检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Zhao, Y. Liu, J.C. Xiong, S.L. Xue, W.C. Yi, Q.B. Lu, G.P. Xu, W. Lyu, F.C. Sun, J.C. Peng, W.X. Zheng, C. Zhang, Y.Q. Cai, C. Xiao, S. Xie, S.L. Wang, C.W. Tan, W.J. An, Z.H. Chen, G. Du, Y.Q. Huang, Y. Gao, M. Gong, K. Guo, D.Y. He, J.J. Li, B. Li, G. Li, X.Q. Li, X.B. Liao, J.Y. Liang, J. Liang, X.H. Liu, Y.Q. Ma, X. Qiao, R. Song, L.M. Song, X.Y. Sun, X.L. Wang, J. Wang, J.Z. Wang, P. Wen, X.Y. Wu, H. Xu, Y.B. Yang, S. Zhang, B.X. Zhang, D.L. Zhang, F. Zhang, P. Zhang, H.M. Zhang, Z. Zhao, X.Y. Zheng, S.J. Zhang, K.K. Han, X.B. Wu, H.Y. Hu, T. Geng, H. Zhang, H.B. Lu, F.J. Zhang, S.N. Yu, H. Department of Astronomy Beijing Normal University Beijing Beijing100875 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing Beijing100049 China University of Chinese Academy of Sciences Beijing Beijing100049 China School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China School of Earth and Space Sciences University of Science and Technology of China Anhui Hefei230026 China Electronic Information School Wuhan University Hubei Wuhan430072 China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric Sciences Jiangsu Nanjing210000 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University GuiZhou Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University GuiZhou Guiyang550001 China Institute of Astrophysics Central China Normal University HuBei Wuhan430079 China School of Computing and Artificial Intelligence Southwest Jiaotong University SiChuan Chengdu611756 China College of Electronic and Information Engineering Tongji University Shanghai Shanghai201804 China Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai Shanghai201304 China National Space Science Center Chinese Academy of Sciences Beijing Beijing100190 China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing Beijing100029 China

Gravitational-wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a space-borne instrument dedicated to monitoring high-energy transients, including Terrestrial Gamma-ray Flashes (TGFs) and Terrestrial Electron Beams (TEBs). We implemented a TGF/TEB search algorithm for GECAM, with which 147 bright TGFs, 2 typical TEBs and 2 special TEB-like events are identified during an effective observation time of ∼9 months. We show that, with gamma-ray and charged particle detectors, GECAM can effectively identify and distinguish TGFs and TEBs, and measure their temporal and spectral properties in detail. A very high TGF-lightning association rate of ∼80% is obtained between GECAM and GLD360 in east Asia region. Copyright © 2023, The Authors. All rights reserved.

关键词： Electron beams

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Strategy evolution on dynamic networks

arXiv

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arXiv 2023年

作者： Su, Qi McAvoy, Alex Plotkin, Joshua B. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Biology University of Pennsylvania PhiladelphiaPA19104 United States Center for Mathematical Biology University of Pennsylvania PhiladelphiaPA19104 United States

Models of strategy evolution on static networks help us understand how population structure can promote the spread of traits like cooperation. One key mechanism is the formation of altruistic spatial clusters, where neighbors of a cooperative individual are likely to reciprocate, which protects prosocial traits from exploitation. But most real-world interactions are ephemeral and subject to exogenous restructuring, so that social networks change over time. Strategic behavior on dynamic networks is difficult to study, and much less is known about the resulting evolutionary dynamics. Here, we provide an analytical treatment of cooperation on dynamic networks, allowing for arbitrary spatial and temporal heterogeneity. We show that transitions among a large class of network structures can favor the spread of cooperation, even if each individual social network would inhibit cooperation when static. Furthermore, we show that spatial heterogeneity tends to inhibit cooperation, whereas temporal heterogeneity tends to promote it. Dynamic networks can have profound effects on the evolution of prosocial traits, even when individuals have no agency over network structures. © 2023, CC BY.

