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Cooperative output regulation of heterogeneous directed multi-agent systems:a fully distributed model-free reinforcement learning framework

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Science China(information Sciences) 2025年第2期68卷 170-185页

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and AutomationHarbin Institute of Technology (Shenzhen) School of Automation Central South University School of Marine Science and Technology Northwestern Polytechnical University School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

关键词： model-free reinforcement learning unknown heterogeneous multi-agent systems fully distributed event-triggered control directed graph

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Fast and Scalable Neural Network Quantum States Method for Molecular Potential Energy Surfaces

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IEEE Transactions on Parallel and Distributed Systems 2025年第7期36卷 1431-1443页

作者： Wu, Yangjun Cao, Wanlu Zhao, Jiacheng Shang, Honghui University of Science and Technology of China State Key Laboratory of Precision and Intelligent Chemistry Hefei230026 China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China

The Neural Network Quantum States (NNQS) method is highly promising for accurately solving the Schrodinger equation, yet it encounters challenges such as com- ¨ putational demands and slow rates of convergence. To address the high computational requirements, we introduce optimizations including cross-sample KV cache sharing technique to enhance sampling efficiency, Quantum Bitwise and BloomHash methods for more efficient local energy computation, and mixed-precision training strategies to boost computational efficiency. To overcome the issue of slow convergence, we propose a parallel training algorithm for NNQS under second quantization to accelerate the training of base models for multi-molecular potential surfaces. Our approach achieves up to 27-fold acceleration specifically in local energy calculations in systems with 154 spin orbitals and demonstrates strong and weak scaling efficiencies of 98% and 97%, respectively, on the H2O2 potential surface training set. The parallelized implementation of transformer-based NNQS is highly portable on various high-performance computing architectures, offering new perspectives on quantum chemistry simulations. © 1990-2012 IEEE.

关键词： Schrodinger equation

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Machine learning empowered UAV-based beamforming design in ISAC systems

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Science China(information Sciences) 2025年第5期68卷 285-286页

作者： Xiangyang DUAN Xiaoqi ZHANG Shuqiang XIA Zhongbin WANG Yihua MA Weijie YUAN State Key Laboratory of Mobile Network and Mobile Multimedia Technology Wireless and Computing Research InstituteZTE Corporation School of Electrical and Data Engineering University of Technology Sydney School of Automation and Intelligent Manufacturing Southern University of Science and Technology

Integrated sensing and communication (ISAC) has been proposed as an enabling technology for the realization of the next-generation wireless system,which focuses on performing wireless communication and sensing *** the various potential ISAC-based applications,unmanned aerial vehicle (UAV)-based ISAC plays a significant part in unlocking the potential of future next-generation wireless communication,facilitating low-latency data transmission in high-mobility *** by recent advancements,a variety of effective techniques have been investigated to optimize beamforming design in ISAC *** instance,the authors in [1] introduced an extended Kalman filtering (EKF)-based method tailored for millimeter wave (mmWave) ISAC ***,Ref.[2]proposed an extended interacting multiple model (IMM)-EKF framework designed for vehicular networks with intricate roadway *** these advancements,the aforementioned methods typically employ a separate scheme for channel prediction and beam alignment,which introduces additional signaling overhead in real-world ***,there is a demand for an end-to-end beamforming design approach specifically for UAV-based ISAC systems.

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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Science China(information Sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of Computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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A CNN-SVR model for NO_(2)profile prediction based on MAX-DOAS observations:The influence of Chinese New Year overlapping the 2020 COVID-19 lockdown on vertical distributions of tropospheric NO_(2)in Nanjing,China

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Journal of Environmental Sciences 2024年第7期141卷 151-165页

作者： Xin Tian Zijie Wang Pinhua Xie Jin Xu Ang Li Yifeng Pan Feng Hu Zhaokun Hu Mingsheng Chen Jiangyi Zheng Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China Key laboratory of Environmental Optical and Technology Anhui Institute of optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China School of Environmental Science and Optoelectronic Technology University of Science and Technology of ChinaHefei 230026China

In this study,a hybrid model,the convolutional neural network-support vector regression model,was adopted to achieve prediction of the NO_(2)profile in Nanjing from January 2019to March *** the sudden decline in NO_(2)in February 2020,the contribution of the Coronavirus Disease-19(COVID-19)lockdown,Chinese New Year(CNY),and meteorologi cal conditions to the reduction of NO_(2)was ***_(2)vertical column densities(VCDs) from January to March 2020 decreased by 59.05%and 32.81%,relative to the same period in 2019 and 2021,*** the period of 2020 COVID-19,the average NO_(2)VCDs were 50.50%and 29.96%lower than those during the pre-lockdown and post-lockdown pe riods,*** NO_(2)volume mixing ratios(VMRs)during the 2020 COVID-19 lock down significantly decreased below 400 *** NO_(2)VMRs under the different wind fields were significantly lower during the lockdown period than during the pre-lockdown period This phenomenon could be attributed to the 2020 COVID-19 *** NO_(2)VMRs be fore and after the CNY were significantly lower in 2020 than in 2019 and 2021 in the same period,which further proves that the decrease in NO_(2)in February 2020 was attributed to the COVID-19 *** source analysis of an NO_(2)pollution episode during the lockdown period showed that the polluted air mass in the Beijing-Tianjin-Hebei was trans ported southwards under the action of the north wind,and the subsequent unfavorable meteorological conditions(local wind speed of<2.0 m/sec)resulted in the accumulation o pollutants.

