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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Understanding the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is pivotal for grasping the regional water cycle and energy balance dynamics. Yet it is unclear how the changes of the intricate synergy among diverse environment factors significantly impacts the patterns of ET and its transformation state. Addressing this gap, this study employed observational data from 14 sites, utilizing the Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Model parameters of SWH were optimized by Monte Carlo method, achieving a commendable simulation performance with a mean R2 value of 0.75. Our analysis reveals that in alpine meadow, the ratio of transpiration (T) to ET (T/ET) is generally higher (0.55) compared to alpine steppe (0.39). The T/ET values for hover around or below 0.5, indicating a balanced contribution of various ET components. Spatially, both PPT and LAI have an impact on ET and T/ET. PPT emerges as the primary driver of interannual spatial variations in ET, while LAI plays a crucial role in regulating T/ET. Our results further demonstrate that a transition from water-dominated to energy-dominated systems in TP grassland ecosystem at an approximately aridity index of 0.73. This is noteworthy considering the general application of an aridity index of 0.65 to delineate arid from humid regions. It implies that TP grasslands, within the 0.65-0.73 range, remain primarily water-driven. This insight holds substantial implications for comprehending water-energy dynamics in similar ecosystems, challenging the conventional binary categorization of arid and humid areas. Such findings are of immense significance in enhancing understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2024, The Authors. All rights reserved.

关键词： Water supply

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A Model Based Heterogeneous Data Collaboration Method

A Model Based Heterogeneous Data Collaboration Method

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Junjie Xue Junhua Zhou Guoqiang Shi Yanhong Jiang Bowen Wei Hongyan Quan Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Beijing Simulation Center Science and Technology on Space System Simulation Laboratory Beijing China School of Software Engineering East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (数字)9781728192581

ISBN: (纸本)9781728192598

Model based heterogeneous data collaboration is important in the integrated design and manufacturing technology. In this work, we propose a systematic method of collaborative design method for heterogeneous data sources. In our method, we use the Open Model Based Engineering Environment (OpenMBEE) to study. In our work, we setup environment and construct the integrated multi-tools achieving consistency design, and then we successfully design and implement a reasonable scheme to parameterize the model in requirement analysis and store the extracted parameters in Model Management system (MMS) for transporting to the visualization tool for further simulation. We integrate the mathematical analysis tools of MATLAB, and successfully design the interface in the tool integration, and realize the model consistency in the problem of integrating multiple tools in the design based on model center. The effectiveness of the proposed integration strategies is verified by some practical instances. That is verified that our provided study can an effective design idea for complex production.

关键词： Analytical models Collaboration Production Tools Data models Mathematical model Matlab

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Adaptive Dynamic Surface Control of Constrained Nonlinear systems

Adaptive Dynamic Surface Control of Constrained Nonlinear Sy...

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第32届中国控制与决策会议

作者： Ailiang Xing Biao Song Yixin Yin School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Demonstration and Research Department of Network Information System Beijing Institute of Computer Application

This paper presents an adaptive dynamic surface control for uncertain nonlinear strict feedback systems with constraints on both input and *** technology is employed in the controller *** performance method with asymmetric barrier Lyapunov function(BLF) is used to deal with the output *** input saturation is solved by using the Nussbaum *** dynamic surface control(DSC) technique is employed to reduce the complexity of the virtual *** is illustrated that all closed-loop signals are semi-global uniformly ultimately bounded(SGUUB).Finally,a simulation example proves the proposed results.

关键词： Input saturation Output constraints Nussbaum function Dynamic surface control

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Accuracy vs. Efficiency: Achieving both Through Hardware-Aware Quantization and Reconfigurable Architecture with Mixed Precision

Accuracy vs. Efficiency: Achieving both Through Hardware-Awa...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Libo Chang Shengbing Zhang Huimin Du Shiyu Wang Meikang Qiu Jihe Wang School of Computer Science Northwestern Polytechnical University and National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China School of Electronic Engineering Xi'an University of Posts and Telecommunication Xi'an China School of Computer Science Northwestern Polytechnical University and Engineering Research Center of Embedded System Integration Ministry of Education Xi'an China Texas A&M University-Commerce Texas USA

