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Joint Retrieval of PM_(2.5) Concentration and Aerosol Optical Depth over China Using Multi-Task Learning on FY-4A AGRI

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Advances in Atmospheric Sciences 2025年第1期42卷 94-110页

作者： Bo LI Disong FU Ling YANG Xuehua FAN Dazhi YANG Hongrong SHI Xiang’ao XIA Electronic Engineering College Chengdu University of Information TechnologyChengdu 610225China Tianjin Meteorological Radar Research Trial Center Tianjin Meteorological BureauTianjin 300074China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Research Centre on Meteorological Observation Engineering Technology China Meteorological AdministrationBeijing 100081China Key Laboratory of Atmospheric Environment and Extreme Meteorology Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China University of Chinese Academy of Sciences Beijing 100049China

Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate ***,the complex correlation of AOD–PM_(2.5)and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same *** this point,a multi-task learning(MTL)model,which enables the joint retrieval of PM_(2.5)concentration and AOD,is proposed and applied on the top-of-the-atmosphere reflectance data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager(FY-4A AGRI),and compared to that of two single-task learning models—namely,Random Forest(RF)and Deep Neural Network(DNN).Specifically,MTL achieves a coefficient of determination(R^(2))of 0.88 and a root-mean-square error(RMSE)of 0.10 in AOD *** comparison to RF,the R^(2)increases by 0.04,the RMSE decreases by 0.02,and the percentage of retrieval results falling within the expected error range(Within-EE)rises by 5.55%.The R^(2)and RMSE of PM_(2.5)retrieval by MTL are 0.84 and 13.76μg m~(-3)*** with RF,the R^(2)increases by 0.06,the RMSE decreases by 4.55μg m~(-3),and the Within-EE increases by 7.28%.Additionally,compared to DNN,MTL shows an increase of 0.01 in R^(2)and a decrease of 0.02 in RMSE in AOD retrieval,with a corresponding increase of 2.89%in *** PM_(2.5)retrieval,MTL exhibits an increase of 0.05 in R^(2),a decrease of 1.76μg m~(-3)in RMSE,and an increase of 6.83%in *** evaluation suggests that MTL is able to provide simultaneously improved AOD and PM_(2.5)retrievals,demonstrating a significant advantage in efficiently capturing the spatial distribution of PM_(2.5)concentration and AOD.

关键词： AOD PM_(2.5) FY-4A multi-task learning joint retrieval

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Automatic Fruit Detection Based on Robotic Vision

Automatic Fruit Detection Based on Robotic Vision

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Big Data & Artificial Intelligence & Software engineering (ICBASE), International Conference on

作者： Yiran Chen Rui Yao Haoran Liu Bingqi Liu Mingzhe Liu School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Norla Institute of Technical Physics Chengdu China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

Automatic fruit detection has greatly reduced labor costs and crop damage rates, contributing to the progress of agricultural modernization. It involves real-time assessment of the surrounding environment and recognition of target fruit categories. However, fruit detection faces challenges due to the similarities in shape, color, and other features among different types of fruits, as well as the impact of environmental factors such as inadequate lighting, occlusion, and background variations on recognition accuracy. This project focuses on the simulation of the robotic fruit harvesting process, which includes environmental image acquisition, image processing, and robot control. The robot utilizes its integrated camera to capture real-time images of the surrounding environment. These images are then processed on a computer, employing color recognition based on traditional algorithms. The images are segmented based on the identified color regions and subjected to object detection using YOLO v5. By discerning the presence of the target objects, the results are transmitted to the robot, which responds accordingly. The real-time performance is enhanced by filtering and narrowing down the target regions, reducing the time required for object recognition.

关键词： YOLO Image recognition Accuracy Image color analysis Target recognition Lighting Laser feedback Real-time systems Computational efficiency Laser beams

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Fully Distributed Adaptive Observer-Based Consensus Protocols for Nonlinear Multi-Agent Systems via Dynamic Event-Triggered Transmission Strategy 42

Fully Distributed Adaptive Observer-Based Consensus Protocol...

