检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Kim, Jinuk Jang, Wonjin Kim, Jin Hwi Lee, Jiwan Cho, Kyung Hwa Lee, Yong-Gu Chon, Kangmin Park, Sanghyun Pyo, JongCheol Park, Yongeun Kim, Seongjoon School of Civil and Environmental Engineering Konkuk University Seoul05029 Korea Republic of School of Urban and Environmental Engineering Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Department of Environmental Engineering Kangwon National University Gangwon-do 24341 Korea Republic of Department of Integrated Energy and Infra System Kangwon National University Gangwon-do 24341 Korea Republic of Water Quality Assessment Research Division National Institute of Environmental Research Environmental Research Complex Incheon22689 Korea Republic of Center for Environmental Data Strategy Korea Environment Institute Sejong30147 Korea Republic of

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. However, to effectively retrieve CDOM, the wavelength of the absorption coefficient and input wavelength bands to construct algorithms can be highly variable. In this study, we proposed the effective wavelength band to detect the CDOM absorption coefficient and the best combination of input wavelength bands to construct a CDOM retrieval algorithm using a random forest. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016–2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption coefficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The verification of a wavelength value of the CDOM absorption coefficient in remote sensing has room to be used as a reference value, and it shows the possibility of being utilized in the construction of CDOM retrieving algorithm and spatial distribution. © 2022, The Authors. All rights reserved.

关键词： Reflection

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Hierarchical Learning for IRS-Assisted MEC systems with Rate-Splitting Multiple Access

arXiv

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arXiv 2024年

作者： Wu, Yinyu Zhang, Xuhui Ren, Jinke Shen, Yanyan Yang, Bo Wang, Shuqiang Guan, Xinping Niyato, Dusit The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China The University of Chinese Academy of Sciences Beijing100049 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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iGEM: a model system for team science and innovation

arXiv

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arXiv 2023年

作者： Santolini, Marc Blondel, Leo Palmer, Megan J. Ward, Robert N. Jeyaram, Rathin Brink, Kathryn R. Krishna, Abhijeet Barabási, Albert-László Université Paris Cité Inserm System Engineering and Evolution Dynamics ParisF-75004 France Learning Planet Institute ParisF-75004 France Network Science Institute Department of Physics Northeastern University BostonMA02115 United States Department of Bioengineering Stanford University StanfordCA United States Center for International Security and Cooperation Stanford University StanfordCA United States School of Public Policy Georgia Institute of Technology AtlantaGA United States Channing Division of Network Medicine Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Network and Data Science Central European University Budapest Hungary

Teams are a primary source of innovation in science and technology. Rather than examining the lone genius, scholarly and policy attention has shifted to understanding how team interactions produce new and useful ideas. Yet the organizational roots of innovation remain unclear, in part because of the limitations of current data. This paper introduces the international Genetically Engineered Machine (iGEM) competition, a model system for studying team science and innovation. By combining digital laboratory notebooks with performance data from 2,406 teams over multiple years of participation, we reveal shared dynamical and organizational patterns across teams and identify features associated with team performance and success. This dataset makes visible organizational behavior that is typically hidden, and thus understudied, creating new opportunities for the science of science and innovation. © 2023, CC BY.

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Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach

arXiv

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arXiv 2021年

作者： Wang, Xiong Ye, Jiancheng Lui, John C.S. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Network Technology Lab Hong Kong Research Center Huawei Technologies Co. Ltd. Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short to handle task placement involving many coexisting users in a dynamic and uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system-side information to enable a decentralized user-initiated service placement. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We show that DEBO can deduce the optimal user-server assignment, thereby achieving a close-to-optimal service performance and tight O(log T) offloading regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users' offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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Semi-supervised learning for COVID-19 image classification via ResNet

arXiv

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arXiv 2021年

作者： Nwosu, Lucy Li, Xiangfang Qian, Lijun Kim, Seungchan Dong, Xishuang Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Center of Excellence in Research and Education for Big Military Data Intelligence CREDIT Center Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. Supervised deep learning has been successfully applied to recognize COVID-19 pathology from X-ray imaging datasets. However, it requires a substantial amount of annotated X-ray images to train models, which is often not applicable to data analysis for emerging events such as COVID-19 outbreak, especially in the early stage of the outbreak. To address this challenge, this paper proposes a two-path semi-supervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. Moreover, we design a weighted supervised loss that assigns higher weight for the minority classes in the training process to resolve the data imbalance. Experimental results on a large-scale of X-ray image dataset COVIDx demonstrate that the proposed model can achieve promising performance even when trained on very few labeled training images. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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Non-Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models

arXiv

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arXiv 2022年

作者： Behrens, Gunnar Beucler, Tom Gentine, Pierre Iglesias-Suarez, Fernando Pritchard, Michael Eyring, Veronika Institut für Physik der Atmosphäre Oberpfaffenhofen Germany Department of Earth and Environmental Engineering Columbia University New YorkNY10027 United States Institute of Earth Surface Dynamics University of Lausanne VD Lausanne1015 Switzerland Earth Institute and Data Science Institute Columbia University New YorkNY10027 United States Department of Earth System Science University of California Irvine IrvineCA United States Bremen Germany

Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non-linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed-forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation;b) high optically thin cirrus-like clouds are separated from low optically thick cumulus clouds;and c) shallow convective processes are associated with large-scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub-grid-scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties. © 2022, CC BY.

