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Cross-domain intelligent cooperative spectrum sensing algorithm based on Federated Learning and Swin-Transformer neural network

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Engineering Applications of Artificial Intelligence 2025年 157卷

作者： Lingwei Xu Zhihe Gao Yufang Li T. Aaron Gulliver Department of Information Science and Technology Qingdao University of Science & Technology Qingdao 266061 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing 400065 China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang 441053 Hubei China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(Guangxi Minzu University) Nanning 530006 China Department of Electrical and Computer Engineering University of Victoria Victoria BC V8W 2Y2 Canada

The deployment of 6th Generation (6G) mobile networks will bring revolutionary changes to organizations and users through higher speeds, lower latency, and more intelligent network management. This will require more efficient spectrum sharing to improve network performance. Cognitive Radio (CR) can be employed to help achieve this goal. However, data collection at the fusion center for cooperative spectrum sensing (CSS) creates a risk of sensing user (SU) data leakage. In this paper, an intelligent CSS algorithm for mobile communication is proposed based on Federated Learning with a Swin-Transformer. First, to achieve the cross-domain process, a Continuous Wavelet Transform (CWT) is used with the normalized time series data to obtain a time-frequency spectrum map with joint time-frequency features. Second, Federated Learning is employed to realize distributed CSS to reduce sensing overhead, improve the security of sensing data, and eliminate the SU data isolation with traditional distributed CSS. Then, a new Swin-Transformer spectrum sensing is proposed for fusion learning of the time-frequency spectrum map. The fusion of Federated Learning and Swin-Transformer neural network provides cooperation with multiple SUs to achieve efficient spectrum sensing using the K -rank fusion criterion. Compared with the Convolutional Neural Network (CNN), ViT-Transformer, Graph Neural Network (GNN) and EfficientNet algorithms, the detection probability of the proposed algorithm has increased by 45 %, and the false alarm probability has decreased by 13 %.

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Construction of a CRISPR-based paired-sgRNA library for chromosomal deletion of long non-coding RNAs

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Quantitative Biology 2020年第1期8卷 31-42页

作者： Minzhen Tao Qiaochu Mu Yurui Zhang Zhen Xie MOE Key Laboratory of Bioinformatics and Bioinformatics Division Center for Synthetic and System BiologyDepartment of AutomationBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Zhejiang Tmall Techonogy Co. LtdYuhang DistrictHangzhou 311121China Beiing Syngentech Co..Ltd Zhongguancun Life Science ParkChangping DistrictBeijing 102206China

Background:Derived from an adaptive bacterial immune system,the clustered regularly interspaced palindromic repeats(CRISPR)/CRISPR-associated 9(Cas9)system has shown great potential in high-throughput functional genomic screening,especially for protein-coding ***,it is still challenging to apply the similar strategy to study non-coding genomic elements such as long non-coding RNAs(lncRNAs)or clusters of microRNAs,because short insertions or deletions may not be sufficient to generate loss-of-function ***:Here,we presented a systematic strategy for designing a CRISPR-based paired-sgRNA library for highthroughput screening in non-coding *** to the abundance of lncRNAs and their diverse regulatory roles in vivo,we repurposed microarray datasets to select 600 highly expressed lncRNAs in non-small-cell lung cancer and designed two schemes for lncRNA deletion with^20 paired-sgRNAs for each *** Golden-Gate assembly,we generated a pooled CRISPR-based library with a total of 12,878 sgRNA ***:Over 80%of paired-sgRNAs were recovered from final pooled library with a relarively even *** efficiency of sgRNA pairs was validated through experiments of transient transfection and viral ***,randomly selected paired-sgRNAs showed that efficient deletion of genomic DNA could be achieved with a deletion size within the range of 500 to 3000 ***:In summary,we have demonstrated a strategy to design and construct a pooled paired-sgRNA library to generate genomic deletion in the lncRNA regions,validated their deletion efficiency and explored the relationship of deletion efficiency with respect to deletion *** method would be also suitable for investigation of other uncharacterized non-coding genomic regions in mammalian cells in an efficient and cost-efTective manner.