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42nd Chinese Control Conference, CCC 2023

作者： Han, Sheng Zhu, Hong Kuang, Yiqun Peng, Zhinan Shi, Kaibo Zhong, Qishui School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Institute of Electronic and Information Engineering University of Electronic Science and Technology of China Dongguan523808 China School of Electronic Information and Electrical Engineering Chengdu University Sichuan Chengdu610106 China College of Electrical Engineering Sichuan University Sichuan Chengdu610065 China School of Aeronautics and Astronautics University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9789887581543

This paper focuses on the event-triggered consensus problem of nonlinear multi-agent systems (MASs) via a fully distributed control scheme. Firstly, a nonlinear full-dimensional state observer is established to estimate the state of each agent according to the measurement of the system output. Then, an observer-based adaptive consensus protocol is proposed by utilizing only the neighboring state information, where the global topology of the MASs communication network is no longer required. In addition, a dynamic event-triggered transmission scheme is presented to further reduce the communication load in the MASs network. Furthermore, the consensus of MASs can be ensured by solving two linear matrix inequalities and the gain matrices of observer and controller are obtained. Finally, a simulation test is provided to verify the feasibility and performance of the proposed control methods. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Multi agent systems

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Enhanced Experts with Uncertainty-Aware Routing for Multimodal Sentiment Analysis 24

Enhanced Experts with Uncertainty-Aware Routing for Multimod...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Gao, Zixian Hu, Disen Jiang, Xun Lu, Huimin Shen, Heng Tao Xu, Xing Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Multimodal sentiment analysis, which has garnered widespread attention in recent years, aims to predict human emotional states using multimodal data. Previous studies have primarily focused on enhancing multimodal fusion and integrating information across different modalities, while overlooking the impact of noisy data on the internal features of each single modality. In this paper, we propose the Enhanced experts with Uncertainty-Aware Routing (EUAR) method to address the influence of noisy data on multimodal sentiment analysis by capturing uncertainty and dynamically altering the network. Specifically, we introduce the Mixture of Experts approach into multimodal sentiment analysis for the first time, leveraging its properties under conditional computation to dynamically alter the network in response to different types of noisy data. Particularly, we refine the experts within the MoE framework to capture uncertainty in the data and extract clearer features. Additionally, a novel routing mechanism is introduced. Through our proposed U-loss, which utilizes the quantified uncertainty by experts, the network learns to route different samples to experts with lower uncertainty for processing, thus obtaining clearer, noise-free features. Experimental results demonstrate that our method achieves state-of-the-art performance on three widely used multimodal sentiment analysis datasets. Moreover, experiments on noisy datasets show that our approach outperforms existing methods in handling noisy data. © 2024 ACM.

关键词： Data assimilation

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Evaluation of teaching quality of computing method course based on improved BP neural network

Evaluation of teaching quality of computing method course ba...

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2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020

作者： Deng, Xiaotong Gu, Yi Li, Fei Liu, Xiaohui Zeng, Guoqiang College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Mechanical Engineering Southwest Jiaotong University China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China

Calculation method is a very important basic course of engineering, in the environment of engineering education, a large number of colleges and universities bring calculation methods into the category of professional compulsory courses. This course is not only theoretical, but also practical. Therefore, teachers are required to combine the two organically. The evaluation of teaching quality is of great practical significance to the cultivation of students and the optimization of curriculum. This paper establishes a more convenient and efficient teaching quality evaluation model of calculation method course by making corresponding evaluation index of calculation method course, and proves its excellence through experiments. © Published under licence by IOP Publishing Ltd.

关键词： Curricula

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Research on operation strategy of new energy microgrid theory model containing biomass energy

Research on operation strategy of new energy microgrid theor...

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2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020

作者： Wang, Tang Li, Fei Ge, Liangquan Zeng, Guoqiang Zhang, Qingxian Gu, Yi College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Mechanical Engineering Southwest Jiaotong University China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China

In order to alleviate the energy crisis and improve environmental quality, rational use of renewable energy has become a development trend. This paper proposes a research on the new energy micro-grid operation strategy, and selects the appropriate power generation method according to the development status of each distributed energy in the microgrid. Specially establish the biomass power generation, photovoltaic power generation, wind power generation and storage battery models of the micro-grid, and infer the power generation output characteristics according to the power generation characteristics of each module. A brief analysis of the micro-grid model containing biomass power generation established in the article is carried out, and the basic operation strategy of the micro-grid is proposed, which provides a theoretical basis for the development of the micro-grid in the future. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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Application of BP neural network on quantitative evaluation of curriculum reform in advanced education-A case study of signal and system

Application of BP neural network on quantitative evaluation ...

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2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020

作者： Meng, Lu Gu, Zhixing Li, Fei Liu, Xiaohui Zeng, Guoqiang He, Xingjie College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Mechanical Engineering Southwest Jiaotong University China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China

In this paper, BP neural network model is used to study and analyse the teaching reform of signal and system course. Referring to the structure of BP neural network, the quality evaluation index system of signal and system course is established. On this basis, the factors influencing students' examination results are predicted, and the questionnaire is designed and BP neural network is used for prediction. The mean square deviation of the result is 0.7788. Through the correlation coefficient analysis of the data obtained, we can get the major factors that affect students' final scores. In view of this, the paper puts forward the direction and specific measures of teaching reform of this course. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Analysis of influencing factors and teaching reform of nuclear professional English based on logistic regression

Analysis of influencing factors and teaching reform of nucle...