关键词： Learning systems

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Local Partial Zero-Forcing Combining for Cell-Free Massive MIMO systems

arXiv

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arXiv 2022年

作者： Zhang, Jiayi Zhang, Jing Björnson, Emil Ai, Bo 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 Department of Electrical Engineering Linköping University Linköping Sweden The Department of Computer Science KTH Royal Institute of Technology Kista Sweden The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is to realize signal processing in a scalable way. In this paper, we consider scalable full-pilot zero-forcing (FZF), partial FZF (PFZF), protective weak PFZF (PWPFZF), and local regularized ZF (LRZF) combining by exploiting channel statistics. We derive closed-form expressions of the uplink SE for FZF, PFZF, and PWPFZF combining with large-scale fading decoding over independent Rayleigh fading channels, taking channel estimation errors and pilot contamination into account. Moreover, we investigate the impact of the number of pilot sequences, antennas per AP, and APs on the performance. Numerical results show that LRZF provides the highest SE. However, PWPFZF is preferable when the number of pilot sequences is large and the number of antennas per AP is small. The reason is that PWPFZF has lower computational complexity and the SE expression can be computed in closed-form. Furthermore, we investigate the performance of PWPFZF combining with fractional power control and the numerical results show that it improves the performance of weak UEs and realizes uniformly good service for all UEs in a scalable fashion. Copyright © 2022, The Authors. All rights reserved.

关键词： Power control

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Feature selection for chemical process fault diagnosis by artificial immune systems

引用

Chinese Journal of Chemical engineering 2018年第8期26卷 1599-1604页

作者： Liang Ming Jinsong Zhao Department of Chemical Engineering Tsinghua University Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua University

With the Industry 4.0 era coming, modern chemical plants will be gradually transformed into smart factories, which sets higher requirements for fault detection and diagnosis(FDD) to enhance operation safety intelligence. In a typical chemical process, there are hundreds of process variables. Feature selection is a key to the efficiency and effectiveness of FDD. Even though artificial immune system has advantages in adaptation and independency on a large number of fault samples, antibody library construction used to be based on experience. It is not only time consuming, but also lack of scientific foundation in fault feature selection, which may deteriorate the FDD performance of the AIS. In this paper, a fault antibody feature selection optimization(FAFSO) algorithm is proposed based on genetic algorithm to optimize the fault antibody features and the antibody libraries' thresholds simultaneously. The performance of the proposed FAFSO algorithms is illustrated through the Tennessee Eastman benchmark problem.

关键词： Artificial immune system Genetic algorithm Feature selection

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Algorithms for magnetic symmetry operation search and identification of magnetic space group from magnetic crystal structure

arXiv

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arXiv 2022年

作者： Shinohara, Kohei Togo, Atsushi Tanaka, Isao Department of Materials Science and Engineering Kyoto University Sakyo Kyoto606-8501 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki Tsukuba305-0047 Japan Center for Elements Strategy Initiative for Structural Materials Kyoto University Sakyo Kyoto606-8501 Japan Nanostructures Research Laboratory Japan Fine Ceramics Center Nagoya456-8587 Japan

A crystal symmetry search is crucial for computational crystallography and materials science. Although algorithms and implementations for the crystal symmetry search have been developed, their extension to magnetic space groups (MSGs) remains limited. In this paper, algorithms for determining magnetic symmetry operations of magnetic crystal structures, identifying magnetic space-group types of given MSGs, searching for transformations to a BNS setting, and symmetrizing the magnetic crystal structures using the MSGs are presented. The determination of magnetic symmetry operations is numerically stable and is implemented with minimal modifications from the existing crystal symmetry search. Magnetic space-group types and transformations to the BNS setting are identified by a two-step approach combining space-group type identification and the use of affine normalizers. Point coordinates and magnetic moments of the magnetic crystal structures are symmetrized by projection operators for the MSGs. An implementation is distributed with a permissive free software license in spglib v2.0.2: https://***/spglib/spglib. © 2022, CC BY.

关键词： Crystal symmetry

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Ultra-broadband Luminescent Bi/Er Co-Doped Optical Fibre Based Temperature Sensing system

Ultra-broadband Luminescent Bi/Er Co-Doped Optical Fibre Bas...

引用

OptoElectronics and Communications Conference, OECC

作者： Yanhua Luo Jianxiang Wen Yanhua Dong Mengshi Zhu Tingyun Wang Shuen Wei Weiwen Zou Jiaying Wang Gang-Ding Peng Optical Fiber Research Institute Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Institute for Photonics and Advanced Sensing School of Physics Chemistry and Earth Sciences The University of Adelaide Adelaide SA Australia Department of Electronic Engineering State Key Laboratory of Advanced Optical Communications System and Networks Shanghai Jiao Tong University Shanghai China Photonics and Optical Communications Group School of Electrical Engineering and Telecommunications University of New South Wales Sydney NSW Australia

Bi/Er co-doped optical fibres (BEDFs) with the ultra-broadband emission and an eccentric core have been proposed for Mach–Zehnder interferometric (MZI) temperature sensing, which provides the temperature sensitivitie... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Temperature sensors Optical fibers Optical interferometry Temperature distribution Sensitivity Sensors Broadband communication Light sources

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