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Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data

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International Journal of digital Earth 2022年第1期15卷 1784-1816页

作者： Shaopeng Li Bo Jiang Shunlin Liang Jianghai Peng Hui Liang Jiakun Han Xiuwan Yin Yunjun Yao Xiaotong Zhang Jie Cheng Xiang Zhao Qiang Liu Kun Jia The State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of SciencesBeijingPeople’s Republic of China Beijing Engineering Research Center for Global Land Remote Sensing Products Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China Department of Geographical Sciences University of MarylandCollege ParkMDUSA Beijing Engineering Research Center for Global Land Remote Sensing Products College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingPeople’s Republic of China

The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water *** studies have been conducted to estimate from satellite top-of-atmosphere(TOA)data using various methods,particularly the application of machine learning(ML)and deep learning(DL).However,few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in *** on extensive in situ measurements distributed at mid-low latitudes,the corresponding Moderate Resolution Imaging Spectroradiometer(MODIS)TOA observations,and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)used as a priori knowledge,this study assessed nine models for daily estimation,including six classic ML methods(random forest-RF,adaptive boosting-Adaboost,extreme gradient boosting-XGBoost,multilayer perceptron-MLP,radial basis function neural network-RBF,and support vector machine-SVM)and three DL methods(multilayer perceptron neural network with stacked autoencoders-SAE,deep belief network-DBN and residual neural network-ResNet).The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost,although they all performed poorly in certain conditions such as winter days,rugged terrain,and high *** had the most robust performance across different land cover types,elevations,seasons,and latitude zones,but it has disadvantages in practice because of its highly configurable implementation environment and low computational *** estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite(GLASS)product.

关键词： Net radiation energy balance mid-low latitude model comparison machine learning deep learning MODIS ERA5

Predictive Resilience Assessment of Road Networks Based on Dynamic Multi-Granularity Graph Neural Network

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SSRN

SSRN 2024年

作者： Zang, Di Ding, Yongjie Zhao, Jiayi Tang, Keshuang Zhu, Hong Key Laboratory of Embedded System and Service Computing Ministry of Educaiton Department of Computer Science and Technology Tongji University No. 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education College of Transportation Engineering Tongji University No. 4800 Cao’an Road Shanghai China

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: 1) Simulation methods for modeling rainfall impacts lack realism;2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial-temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios. © 2024, The Authors. All rights reserved.

关键词： Roads and streets

Exploring Text-Guided Single Image Editing for Remote Sensing Images

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arXiv 2024年

作者： Han, Fangzhou Si, Lingyu Dong, Hongwei Zhang, Lamei Chen, Hao Du, Bo The Department of Information Engineering Harbin Institute of Technology Harbin China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China The Hubei Luojia Laboratory National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. Deep learning based editing methods generally involve two sequential stages: generation and editing. During the generation stage, consistency in content and details between the original and edited images must be maintained, while in the editing stage, controllability and accuracy of the edits should be ensured. For natural images, these challenges can be tackled by training generative backbones on large-scale benchmark datasets and using text guidance based on vision-language models (VLMs). However, these previously effective approaches become less viable for RSIs due to two reasons: First, existing generative RSI benchmark datasets do not fully capture the diversity of remote sensing scenarios, particularly in terms of variations in sensors, object types, and resolutions. Consequently, the generalization capacity of the trained backbone model is often inadequate for universal editing tasks on RSIs. Second, the large spatial resolution of RSIs exacerbates the problem in VLMs where a single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics when using text to guide RSI editing. To solve above problems, this paper proposes a text-guided RSI editing method that is controllable but stable, and can be trained using only a single image. It adopts a multi-scale training approach to preserve consistency without the need for training on extensive benchmark datasets, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability in the text-guided editing process. High-quality RSI editing results can be achieved by first applying multi-scale training to the target image, followed by text-guided fine-tuning using RSI pre-trained VLMs and PE. Experi

关键词： Semantics

Simplified Single-Scatter Path Loss Model of LED-based Non-Line-of-Sight Ultraviolet Communications

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Simplified Single-Scatter Path Loss Model of LED-based Non-L...

2021 International Conference engineering and Telecommunication, En and T 2021

作者： Gao, Xinyu Cao, Tian Zhang, Hongming Pan, Changyong Song, Jian Tsinghua University Department of Electronic Engineering Beijing China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen China

ISBN: (纸本)9781728183909

In this work, a simplified model of single-scatter path loss (PL) for the LED-based non-line-of-sight ultraviolet communication system is proposed. For the case of narrow field-of-view of receiver, a closed-form expression of PL is derived by modeling the LED emission pattern as Lambertian distribution. The proposed PL model is compared with the exact single-scatter PL model and the Monte Carlo numerical model to verify its effectiveness and accuracy. The results indicate that the root-mean-square-error between the proposed model and the exact single-scatter model is less than 0.5dB in its application ranges. © 2021 IEEE.