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Erratum to: CA-DTS: A Distributed and Collaborative Task Scheduling Algorithm for Edge computing Enabled intelligent Road Network

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Journal of Computer science and Technology 2023年第6期38卷 1451-1451页

作者： Hu, Shi-Hong Luo, Qu-Yuan Li, Guang-Hui Shi, Weisong Ye, Bao-Liu Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China School of Information Science and Technology Southwest Jiaotong University Chengdu China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Department of Computer and Information Sciences University of Delaware Newark USA National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

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The X-ray high-energy cutoff in Compact Symmetric Object Mrk 348

arXiv

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arXiv 2024年

作者： Liao, Mai Wang, Junxian Kang, Jialai Li, Xiaofeng Zhou, Minhua National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing100101 China Chinese Academy of Sciences South America Center for Astronomy National Astronomical Observatories CAS Beijing100101 China Instituto de Estudios Astrofísicos Facultad de Ingeniería y Ciencias Universidad Diego Portales Av. Ejército 441 Santiago Chile CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Anhui Hefei230026 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China Department of Astronomy Guangzhou University Guangzhou510006 China Great Bay Center National Astronomical Data Center Guangdong Guangzhou510006 China Astronomy Science and Technology Research Laboratory Department of Education of Guangdong Province Guangzhou510006 China School of Physical Science and Intelligent Education Shangrao Normal University 401 Zhimin Road Shangrao334001 China

Compact radio AGN are thought to be young radio active galactic nuclei (AGN) at the early stage of AGN evolution, thus are ideal laboratory to study the high-energy emission throughout the evolution of radio AGN. In this work, we report for the first time the detection of the high-energy cutoff (Ecut), a direct indicator of thermal coronal radiation, of X-ray emission in Mrk 348 (z = 0.015), a young radio galaxy classified as compact symmetric object. With a 100 ks NuSTAR exposure, we find that the high-energy cutoff (Ecut ) is firmly detected (218−62+124 keV). Fitting with various Comptonization models indicates the presence of a hot corona with temperature kTe = 35 – 40 keV. These strongly support the corona origin for its hard X-ray emission. The comparison in the Ecut – spectra index Γ plot of Mrk 348 with normal large-scale radio galaxies (mostly FR II) yields no difference between them. This suggests the corona properties in radio sources may not evolve over time (i.e., from the infant stage to mature stage), which is to-be-confirmed with future sample studies of young radio AGN. Copyright © 2024, The Authors. All rights reserved.

关键词： Active Galactic Nuclei

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Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge computing Environment

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ACM Transactions on Sensor Networks 1000年

作者： Yongzhe Jia Bowen Liu Xuyun Zhang Fei Dai Arif Khan Lianyong Qi Wanchun Dou State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland College of Computer Science and Technology China University of Petroleum East China - Qingdao Campus Qingdao China

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.

关键词： Federated learning split learning model pruning edge computing

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Cascade control for supply air temperature in a variable air volume system

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IOP Conference Series: Earth and Environmental science 2019年第1期238卷

作者： Junhua Zhuang Yimin Chen Jie Wu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China Department of Civil Architectural and Environmental Engineering Drexel University Philadelphia PA 19104 USA Beijing ZXT M&E Engineering Consultant Co. Ltd

Most supply air temperature in variable air volume systems is typically set at 55F(13 °C) that is design value in these systems. However, sometimes the load of cooling varies greatly in different zones, and it means the indoor temperature of low-load zones is lower than comfortable value, even if the supply air damper is at the minimum value that is not closed for minimum outside air requirement. Meanwhile, in transition season, the indoor temperature is also lower due to low outdoor temperature. These factors provided an opportunity to apply higher supply air temperature to reduce air handling unit (AHU) cooling load and box reheating. In this paper, a cascade control method is used to raise the supply air temperature. The external control loop controls the setting value of supply air temperature with the minimum supply air volume as the control target. And the internal control loop controls the supply air temperature by regulating valve. The cascade structure can solve the problem that the time constant of the system is too large. The simulation results show that the quality of control is good and the reheating can be cancelled.

