Systems Ministry of Industry and Information Technology China

ISBN: (纸本)9798350368741

Effectively reconstructing 3D hyperspectral images (HSIs) from 2D measurements presents a significant challenge in Coded Aperture Snapshot Spectral Imaging (CASSI) systems. While recent transformers exhibit potential in HSI reconstruction, they often suffer from inadequate exploration of multi-scale spatial-spectral self-similarity, leading to mean effects and information loss. Additionally, these methods struggle with insufficient modeling of the degradation inherent in the compressive imaging process. To address these issues, we propose a novel Mask-guided Multi-scale Spatial-Spectral Transformer (MMSST). Specifically, we introduce a Degradation Aware Mask Attention (DAMA) module to incorporate degradation information of the compressive imaging process. Furthermore, MMSST leverages Local-Regional SpAtial attention (LRSA) and Global-Regional SpEctral attention (GRSE) to effectively exploit multi-scale self-similarity across spatial and spectral dimensions. Extensive experimental results demonstrate the effectiveness of our MMSST. © 2025 IEEE.

关键词： hyperspectral reconstruction Snapshot Compressive Imaging spatial-spectral transformer

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A Novel Class of Energy-Preserving Runge-Kutta Methods for the Korteweg-de Vries Equation

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Numerical Mathematics(Theory,Methods and Applications) 2022年第3期15卷 768-792页

作者： Yue Chen Yuezheng Gong Qi Hong Chunwu Wang Department of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) MIITNanjing 211106China Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems Nanjing 210023China

In this paper,we present a quadratic auxiliary variable approach to develop a new class of energy-preserving Runge-Kutta methods for the Korteweg-de Vries *** quadratic auxiliary variable approach is first proposed to reformulate the original model into an equivalent system,which transforms the energy conservation law of the Korteweg-de Vries equation into two quadratic invariants of the reformulated *** the symplectic Runge-Kutta methods are directly employed for the reformulated model to arrive at a new kind of time semi-discrete schemes for the original *** consistent initial conditions,the proposed methods are rigorously proved to maintain the original energy conservation law of the Korteweg-de Vries *** addition,the Fourier pseudo-spectral method is used for spatial discretization,resulting in fully discrete energy-preserving *** implement the proposed methods effectively,we present a very efficient iterative technique,which not only greatly saves the calculation cost,but also achieves the purpose of practically preserving *** numerical results are addressed to confirm the expected order of accuracy,conservative property and efficiency of the proposed algorithms.

关键词： Quadratic auxiliary variable approach symplectic Runge-Kutta scheme energypreserving algorithm Fourier pseudo-spectral method

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Demo: A multi-agent communication reinforcement learning Cooperative Detection System 23

Demo: A multi-agent communication reinforcement learning Coo...

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Proceedings of the Eighth ACM/IEEE Symposium on Edge Computing

作者： Hongye Gao Yichen Shi Weixiong Rao Qiongmin Ma Tongji University Shanghai China National Key Laboratory for Complex Systems Simulation Beijing China

ISBN: (纸本)9798400701238

Communication is an effective mechanism to coordinate the behavior of mobile multi-agents. We propose a general multi-agent communication framework. With help of graph neural networks and reinforcement learning, this framework can provide a cooperative detection method with more powerful capabilities. In this paper, we demonstrate the configurable co-detection simulation system. Moreover, the evaluation of the multi-agent communication framework validates that the agents can effectively cooperate with each other through decentralized communication, and effectively reduce computation overhead meanwhile with high scalability.

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Relationship graph learning network for visual relationship detection 2

Relationship graph learning network for visual relationship ...

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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Li, Yanan Yu, Jun Zhan, Yibing Chen, Zhi Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781450383080

Visual relationship detection aims to predict the relationships between detected object pairs. It is well believed that the correlations between image components (i.e., objects and relationships between objects) are significant considerations when predicting objects' relationships. However, most current visual relationship detection methods only exploited the correlations among objects, and the correlations among objects' relationships remained underexplored. This paper proposes a relationship graph learning network (RGLN) to explore the correlations among objects' relationships for visual relationship detection. Specifically, RGLN obtains image objects using an object detector, and then, every pair of objects constitutes a relationship proposal. All relationship proposals construct a relationship graph, in which the proposals are treated as nodes. Accordingly, RGLN designs bi-stream graph attention subnetworks to detect relationship proposals, in which one graph attention subnetwork analyzes correlations among relationships based on visual and spatial information, and the other analyzes correlations based on semantic and spatial information. Besides, RGLN exploits a relationship selection subnetwork to ignore redundant information of object pairs with no relationships. We conduct extensive experiments on two public datasets: the VRD and the VG datasets. The experimental results compared with the state-of-the-art demonstrate the competitiveness of RGLN. © 2021 ACM.

