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Machine learning empowered UAV-based beamforming design in ISAC systems

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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Research on key Techniques for Reverse engineering of Deep Learning Models for x86 Executable Files 24

Research on Key Techniques for Reverse Engineering of Deep L...

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2024 7th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2024

作者： Shi, Meng Lin, Wei Deng, Wenbo College of Cyberspace Security Information Engineering University Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Department of Cyberspace Security Henan Zhengzhou China

ISBN: (纸本)9798400707254

In recent years, with the continuous development of machine learning and deep learning, their related applications have gradually appeared in our field of vision, showing explosive growth. A deep learning compiler optimizes a deep learning model, compiles it, and generates a binary file. When this binary file is placed into an edge device, the device can perform model inference. However, the generated binary file shares characteristics with ordinary executable files, meaning that important information within the binary file could potentially be leaked through disassembly. This paper leverages this critical characteristic by reverse-engineering the binary library files of the model to extract key parameters and thereby restore the model’s framework. Using model library files deployed on edge devices, combined with disassembly, model prediction, dynamic analysis, and other methods, this study successfully recovers the model’s operators, topology, and network hyperparameters. Experimental evaluations demonstrate that the framework of the model can be accurately reconstructed, and the substitute model trained using the original dataset achieves accuracy comparable to that of the original model. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Numerical research on slamming characteristics of aircraft landing on water

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Journal of Hydrodynamics 2023年第1期35卷 171-184页

作者： Dao-sheng Ning Ya-jun Shi Gui-yong Zhang Heng Wang Huan Hu Zhi-fan Zhang State Key Laboratory of Structural Analysis for Industrial Equipment School of Naval Architecture EngineeringDalian University of TechnologyDalian116024China China Ship Scientific Research Center Wuxi200136China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration Shanghai200240China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing100081China

In order to investigate the impact characteristic of an aircraft landing on water,a computational fluid dynamics(CFD)simulation is conducted to explore the slamming characteristics of the NACA TN 2929 A *** flow around the model is solved by using Reynolds-averaged Navier-Stokes equations with the shear stress transport(SST)k—ωturbulence model,based on finite volume method(FVM).The free surface is captured by using the volume of fluid(VOF)method,and the aircraft impact process is realized with help of overset mesh ***,the effects of horizontal and vertical velocities and initial pitch angle on the slamming load,attitude change,impact pressure and flow field evolution are *** results reveal that the horizontal velocity has a considerable influence on whether the aircraft’s horizontal tail hits the water,and further affects the maximum vertical load as well as the maximum pitch angle throughout the impact *** vertical velocity determines the slamming load before the horizontal tail strikes the water,while the horizontal velocity has a significant effect on the load after the horizontal tail hits the water.A smaller initial pitch angle results in not only a heavier slamming load but also a more dramatic change of the posture after the aircraft impacts the *** impact pressure of the aircraft is maximized at the junction of the approaching surface of the fuselage and the free *** some cases,the pressure is also concentrated on the undersurface of the horizontal tail.

关键词： Computational fluid dynamics(CFD) aircraft landing water entry slamming load motion attitude change

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Haica: A High Performance computing & Artificial Intelligence Fused computing Architecture 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Chen, Zhengbo Zheng, Fang Guo, Feng Yu, Qi Chen, Zuoning Information Engineering University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Chinese Academy of Engineering Beijing China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

In recent years, HPC and AI fusion, which refers to applying AI technology to traditional HPC applications, has become a new trend. HPC and AI fusion requires supports for multiple precisions used in both domains. While, supporting multiple precisions in a single computing unit is not easy. Prior research typically targets on modifying a high-precision FMA to support low precisions. However, such efforts suffer from disadvantages of increased latency and limited compute throughput for low-precision operations, high area and power overhead, and limited support of new data formats appeared in AI domain. To address this issue, we propose Haica, a double-precision FMA and low-precision systolic array fused architecture, to achieve HPC and AI fusion. Our work has two innovations. First, we propose a low-cost multiple-low-precision FMA by taking advantage of the commonality among FP16, BF16, and TF32. Second, inspired by the idea of splicing high precision with low precision, we replace the multiply and merge modules in a double-precision FMA with a modified systolic array that is composed of the proposed low-precision FMAs. We implement the logic design of Haica using RTL and evaluate its overhead. Results show that compared to the naive combination of a double-precision FMA and single-half-mixed-precision systolic array, Haica provides extra support for BF16 and TF32 with only 7.87% area and 33.26% power overhead. This demonstrates that Haica achieves HPC and AI fusion in a cost-effective manner. © 2023, Springer Nature Switzerland AG.

关键词： Artificial intelligence

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ERROR ANALYSIS OF FRACTIONAL COLLOCATION METHODS FOR VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS WITH NONCOMPACT OPERATORS

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Journal of Computational Mathematics 2025年第3期43卷 690-707页

作者： Zheng Ma Chengming Huang Anatoly A.Alikhanov School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China North-Caucasus Center for Mathematical Research North-Caucasus Federal UniversityStavropol 355017Russia North-Eastern Federal University Yakutsk 677000Russia

This paper is concerned with the numerical solution of Volterra integro-differential equations with noncompact *** focus is on the problems with weakly singular *** handle the initial weak singularity of the solution,a fractional collocation method is applied.A rigorous hp-version error analysis of the numerical method under a weighted H1-norm is carried *** result shows that the method can achieve high order convergence for such *** experiments are also presented to confirm the effectiveness of the proposed method.