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2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020

作者： Wang, Jiamei Ge, Liangquan Li, Fei Liu, Xiaohui Zeng, Guoqiang He, Xingjie College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Mechanical Engineering Southwest Jiaotong University China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China

nuclear professional English course can help students of nuclear energy engineering major to understand the corresponding course content accurately and effectively while learning and researching frontier topics. This course has an important guiding role for their future development. This experiment obtained a series of data from students who had completed the course by questionnaire survey, and logistic regression algorithm was used to analyse the influencing factors of professional English test, then obtained the P value. The significant relationship between the independent variable and the dependent variable can be seen from the P value, thus reflecting the problems existing in the current course. The odds ratio (OR value) value of 0.030 is the biggest influence on the passing rate of the examination factor. In the questionnaire survey on the distribution of students' intention, 96.25% of the students hope to have a new teaching mode, while 38.10% of the students do not take the initiative to read English books. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Modeling of the cooperative operation process of aircraft ground centralized deicing 4

Modeling of the cooperative operation process of aircraft gr...

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2024 4th International Conference on Mechatronics technology and Aerospace engineering, ICMTAE 2024

作者： Xing, Zhiwei Wang, Yuning Yan, Chunyu Sun, Ke Zhang, Tao Zhao, Liang College of Electronic Information and Automation Civil Aviation University of China Tianjin300300 China College of Safety Science and Engineering Civil Aviation University of China Tianjin300300 China Engineering Technology Research Center Second Research Institute of Civil Aviation Administration of China Chengdu610041 China Sichuan Branch of China Eastern Airlines Technology Company Limited Chengdu610041 China

Aiming at the problems of insufficient refinement and low efficiency of aircraft ground centralized deicing operation under snow and ice weather, a description method of aircraft ground centralized deicing operation process based on a spatiotemporal correlation network was proposed. Based on the allocation and spatiotemporal distribution of airport deicing resources, a colored spatiotemporal network model was established to describe the dynamic process of aircraft ground deicing through the evolution of a colored spatiotemporal network. A resource optimization method was designed. The traditional genetic algorithm was improved by Sine chaotic mapping technology and differential evolution, and verified by experiment. The results show that the root-mean-square error of the idle time of each deicing site is 18.29 after optimization, which is 4.88 less than before optimization, suggesting that the utilization rate of the deicing site is increased by 21.06%. The improved NSGA-Ⅱ algorithm can increase the completion rate of deicing potential resource allocation by 7.62% and has a faster convergence rate compared with the traditional algorithm, which proves the feasibility of the proposed method. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Mean square error

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FedRS-Net: A Federated Learning Approach for Collaborative Multi-Modal Maritime Analytics

FedRS-Net: A Federated Learning Approach for Collaborative M...

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International Conference on Information Fusion

作者： Bole Wilfried Tienin Guolong Cui Yannick Abel Talla Nana Chiagoziem Chima Ukwuoma Roldan Mba Esidang Mohammed Raouf Senouci School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Information Science and Technology Southwest Jiaotong University Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Ensuring the safety and security of our oceans demands a comprehensive Maritime Situational Awareness (MSA) strategy. However, this task has several challenges, including using multi-modal data, data sharing among agencies, privacy concerns, and bandwidth limitations. To address these challenges, this research introduced FedRS-Net. FedRS-Net is a federated deep learning framework that trains multi-modal remote sensing data without exposing client data. The system employs a communication-efficient federated averaging algorithm and a novel convolutional neural network architecture called Redesigned Skip Connection. It integrates synthetic aperture radar (SAR) and optical satellite imageries to achieve remarkable results. Extensive experiments were conducted on the maritime vessel datasets, resulting in a testing accuracy of $99.8 \%$. Further, applying secure aggregation and momentum-based gradient compression reduced communication costs by $\mathbf{7 \%}$. FedRS-Net overcomes privacy concerns and facilitates agency collaboration by enabling collective maritime monitoring through decentralized data. This research provides a robust federated learning solution tailored for multi-modal remote sensing analytics applications.

关键词： Integrated optics Data privacy Accuracy Federated learning Collaboration Optical imaging Satellite images Optical sensors Synthetic aperture radar Remote sensing

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