关键词： Light emitting diodes

An Enhancing Stability Control Strategy of VSG-Based Power Source Under PWM Rectifier Load

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Gaoxiang Liu, Bowen Song, Wenhao Jining, Chen Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical Engineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Nanning530004 China

Under PWM rectifier load, the power source based on virtual synchronous generator (VSG) is prone to cause mid-frequency oscillation. Firstly, the system oscillation mechanism and characteristic of the VSG-based power source are analyzed based on impedance method. Due to the negative resistance and capacitive characteristic in the middle frequency range, the PWM rectifier load is prone to interact with the VSG-based power source, which can lead to impedance resonance. Secondly, to improve the stability of the VSG-based power source under the PWM rectifier load, a voltage differentiation feedforward control is proposed. In addition, since the mid-frequency impedance magnitude decrease obviously, the improved VSG-based power source is closer to the ideal voltage source, which can automatically suppress the middle harmonics induced by uncontrollable rectifier load. Finally, the proposed control and the corresponding theoretical analysis are verified by simulations and experiments. © 1975-2011 IEEE.

关键词： Synchronous generators

Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation

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arXiv 2024年

作者： Huang, Tianyu Zhou, Tao Xie, Weidi Wang, Shuo Dou, Qi Zhang, Yizhe The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai Key Laboratory of MICCAI Shanghai China Shanghai Jiaotong University Shanghai China The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation for medical images. In medical imaging contexts, it is not uncommon for human experts to rectify segmentations of specific test samples after SAM generates its segmentation predictions. These rectifications typically entail manual or semi-manual corrections employing state-of-the-art annotation tools. Motivated by this process, we introduce a novel approach that leverages the advantages of online machine learning to enhance Segment Anything (SA) during test time. We employ rectified annotations to perform online learning, with the aim of improving the segmentation quality of SA on medical images. To improve the effectiveness and efficiency of online learning when integrated with large-scale vision models like SAM, we propose a new method called Auxiliary Online Learning (AuxOL). AuxOL creates and applies a small auxiliary model (specialist) in conjunction with SAM (generalist), entails adaptive online-batch and adaptive segmentation fusion. Experiments conducted on eight datasets covering four medical imaging modalities validate the effectiveness of the proposed method. Our work proposes and validates a new, practical, and effective approach for enhancing SA on downstream segmentation tasks (e.g., medical image segmentation). © 2024, CC BY.

关键词： Medical imaging

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Context Graph

arXiv 2024年

作者： Xu, Chengjin Li, Muzhi Yang, Cehao Jiang, Xuhui Tang, Lumingyuan Qi, Yiyan Guo, Jian IDEA Research International Digital Economy Academy China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China

Knowledge Graphs (KGs) are foundational structures in many AI applications, representing entities and their interrelations through triples. However, triple-based KGs lack the contextual information of relational knowledge, like temporal dynamics and provenance details, which are crucial for comprehensive knowledge representation and effective reasoning. Instead, Context Graphs (CGs) expand upon the conventional structure by incorporating additional information such as time validity, geographic location, and source provenance. This integration provides a more nuanced and accurate understanding of knowledge, enabling KGs to offer richer insights and support more sophisticated reasoning processes. In this work, we first discuss the inherent limitations of triple-based KGs and introduce the concept of CGs, highlighting their advantages in knowledge representation and reasoning. We then present a context graph reasoning CGR3 paradigm that leverages large language models (LLMs) to retrieve candidate entities and related contexts, rank them based on the retrieved information, and reason whether sufficient information has been obtained to answer a query. Our experimental results demonstrate that CGR3 significantly improves performance on KG completion (KGC) and KG question answering (KGQA) tasks, validating the effectiveness of incorporating contextual information on KG representation and reasoning. © 2024, CC BY-NC-ND.

关键词： Knowledge graph

LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement

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arXiv 2024年

作者： Yang, Haodong Xu, Jisheng Lin, Zhiliang He, Jianping The Department of Computer Science Shanghai Jiao Tong University China The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai200240 China

computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

A Continual Learning Method for Reducing Class Interference Based on Replay 42

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A Continual Learning Method for Reducing Class Interference ...

42nd Chinese Control Conference, CCC 2023

作者： Xu, Zhibo Wang, Tian Wang, Jian Li, Ce Fu, Yao Snoussi, Hichem School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Sklsde Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100191 China School of Computer Science Wuhan University Wuhan430072 China China University of Mining and Technology Department of Computer Science and Technology Beijing100083 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Laboratory of Computer Science and Digital Society University of Technology of Troyes Troyes France

ISBN: (纸本)9789887581543

Although deep neural networks perform well on many individual tasks, they suffer from catastrophic forgetting when learning new tasks continually. Recently, various continual learning methods have been proposed, and some approaches based on replaying memory data achieve promising performance. Maximally Interfered Retrieval is a strong replay-based baseline, however, exists class interference due to class imbalance and lacks the ability to generalize to the real class-incremental scenario. In this paper, aiming at these problems, we design Class cumulative Classifier to replace the shared output layer of the original network, which makes it closer to practical applications, and Class balanced Buffer to address the class imbalance of stored samples. In addition, we propose Retrospect strategy to further improve the accuracy. Experimental results on benchmark datasets show that our method outperforms several strong baselines and is more suitable for complex datasets with more classes. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Image classification