关键词： CRISPR/Cas9 paired-sgRNA chromosomal deletion non-coding genomic elements lncRNA

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C³DA: A Universal Domain Adaptation Method for Scene Classification From Remote Sensing Imagery

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IEEE Geoscience and Remote Sensing Letters 2024年 21卷

作者： Jiaxu Guo Yushan Lai Jinxiao Zhang Juepeng Zheng Haohuan Fu Lin Gan Liang Hu Gaochao Xu Xilong Che College of Computer Science and Technology Jilin University Changchun China National Supercomputing Center in Wuxi Wuxi China School of Artificial Intelligence Sun Yat-sen University Zhuhai China Department of Earth System Science Tsinghua University Beijing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Ministry of Education Key Laboratory for Earth System Modeling and Department of Earth System Science Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Various remote sensing applications have widely used domain adaptation (DA) methods. Since it does not need to add human interpretation in the target domain, it can be used in cross-region, multitemporal, and multisensor application scenarios. In order to further optimize the design of the loss function and better address the challenges of DA in remote sensing, in this letter, we propose a new universal DA method named C3DA for scene recognition of remote sensing images. It has a comprehensive C3 criterion for recognizing the “unknown” classes by innovatively fusing confidence, consistency, and certainty of samples to make our network training more efficient. We evaluate the performance of our proposed method based on six transfer tasks on three remote sensing datasets. The evaluation results show that our proposed method achieves an average H-score of 58.44%, significantly higher than other SOTA universal DA methods with an average improvement of 2.32%~29.43%. Compared to the baseline ResNet-50, it achieves up to 19.92% improvement, demonstrating that the proposed method outperforms the universal DA scenario. In the future, we also plan to expand the application of this method to more scenarios.

关键词： Remote sensing Entropy Training Feature extraction Deep learning Adversarial machine learning Vectors

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Surrogate representation of sink strengths and the long-term role of crystalline interfaces in the development of irradiation-induced bubbles

arXiv

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arXiv 2020年

作者： Luo, Jing Xin, Yong Zhou, Zhengcheng Zhu, Yichao Guo, Xu Department of Engineering Mechanics Dalian University of Technology Dalian116023 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology China International Research Center for Computational Mechanics Dalian University of Technology China

The present article addresses an early-stage attempt on replacing the analyticity-based sink strength terms in rate equations by surrogate models of machine learning representation. Here we emphasise, in the context of multiscale modelling, a combinative use of machine learning with scale analysis, through which a set of fine-resolution problems of partial differential equations describing the (quasi-steady) short-range individual sink behaviour can be asymptotically sorted out from the mean-field kinetics. Hence the training of machine learning is restrictively oriented, that is, to express the local and already identified, but analytically unavailable nonlinear functional relationships between the sink strengths and other local continuum field quantities. With the trained models, one is enabled to quantitatively investigate the biased effect shown by a void/bubble being a point defect sink, and the results are compared with existing ones over well-studied scenarios. Moreover, the faster diffusive mechanisms on crystalline interfaces are distinguishingly modelled by locally planar rate equations, and their linkages with rate equations for bulk diffusion are formulated through derivative jumps of point defect concentrations across the interfaces. Thus the distinctive role of crystalline interfaces as partial sinks and quick diffusive channels can be investigated. Methodologicalwise, the present treatment is also applicable for studying more complicated situation of long-term sink behaviour observed in irradiated materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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designing of covalent organic framework/2D g-C 3 N 4 heterostructure using a simple method for enhanced photocatalytic hydrogen production

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Journal of Colloid and Interface science 2024年 653卷 1650-1661页

作者： Ahmed E. Hassan Ahmed M. Elewa Mai S.A. Hussien Ahmed F.M. EL-Mahdy Islam M.A. Mekhemer Ibrahim S. Yahia Tarek A. Mohamed Ho-Hsiu Chou Zhenhai Wen CAS Key Laboratory of Design and Assembly of Functional Nanostructures and Fujian Provincial Key Laboratory of Nanomaterials Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou Fujian 350002 China University of Chinese Academy of Sciences Beijing 100049 China Department of Chemistry Faculty of Science Al-Azhar University Nasr City 11884 Cairo Egypt Department of Chemical Engineering National Tsing Hua University Hsinchu 300044 Taiwan Nuclear Chemistry Department Hot Laboratories Center Atomic Energy Authority Cairo 13759 Egypt Nanoscience Laboratory for Environmental and Biomedical Applications (NLEBA) Semiconductor Lab Department of Physics Faculty of Education Ain Shams University Roxy Cairo 11757 Egypt Department of Chemistry Faculty of Education Ain Shams University Roxy Cairo 11757 Egypt Department of Materials and Optoelectronic Science National Sun Yat-Sen University Kaohsiung 80424 Taiwan Laboratory of Nano-Smart Materials for Science and Technology (LNSMST) Department of Physics Faculty of Science King Khalid University P.O. Box 9004 Abha Saudi Arabia Research Center for Advanced Materials Science (RCAMS) King Khalid University Abha 61413 P.O. Box 9004 Saudi Arabia Center of Medical and Bio-Allied Health Sciences Research (CMBHSR) Ajman University Ajman P.O. Box 346 United Arab Emirates