关键词： Volterra integro-differential equation Noncompact operator Nonsmooth solution Collocation method Fractional polynomial hp-version error analysis

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Digital CEOs in Digital Enterprises:Automating,Augmenting,and Parallel in Metaverse/CPSS/TAOs

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 820-823页

作者： Juanjuan Li Rui Qin Sangtian Guan Xiao Xue Peng Zhu Fei-Yue Wang with State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Economics and Management Nanjing University of Science and TechnologyNanjing 210094China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advan... 详细信息

关键词： exceptional Auto advancing

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Model-based Visual 3D Pose Tracking of Non-cooperative Spacecraft in Close Range 74

Model-based Visual 3D Pose Tracking of Non-cooperative Space...

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74th International Astronautical Congress, IAC 2023

作者： Liu, Chang Tang, Hao Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing Shenzhen China

This work proposes a new monocular algorithm to track the 3D pose of non-cooperative spacecraft for proximity operations in real time with the 3D boundaries and 3D contour of the spacecraft as features. The 3D boundary segments of the spacecraft are automatically extracted from the 3D spacecraft model a priori. At the beginning instant of tracking, the tracking system is initialized by a pose retrieval method. At the following time instant, with the initial pose and its covariance provided by the extended Kalman filter (EKF), the 3D contour segments are rapidly extracted from the 3D model. The boundary segments and contour segments are taken as 3D features. In the input image, we search the image data corresponding to the 3D features within the range decided by the initial pose and its covariance. Then, the spacecraft pose is determined in real time by minimizing the geometric distances of the 3D features to the back-projection lines of their corresponding image data. The covariance of the pose is estimated via the first-order optimal condition of the minimization. Eventually, the EKF is derived from the second-order autoregression (2nd-AR) to generate the final pose estimate, and the initial pose and its covariance for next time instant. The synthetic trials quantitatively indicate that our method can track the 3D pose of spacecraft in real time with high accuracy and has outstanding robustness to the dark occlusion, cluster background, large image distance, and image noise. The real trial qualitatively validates the effectiveness of our tracking method. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Kalman filter Pose determination Spacecraft Tracking

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Quantum control for time-dependent noise by inverse geometric optimization

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Science China(Physics,Mechanics & Astronomy) 2024年第9期67卷 51-57页

作者： Xiaodong Yang Yingcheng Li Ran Liu Xinfang Nie Tao Xin Dawei Lu Jun Li Institute of Quantum Precision Measurement State Key Laboratory of Radio Frequency Heterogeneous IntegrationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China State Key Laboratory of Surface Physics Department of PhysicsCenter for Field Theory and Particle Physicsand Institute for Nanoelectronic Devices and Quantum ComputingFudan UniversityShanghai 200433China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area(Guangdong) Shenzhen 518045China

Quantum systems are exceedingly difficult to engineer because they are sensitive to various types of *** particular,timedependent noises are frequently encountered in experiments but how to overcome them remains a challenging *** this work,we propose a flexible robust control technique to resist time-dependent noises based on inverse geometric optimization working in the filter-function *** basic idea is to parameterize the control filter function geometrically and minimize its overlap with the noise spectral *** then effectively reduces the noise susceptibility of the controlled system *** show that the proposed method can produce high-quality robust pulses for realizing desired quantum evolutions under realistic noise ***,we demonstrate this method in examples including dynamical decoupling and quantum sensing protocols to enhance their performances.

关键词： time-dependent noise robust control quantum gates and states dynamical decoupling quantum sensing

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Data quantity governance for machine learning in materials science

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National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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A Novel Approach for state of Health Estimation and Remaining Useful Life Prediction of Supercapacitors Using an Improved Honey Badger Algorithm Assisted Hybrid Neural Network

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Protection and Control of Modern Power Systems 2024年第6期9卷 1-18页

作者： Zhenxiao Yi Shi Wang Zhaoting Li Licheng Wang Kai Wang the School of Electrical Engineering Qingdao UniversityQingdao 266071Chinaand the State Key Laboratory of Reliability and Intelligence of Electrical EquipmentHebei University of TechnologyTianjin 300130China the Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China the School of Engineering Brown UniversityProvidenceRhode Island 02912USA the School of Information Engineering Zhejiang University of TechnologyHangzhou 310014China the School of Electrical Engineering Qingdao UniversityQingdao 266071China

Supercapacitors(SCs)are widely recognized as excellent clean energy storage *** state of health(SOH)estimation and remaining useful life(RUL)prediction are essential for ensuring their safe and reliable *** paper introduces a novel method for SOH estimation and RUL prediction,based on a hybrid neural network optimized by an improved honey badger algorithm(HBA).The method combines the advantages of convolutional neural network(CNN)and bidirectional long-short-term memory(BiLSTM)neural *** HBA optimizes the hyperparameters of the hybrid neural *** CNN automatically extracts deep features from time series data and reduces dimensionality,which are then used as input for the ***,recurrent dropout is introduced in the recurrent layer to reduce overfitting and facilitate the learning *** approach not only improves the accuracy of estimates and forecasts but also significantly reduces data processing *** under different working conditions are used to validate the proposed *** results show that the proposed hybrid model effectively extracts features,enriches local details,and enhances global perception *** proposed hybrid model outperforms single models,reducing the root mean square error to below 1%,and offers higher prediction accuracy and robustness compared to other methods.

关键词： Supercapacitors state of health remaining useful life honey badger algorithm recurrent dropout.

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