We propose a hardware/software co-design framework, which leverages hardware-aware quantization and a reconfigurable processor to improve the computational efficiency of convolutional neural networks (CNNs) on tiny IoT devices based on reconfigurable platforms. Firstly, we proposed a multi-objective optimization value function that can weigh accuracy, the size of CNN models, and computational delay, to improve the efficiency of the mixed- precision quantization algorithm based on deep reinforcement learning. Secondly, we propose a reconfigurable CNN processor that can adapt to the computing characteristics of various quantized CNN models, as well as a reconfigurable computing array and an on-chip elastic buffer, to improve the performance and computing efficiency on edge equipment. Finally, we demonstrate the effectiveness of the proposed co-design method through an extensive evaluation of the Ultra96-V2 platform. With respect to the well-known CNNs—VGG-16, ResNet-50, and MobileNet-V2, the experimental result shows that the throughput of 216.6 GOPS, 214.0 GOPS, and 53.6 GOPS, the computing efficiency of 0.63GOPS/DSP, 0.64GOPS/DSP, and 0.24 GOPS/DSP, respectively. In addition, achieving a better optimized trade-off between the computing efficiency and accuracy compared with the recently proposed CNN processor with fixed bit-width and mixed-precision.

关键词： Adaptation models Quantization (signal) Computational modeling Reinforcement learning Throughput system-on-chip Reconfigurable architectures

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A Coupled Transformer-CNN Network: Advancing Sea Surface Temperature Forecast Accuracy

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Tao Lin, Pengfei Liu, Hailong Wang, Pengfei Wang, Ya Xu, Kai Zheng, Weipeng Li, Yiwen Jiang, Jinrong Zhao, Lian Chen, Jian State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China College of Earth and Planetary Sciences University of Chinese Academy of Sciences Beijing100049 China Laoshan Laboratory Qingdao266237 China Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China Earth System Numerical Simulation Science Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China School of Ocean Sciences China University of Geosciences Beijing100083 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Geo-Information Engineering Xi'an710054 China

Sea surface temperature (SST) is critically important for understanding ocean dynamics and supporting various marine activities, making accurate short-term SST forecasting highly significant. However, accurately modeling the multi-scale variability of SST remains challenging for existing deep learning (DL) models. This study introduces the Coupled Transformer-CNN Network (CoTCN), a hybrid architecture designed to leverage the multi-scale variability of SST. The CoTCN combines the strengths of Transformers and convolutional neural networks (CNNs), significantly enhancing SST forecasts' spatial continuity and predictive accuracy. Compared to five state-of-the-art DL models based on Transformer or CNN that include ConvLSTM, ConvGRU, AFNO, PredRNN, and SwinLSTM, CoTCN demonstrates superior performance in global and local areas of SST forecasting. At 1-day lead time, CoTCN reduces the global average root mean square error (RMSE) by over 15%, with forecast errors ranging from 0.20°C to 0.53°C across 1-10 day lead times. Moreover, the CoTCN effectively mitigates the checkerboard artifacts inherent to the Vision Transformer architecture. These findings highlight the effectiveness of CoTCN in capturing SST's multi-scale features and underscore the promising potential of hybrid architectures for future DL models. © 1980-2012 IEEE.

关键词： Convolutional neural networks

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Natural image classification based on multi-parameter optimization in deep convolutional neural network

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International Journal of Performability Engineering 2019年第9期15卷 2515-2521页

作者： Wang, Lei Zhang, Yanning Xi, Runping Ling, Lu School of Computer Northwestern Polytechnical University Xi'an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710072 China Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Nanchang330013 China

Traditional machine learning algorithms cannot adequately train the parameters of networks using massive data. A deep convolutional neural network based on multi-parameter optimization by the TensorFlow deep learning framework is designed in this paper. In order to improve the training speed and prevent over-fitting, we improve and optimize the multi-parameters, including the batch value, dropout, and momentum in the network structure. The experiment involves training and testing on the standard natural image data sets in cifar-10 and cifar-100. The experimental results show that the method achieve better classification accuracy in less time compared with other algorithms, such as Conv-KN, ImageNet-2010, SVM, LR, and Boosting. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Image classification

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Study on Subsynchronous Resonance Damping Control for Series-Compensated DFIG-Based Wind Farm

Study on Subsynchronous Resonance Damping Control for Series...