designing heterostructure photocatalysts is a promising approach for developing highly efficient photocatalysts for hydrogen energy production. In this work, we synthesized a series of a covalent organic framework (COF)/g-C 3 N 4 (CN) heterojunction photocatalysts, denoted as x % COF/CN (in which x indicates the weight % of COF and x = 5, 10, 20, 30, 40, 50, 90, 95, 100), for hydrogen production . The COF, which is a key component of the photocatalyst, was prepared by assembling benzothiadiazole (BT) and pyrene (Py) derivatives as building blocks. Integrating COF rods into the two-dimensional (2D) layered g-C 3 N 4 structure significantly improved photocatalytic H 2 production. The hybrid system (30 % COF/CN) displayed an outstanding hydrogen evolution rate (HER) of 27540 ± 805 μmol g −1 h −1 , outperforming most known COFs and g-C 3 N 4 -based photocatalysts, besides exhibiting stable photocatalytic performance. Moreover, the apparent quantum yield (AQY) was 15.5 ± 0.8 % at 420 nm. Experimental techniques and density functional theory (DFT) calculations demonstrated that the 30 % COF/CN heterostructure has broad visible-light absorption, adequate band energy levels, and the best chemical reactivity descriptors compared to the individual components, resulting in effective carrier separation and excellent performance. Our findings offer a valuable strategy for developing highly efficient and stable heterojunction photocatalysts for visible-light‐driven H 2 evolution.

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An iterative method for the solution of equilibrium position in the shaft-hole assembly based on spring contact model 8

An iterative method for the solution of equilibrium position...

引用

8th CIRP Conference of Assembly technology and systems, CATS 2020

作者： Zhang, Yuanyang Guo, Junkang Gao, Xianming Hong, Jun School of Mechanical Engineering Xi'An Jiaotong University Xi'an Shaanxi710049 China Key Laboratory for Modern Design and Rotor-Bearing System Ministry of Education Xi'An Jiaotong University Xi'an Shaanxi710049 China College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Xi'an710021 China

The traditional finite element method has the advantages of strong versatility and high calculation accuracy. Thus it is widely applied in various numerical simulation problems. Although the traditional finite element method for solving the shaft-hole assembly problem can obtain better solution accuracy, the process of modeling and calculation take a long time and thus cannot meet the engineering application requirements when the equilibrium position after the assembly needs to be obtained quickly to guide the subsequent process. In this paper, an iterative method based on the spring contact model is proposed. Firstly, the actual form error of the shaft and hole is characterized by the Fourier series expansion method;Then, in the contact surface, we established a discrete spring contact model and obtained the equivalent stiffness of the coupling spring through the discrete solid model. The balance iterative calculation based on the form errors and contact deformation force can finally determine the equilibrium position after the assembly. Compared with the calculation results of the finite element model, the equilibrium position of the shaft-hole assembly can be quickly solved under the condition of sacrificing certain precision. From this aspect it can be estimated that this method has a good potential for engineering application. © 2020 The Author(s).

关键词： Assembly

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Boron concentration field measurement and mixing analysis in the annular downcomer of reactor pressure vessel 27

Boron concentration field measurement and mixing analysis in...

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27th International Conference on Nuclear Engineering: Nuclear Power Saves the World!, ICONE 2019

作者： Zhang, Qi Tan, Sichao Zhao, Hangbin Wang, Peng Gu, Hanyang Yang, Yunjia School of Nuclear Science and Engineering Shanghai Jiao Tong University Shanghai China Fundamental Science on Nuclear Safety and Simulation Technology Laboratory Harbin Engineer University Harbin Heilongjiang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9784888983051

In nuclear power plant, the reactor annular downcomer acts as the upstream of the reactor core, and boric distribution inside the downcomer is focused in the ECC situation. The relevant study indicates that the inlet coolant does not flow along annular channel directly, and the current multi primary loops layout makes the fluid mixing of the downcomer far more complex. In this paper, boron transport and mixing phenomenon inside the reactor annular downcomer were analyzed by Laser Induced Fluorescence (LIF) method, a reduziertreduced RPV model was built with transparent material, the boric acid was equivalently simulated by rhodamine B. The single loop and double loops flow mixing tests were respectively performed, the qualitative and quantitative data at cross sections were acquired. For the single loop test, the injected tracer transported along the circumferential position and was constantly diluted by the ambient pure water;meanwhile, a portion of the injection entered the lower space and filled up the annular channel in the same manner. From the time evolution of concentration gradient distribution, the increasement of inlet velocity is favorable to the uniform mixing. For the double loops fluid mixing tests, the tracer solution and pure water were injected into the model simultaneously through two independent entrances. The location of the intersection boundary of the two fluids was affected by the entrance momentum ratio, and the tracer distribution fluctuated remarkably in the lower space of the downcomer. Copyright © 2018-2019 by JSME

关键词： Boric acid

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Learning to Infer Weather States Using Partial Observations