关键词： Semantics

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Secure short-packet communications using a full-duplex receiver

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International Journal of Intelligent Networks 2023年第1期4卷 349-354页

作者： Chen, Dechuan Li, Jin Hu, Jianwei Zhang, Xingang Zhang, Shuai College of Physics and Electronic Engineering Nanyang Normal University Nanyang473061 China Ministry of Education Nanjing210003 China National Key Laboratory for Complex Systems Simulation Beijing100101 China College of Computer Science and Technology Nanyang Normal University Nanyang473061 China

In this work, we consider the physical layer security in short-packet communications, where a full-duplex (FD) receiver receives information signals from a source while generating artificial noise (AN) to confuse an eavesdropper. Taking into account the finite blocklength coding and the self-interference (SI) in FD mode, we derive new approximation closed-form expression for the secrecy throughput. Moreover, we analyze the asymptotic secrecy throughput in two distinct scenarios, i.e., high signal-to-noise ratio (SNR) regime and infinite blocklength coding, to gain more insights. Our examination illustrates the correctness of our expressions and shows how the critical system variables affect the secrecy throughput. © 2023 The Authors

关键词： Finite difference method

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Arbitrarily High-Order Energy-Preserving Schemes for the Camassa-Holm Equation Based on the Quadratic Auxiliary Variable Approach

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Advances in Applied Mathematics and Mechanics 2023年第5期15卷 1233-1255页

作者： Yuezheng Gong Qi Hong Chunwu Wang Yushun Wang School of Mathematics Nanjing University of Aeronautics and AstronauticsNanjingJiangsu 211106China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) MIITNanjingJiangsu 211106China Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems School of Mathematical SciencesNanjing Normal UniversityNanjingJiangsu 210023China

In this paper,we present a quadratic auxiliary variable(QAV)technique to develop a novel class of arbitrarily high-order energy-preserving algorithms for the Camassa-Holm *** QAV approach is first utilized to transform the original equation into a reformulated QAV system with a consistent initial *** the reformulated QAV system is discretized by applying the Fourier pseudo-spectral method in space and the symplectic Runge-Kutta methods in time,which arrives at a class of fully discrete *** the consistent initial condition,they can be rewritten as a new fully discrete system by eliminating the introduced auxiliary variable,which is rigorously proved to be energy-preserving and *** numerical experiments are conducted to confirm the expected order of accuracy,conservative property and efficiency of the proposed *** presented numerical strategy makes it possible to directly apply a special class of Runge-Kutta methods to develop energy-preserving algorithms for a general conservative system with any polynomial energy.

关键词： Camassa-Holm equation quadratic auxiliary variable high-order energy-preserving schemes symplectic Runge-Kutta methods

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Autonomous Decision Making with Reinforcement Learning in Multi-UAV Air Combat

Autonomous Decision Making with Reinforcement Learning in Mu...

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IEEE International Conference on systems, Man and Cybernetics

作者： Xutao Feng Yaofei Ma Liping Zhao Hanbo Yang Beihang University School of Automation Science and Electrical Engineering Beijing China National Key Laboratory of Modeling and Simulation for Complex Systems Beijing Simulation Center Beijing China

A multi-agent decision network based on QMIX is proposed in this paper to cope with the coordination decision problem of multiple UAV air combat missions. To speed up the training process, three improvements are introduced: 1) An improved $\epsilon$ -decaying method that enable some tutor to help in action selection at the early stage of the training. This measure greatly improves the exploring efficiency when the network are far from being fully trained; 2) State pruning and action mask measures are applied during the training. The former improves the effectiveness of the input state information, and the latter reduces unnecessary action exploring. 3) A gradually training configuration is used to make the training process more robust, where the combat adversaries are configured as the static targets, the randomly maneuver vehicles, and the Min-Max strategy vehicles respectively. The multi-UAV air combat scenarios are built up and the experiments are conducted. The results shows that these improvements have significantly improved training efficiency.

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