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2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019

作者： Luo, Chao Guo, Qi Xiao, Xiangning Gole, A.M. Liao, Mengjun Electric Power Research Institute State Key Laboratory of HVDC China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China North China Electric Power University State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources Beijing China University of Manitoba Electrical and Computer Engineering Department Winnipeg Canada

ISBN: (纸本)9781728149301

Subsynchronous Resonance (SSR) is a potential threat for long distance power transmission from doubly fed induction generator (DFIG) based wind farms with fixed series compensation (FSC). In this paper, subsynchronous resonance damping control (SSRDC) strategy based on shunt voltage source converter is proposed. Taking account of different series compensation levels and wind speeds, several potential inputs to the SSRDC are investigated. SSRDC control parameters are determined via root locus techniques to ensure the system stability of subsynchronous modes. Considering implementation feasibility in practical project and stability of supersynchronous modes, an enhanced SSRDC is proposed. Impact of transmission line and DFIG parameters and the reactive power compensation to the mitigation effect is investigated. Time domain simulation using PSCAD/EMTDC are carried out to validate the effectiveness of the SSRDC. © 2019 IEEE.

关键词： Wind farm

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Shadow Detection Of Moving Objects In Traffic Monitoring Video

Shadow Detection Of Moving Objects In Traffic Monitoring Vid...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Mingrui Zhang Wenbing Zhao Xiying Li Dan Wang College of Computer Science and Technology Beijing University of Technology Beijing China Intelligent Transportation Research Center School of Intelligent System Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Intelligent Transportation System of Guangdong Province Guangzhou China Key Laboratory of Video Image Intelligent Analysis and Application Technology Ministry of Public Security Guangzhou China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Moving object detection is an important application of computer vision. Commonly used foreground separation algorithms such as Gaussian mixture modeling, ViBe, frame difference method, etc., do not consider the color of shadow and recognize the shadow of a moving object as a part of the moving object. In many cases, the shadow detection effect is not good. Focus on the detection of moving object shadows in traffic surveillance videos, this paper improves the existing ViBe algorithm, considers the color characteristics of the shadows, recognizes the shadows as part of the background, gains a smaller amount of calculation and better effect of shadow detection with the advantages of ViBe.

关键词： Surveillance Conferences Color Object detection Traffic control Information technology Videos

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2023年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Accurately quantifying spatiotemporal variations in evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for understanding the regional water cycle and energy balance. However, the determinant of spatial variations in ET and its component processes in grassland ecosystems of TP remain unclear. In this study, we used 14 sites observation data and applied Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Our results highlighted the good performance of SWH model. There were corresponding drops in ET on alpine meadow and on alpine steppe. And the ratio of ET and precipitation (ET/PPT) of most sites was less than 1 in alpine meadow. Furthermore, the ratio of transpiration (T) and ET (T/ET) in alpine meadow generally exceeded that in alpine steppe. PPT emerged as the primary driver of interannual spatial variations in ET, while LAI played a crucial role in regulating T/ET. Our results further demonstrated that in the grassland ecosystem of TP, as the aridity index increased, the ET gradually transitioned from being primarily influenced by water supply to energy demand. This transition occurred at an aridity index of 0.73 approximately. The results of this study imply that in the context of global warming, with aridity index in TP decreasing, the ET of more ecosystems will shift from energy-led to water-led. Such findings are of immense significance in enhancing our understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2023, The Authors. All rights reserved.

关键词： Water supply

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Near-real-time global gridded daily CO2 emissions 2021

arXiv

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arXiv 2022年

作者： Dou, Xinyu Hong, Jinpyo Ciais, Philippe Chevallier, Frédéric Yan, Feifan Yu, Ying Hu, Yifan Huo, Da Sun, Yun Wang, Yilong Davis, Steven J. Crippa, Monica Janssens-Maenhout, Greet Guizzardi, Diego Solazzo, Efisio Lin, Xiaojuan Song, Xuanren Zhu, Biqing Cui, Duo Ke, Piyu Wang, Hengqi Zhou, Wenwen Huang, Xia Deng, Zhu Liu, Zhu Department of Earth System Science Tsinghua University Beijing100084 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Laboratoire des Sciences du Climat et de l'Environnement LSCE/IPSL CEA-CNRS-UVSQ UniversitéParis-Saclay Gif-sur-Yvette France Key Laboratory of Marine Environment and Ecology Frontiers Science Center for Deep Ocean Multispheres and Earth System Ministry of Education Ocean University of China Qingdao266100 China Environmental Sciences and Engineering University of North Carolina at Chapel Hill Chapel Hill United States Key Laboratory of Sustainable Forest Ecosystem Management Northeast Forestry University Harbin150040 China School of Environmental Science and Engineering Tianjin University Tianjin300072 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Earth System Science University of California IrvineCA United States Ispra Italy Product and Solution & Website Business Unit Alibaba Cloud Hangzhou311121 China

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1°×0.1°spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from a near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly. © 2022, CC BY.

关键词： Uncertainty analysis

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