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第1期2卷

作者： Jie Chao Baoxiang Pan Quanliang Chen Shangshang Yang Jingnan Wang Congyi Nai Yue Zheng Xichen Li Huiling Yuan Xi Chen Bo Lu Ziniu Xiao National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing China School of Atmospheric Sciences Chengdu University of Information Technology Chengdu China Key Laboratory of Mesoscale Severe Weather Ministry of Education and School of Atmospheric Sciences Nanjing University Nanjing China College of Computer National University of Defense Technology Hunan China Clustertech LTD Hong Kong China National Climate Center China Meteorological Administration Beijing China

Accurate state estimation of the high-dimensional, chaotic Earth's atmosphere marks a Sisyphean task, yet is indispensable for initiating weather forecasts and gauging climate variability. While much effort is devoted to assimilating observations and forecasts to infer weather states, the inherent low-dimensional statistical structure in atmospheric circulation, shaped by geophysical laws and geographic boundaries, is underutilized as an informative prior for state inference, or as reference for assessing representative of existing observations and planning new ones. We realize these potential by learning climatological distribution from climate reanalysis/simulation, using a deep generative model. For a case study of estimating 2 m temperature spatial patterns, the learned distribution faithfully reproduces climatology statistics. A combination of the learned climatological prior with few station observations yields strong posterior of spatial pattern estimates, which are spatially coherent, faithful and adaptive to observational constraints, and uncertainty-aware. This allows us to evaluate each observation's value in reducing pattern estimation uncertainty, and guide optimal observation network design by pinpointing the most informative sites. Our study showcases how generative models can extract and utilize information produced in the chaotic evolution of climate system. Deep generative model enables accurate spatial interpolation of weather variables from sparse observations The generative model produces spatially coherent and uncertainty-aware estimates, effectively constrained by observational data The approach provides a framework to quantify observation impact on uncertainty reduction, enabling optimization of observation networks Accurate estimation of weather conditions across a large area is crucial but challenging due to the complex and chaotic nature of the atmosphere. Traditional methods rely on combining observations with forecasts, which can be comp

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On-edge multi-task transfer learning: Model and practice with data-driven task allocation

arXiv

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arXiv 2021年

作者： Zheng, Zimu Chen, Qiong Hu, Chuang Wang, Dan Liu, Fangming The National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The Edge Cloud Innovation Lab. Technical Innovation Department Cloud BU Huawei Technologies Co. Ltd. Shenzhen China The Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Hong Kong

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation burden to the resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify task importance. We then show that task allocation with task importance for MTL (TATIM) is a variant of NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a Data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study which bridges model and practice via a new architecture and main components design within AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy utilization

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Fluid flow and heat transfer characteristics of flying-wing finned tubes in high-altitude environments

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International Journal of Thermal sciences 2025年 217卷

作者： Long Miao Rui Wan Kai Qiao Xi-Long Zhang Lei Zhang Hua-Wei Wu Tao Sun Lei Liu Zhen Liu School of Mechanical & Electrical Engineering Xuzhou University of Technology Xuzhou 221000 China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang 441053 China School of Automotive and Traffic Engineering Hubei University of Arts and Science Xiangyang 441053 China School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao 266520 China

High-altitude environments significantly influence the performance of flying-wing finned tube heat exchangers (FWFHEs); however, the specific variation patterns and the underlying mechanisms are poorly understood. To address this gap, this study systematically investigates the fluid flow and heat transfer characteristics, as well as the relevant mechanisms, of flying-wing finned tubes (FWFTs) under high-altitude conditions. The performance of the FWFHE was experimentally evaluated under simulated low-temperature ambient conditions corresponding to an altitude of 1000 m. The established simulation methodology was then validated using these experimental results. Following validation, numerical studies were conducted to examine the fluid flow and heat transfer characteristics of FWFTs over an altitude range of 0–5500 m, with inter-fin inlet air velocities from 6 to 12 m/s and Reynolds numbers ( Re ) between 1250 and 3000. The results indicate that, when the inter-fin inlet air velocity is held constant, the pressure drop, heat transfer rate, and convective heat transfer coefficient all decrease significantly with increasing altitude. Specifically, each parameter is reduced by approximately 5.5%, 5%, and 5% for every 500-m increase in altitude, respectively. Despite these dimensional changes, the primary dimensionless parameters—friction factor, corrected Nusselt number, and performance evaluation criterion—remain largely insensitive to altitude under constant Re conditions, indicating stable dimensionless fluid flow and heat transfer characteristics. This stability results from a compensatory mechanism: the increased airflow velocity needed to maintain constant Re counteracts the rise in kinematic viscosity at higher altitudes. To facilitate engineering calculations, altitude correction factors for pressure drop and heat transfer coefficient are proposed. These factors enable accurate prediction and optimization of the heat transfer area design for FWFHEs operating in

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