关键词： MAX-DOAS CNN-SVR Nitrogen dioxide COVID-19 Chinese New Year Regional transport

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Competition on robust deep learning

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National Science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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Study of the 6.x nm short wavelength radiation spectra of laser-produced erbium plasmas for BEUV lithography

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Plasma Science and technology 2023年第6期25卷 41-51页

作者：吴涛王倩陈柳安陆培祥 Hubei Key Laboratory of Optical Information and Pattern Recognition School of Optical Information and Energy EngineeringWuhan Institute of TechnologyWuhan 430205People's Republic of China Guangdong Intelligent Robotics Institute Dongguan 523808People's Republic of China

Beyond extreme ultraviolet(BEUV)radiation with a wavelength of 6.x nm for lithography is responsible for reducing the source wavelength to enable continued miniaturization of semiconductor *** this work,the Required BEUV light at 6.x nm wavelength was generated in dense and hot Nd:YAG laser-produced Er *** spectral contributions from the 4p–4d and 4d–4f transitions of singly,doubly and triply excited states of Er XXIV–Er XXXII in the BEUV band were calculated using Cowan and the Flexible Atomic *** was also found that the radiative transitions between multiply excited states dominate the narrow wavelength window around 6.x *** the assumption of collisional radiative equilibrium of the laser-produced Er plasmas,the relative ion abundance in the experiment was *** the Boltzmann quantum state energy level distribution and Gram–Charlier fitting function of unresolved transition arrays(UTAs),the synthetic spectrum around 6.x nm was finally obtained and compared with the experimental *** spatio-temporal distributions of electron density and electron temperature were calculated based on radiation hydrodynamic simulation in order to identify the contributions of various ionic states to the UTAs arising from the Er plasmas near 6.x nm.

关键词： laser-produced plasmas unresolved transition array collisional radiative equilibrium

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An Entity Alignment Algorithm Based on Ontology Enhancement and Multi-Feature Fusion 6

An Entity Alignment Algorithm Based on Ontology Enhancement ...

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6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Song, Hao Lei, Yuxia Zi, Kangli Quan, Fuyuan Zhang, Tianhang Li, Qi School of Computer Science Qufu Normal University Shandong Rizhao China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Entity alignment plays a crucial role in the integration of knowledge graphs. However, current knowledge graphs often suffer from inconsistent construction standards, structural heterogeneity, and imprecise semantic representation of entities, which can lead to suboptimal embedding of structural information. Moreover, most existing entity alignment methods rely heavily on structural information, which limits alignment efficiency. To address these challenges, this paper presents Onto-IMF, an ontology-enhanced entity alignment method based on multi-feature fusion. This approach aims to enhance the semantic representation of entities by incorporating ontology information, thereby reducing structural discrepancies among heterogeneous graphs and improving the embedding of structural information. The multi-feature fusion entity alignment leverages diverse semantic features, including relations, attributes, and entity names, to achieve more accurate alignment. Experimental results on various DBP15K subsets indicate that the proposed method achieves Hits@l scores of 86.6%, 93.0%, and 97.7%, respectively, representing an average improvement of 7.9% over the best-performing baseline models. © 2024 IEEE.

关键词： Knowledge graph

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TransCGAN-based Parameter Extraction Framework for SAR Image Simulation 2

TransCGAN-based Parameter Extraction Framework for SAR Image...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Deng, Sidan He, Jingfei Mao, Yongfei Zhao, Liangbo Chen, Liang Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Chongqing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images have a wide range of applications due to their all-weather and all-day working conditions. However, SAR images with different scenarios and imaging conditions are insufficient or even rare, which is required in specific SAR image tasks. Fortunately, SAR image simulation technology can provide ample simulated images under these conditions at a low cost, addressing the scarcity of specific real SAR data. Accurate parameters are crucial for obtaining high-quality simulated images. However, it is time-consuming and labour-intensive to adjust parameters manually, and it often fails to achieve accurate simulation parameters. To tackle this problem, this paper proposes a TransCGAN-based SAR image simulation method that combines deep learning with traditional methods. By training TransCGAN, a conditional generative adversarial network (CGAN) integrated with Transformer architecture, a mapping between SAR images and simulation parameters is established. This enables the extraction of simulation parameters directly from the real SAR image, guided by the corresponding real SAR image. Ultimately, the parameters are subsequently converted into simulated SAR images via simulation software. Experimental results demonstrate that our TransCGAN-based method can effectively extract accurate simulation parameters from real SAR images, resulting in simulated images holding high similarity to real images. © 2024 IEEE.

关键词： Generative adversarial networks

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