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A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

arXiv

引用

arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks

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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

引用

Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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A Real-Time Interpretable Ai Model for the Cholangioscopic Diagnosis of Malignant Biliary Stricture

SSRN

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SSRN 2022年

作者： Zhang, Xiang Tang, Dehua Zhou, Jindong Ni, Muhan Yan, Peng Zhang, Zhenyu Yu, Tao Zhan, Qiang Shen, Yonghua Zhou, Lin Zheng, Ruhua Zou, Xiaoping Zhang, Bin Li, Wu-Jun Wang, Lei Department of Gastroenterology Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University Nanjing Medical University Jiangsu Nanjing210008 China Department of Gastroenterology Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Jiangsu Nanjing210008 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210008 China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Jiangsu Nanjing210008 China Departments of Gastroenterology Qilu Hospital of Shandong University Shandong Jinan China Department of Gastroenterology Wuxi People’s Hospital Affiliated to Nanjing Medical University Jiangsu Wuxi214023 China Department of Gastroenterology Taikang Xianlin Drum Tower Hospital Jiangsu Nanjing210046 China Center for Medical Big Data Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Jiangsu Nanjing210008 China Department of Gastroenterology Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University 321 Zhongshan Road Jiangsu Nanjing210008 China

Background: It is crucial to accurately determine malignant biliary strictures (MBSs) for early curative treatment. Therefore, an effective and accessible modality is needed to facilitate the diagnosis of MBSs. The study aimed to develop a real-time interpretable artificial intelligent (AI) system to predict MBSs under digital single-operator cholangioscopy (DSOC).Methods: A novel interpretable AI system called MBSDeiT was developed, consisting of two models to identify qualified images and then predict MBS in real time. The overall efficiency of MBSDeiT was validated at the image level on internal, external, prospective testing datasets and subgroups analyses, and at the video level on the prospective test datasets, and compared with that of endoscopists. The association between AI predictions and endoscopic features was evaluated to increase the ***: MBSDeiT can first automatically select qualified DSOC images with an AUC of 0.904 and 0.921–0.927 on the internal testing dataset and the external testing datasets, and then identify MBSs with an AUC of 0.971 on the internal testing dataset, an AUC of 0.978–0.999 on the external testing datasets, and an AUC of 0.976 on the prospective testing dataset, respectively. MBSDeiT accurately identified 92.3% MBS in prospective testing videos. Subgroups analyses confirmed the stability and robustness of MBSDeiT. MBSDeiT achieved superior performance to that of expert and novice endoscopists. The AI predictions were significantly associated with four endoscopic features [including mass (OR 25.17, 95% CI 8.73–72.61);friability (70.70, 16.89–296.03);raised intraductal lesion (10.94, 5.68–21.04);and abnormal vessels (103.08, 13.94–762.09)] under DSOC, which is consistent with the endoscopists’ ***: The findings suggest that MBSDeiT could be a promising approach for the accurate diagnosis of MBS under *** Information: The project is funded by Jiangsu Provincial Health Commission

关键词： Artificial intelligence

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Uplink Precoding Design for Cell-Free Massive MIMO with Iteratively Weighted MMSE

arXiv

引用

arXiv 2023年

作者： Wang, Zhe Zhang, Jiayi Ngo, Hien Quoc Ai, Bo Debbah, Mérouane The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The Institute of Electronics Communications and Information Technology Queen’s University BelfastBT3 9DT United Kingdom The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China The Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518066 China The Technology Innovation Institute Abu Dhabi United Arab Emirates CentraleSupélec University Paris-Saclay Gif-sur-Yvette91192 France

In this paper, we investigate a cell-free massive multiple-input multiple-output system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) for the fully centralized processing scheme and large-scale fading decoding (LSFD) scheme. To further improve the SE performance, we design the uplink precoding schemes based on the weighted sum SE maximization. Since the weighted sum SE maximization problem is not jointly over all optimization variables, two efficient uplink precoding schemes based on Iteratively Weighted sum-Minimum Mean Square Error (I-WMMSE) algorithms, which rely on the iterative minimization of weighted MSE, are proposed for two processing schemes investigated. Furthermore, with maximum ratio combining applied in the LSFD scheme, we derive novel closed-form achievable SE expressions and optimal precoding schemes. Numerical results validate the proposed results and show that the I-WMMSE precoding schemes can achieve excellent sum SE performance with a large number of UE antennas. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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