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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Hajar Hammouch Mounim El-Yacoubi Huafeng Qin Aissam Berrahou Hassan Berbia Mohamed Chikhaoui SAMOVAR Institut Polytechnique de Paris Palaiseau France SSLab National School For Computer Science Mohammed V University in Rabat Rabat Maroc SAMOVAR CNRS Institut Polytechnique de Paris Palaiseau France Chongqing Engineering Laboratory of detection control and integrated system School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China National School For Computer Science Mohammed V University in Rabat Rabat Maroc Soil and Water Management Laboratory Institute of Agronomy & Veterinary Medicine Rabat Maroc

ISBN: (纸本)9781665426220

Recently, Deep Learning models, such as Deep Convolutional Neural Networks (CNNs), have shown remarkable performance on various Computer Vision tasks. Unfortunately, many application domains, such as agriculture image analysis, do not have access to large datasets. In this study, we are interested in the prediction of soil moisture dissipation rates from aerial images using a CNN model. This entails a regression task where the CNN, given an aerial input image, has to predict a vector of regression values associated with the dissipation rates at different locations of the agricultural field. CNNs, however, require large datasets to successfully train the deep network. To this end, we present a Deep Convolutional Generative Adversarial Network (DCGAN) to generate fake agriculture images, that are realistic enough with the objective to augment the original dataset, for better generalization capabilities and more accurate predictions by the trained CNN. Unlike existing GAN-based augmentation approaches used for segmentation or classification, where only images have to be generated to augment the training set, the regression task requires both an image and its associated regression vector. In our approach, we use a two-stage scheme where the baseline CNN trained on the original dataset is harnessed to predict, for each image generated by the DCGAN, the regression vector that will serve as its ground truth in the augmented dataset. The results show that the CNN trained on the augmented dataset in this way allows a relative reduction of the Mean Absolute Error by a percentage of 20.8 w.r.t to the CNN trained on the original dataset. We believe that our approach can be generalized to any image augmentation task involving a regression task.

关键词： Training Deep learning Computational modeling Soil moisture Predictive models Agriculture Social intelligence

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A Scale Adaptive Kernel Correlation Filter-Based Tracker with Optimized Update Strategy

A Scale Adaptive Kernel Correlation Filter-Based Tracker wit...

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Chinese control Conference (CCC)

作者： Minghui Shi Yong Heng Liang Han Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing P.R. China Beijing Institute of Electronic System Engineering Beijing China School of Sino-French Engineer Beihang University Beijing P.R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Correlation filter-based trackers have achieved competitive results in the field of visual tracking, but the abilities to deal with the multi-scale and keep the tracking stability still need to be improved. In this article, the complex background tracking problems are studied based on the kernel correlation filter. In order to address the fixed size limitation, we present an improved scheme, which can not only realize scale adaption, but also retain the advantages of the original algorithm. In addition, the adaptive update learning rate of the model is achieved by using the peak sidelobe ratio as an indicator to measure the filter performance. Using the index, we can selectively reduce the model interference to low precision information. Moreover, we design a redetection mechanism that runs automatically when the tracking evaluation indicator is low. Compared with the radical model update methods, those conservative update strategies can effectively decrease the model pollution and ensure the tracking effect. Extensive verifications based on OTB-100 benchmark show that the optimized algorithm improves the accuracy, robustness, and generalization ability of target tracking.

关键词： Target tracking Filtering algorithms Kernel Adaptation models Classification algorithms Correlation Information filtering

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Mechanical Analysis and Performance Optimization for the Lunar Rover’s Vane-Telescopic Walking Wheel

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engineering 2020年第8期6卷 936-943页

作者： Lu Yang Bowen Cai Ronghui Zhang Kening Li Zixian Zhang Jiehao Lei Baichao Chen Rongben Wang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China Guangdong Key Laboratory of Intelligent Transportation System School of Intelligent Systems EngineeringSun Yat-sen UniversityGuangzhou 510275China College of Transportation Jilin UniversityChangchun 130025China Department of Mechanical Science and Engineering Tokyo Institute of TechnologyTokyo 152-8550Japan Armour College of Engineering Illinois Institute of TechnologyChicagoIL 60616USA China Academy of Space Technology Beijing 100029China

It is well-known that optimizing the wheel system of lunar rovers is ***,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar *** this study,an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its ***,a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension,considering the complex terrain of the ***,the performance was optimized under the limitations of preserving the slope passage and minimizing power *** was achieved via analysis of the wheel force during ***,the effectiveness of the proposed method was demonstrated by several simulation *** newly designed wheel can protrude on demand and reduce energy consumption;it can be used as a reference for lunar rover development engineering in China.

关键词： Intelligent vehicle Vane-telescopic walking wheel Performance optimization Vane spring Lunar rover

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Walking of biped robot with variable stiffness at the ankle joint

Walking of biped robot with variable stiffness at the ankle ...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Bi, Qing Liu, Yixiang Zang, Xizhe Song, Rui Wang, Haibin Li, Weimin Chinese Academy of Sciences Shandong Institute of Advanced Technology Jinan250001 China Shandong University School of Control Science and Engineering Jinan250061 China Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin150080 China

ISBN: (纸本)9781728163215

Achieving versatile and efficient walking on biped robots is an important research objective. The impact between the foot and the ground has significant effects on the walking stability of biped robots. Research on human walking indicates that the joints in human lower limbs show different stiffness values in different phases of walking. Inspired by humans, this paper presents the realization of biped robot walking with variable stiffness at the ankle joint. A walking control scheme which combines PD control and variable impedance control is designed for the biped robot. Results of dynamic walking simulation verified that variable stiffness at the ankle joint could contribute to the improvement of walking stability and energy efficiency. © 2019 IEEE.

关键词： Energy efficiency

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Foliar Cadmium Uptake, Transfer, and Redistribution in Chili: A Comparison of Foliar and Root Uptake, Metabolomic, and Contribution

SSRN

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SSRN 2022年

作者： Ouyang, Xiaoxue Ma, Jie Liu, Yong Li, Pan Wei, Rongfei Chen, Qiusheng Weng, Liping Chen, Yali Li, Yongtao Agro-Environmental Protection Institute Ministry of Agriculture and Rural Affairs Tianjin300191 China Key Laboratory for Environmental Factors Control of Agro-Product Quality Safety Ministry of Agriculture and Rural Affairs Tianjin300191 China Tianjin Academy of Agricultural Sciences Tianjin300192 China School of Earth System Science Tianjin University Tianjin300072 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Soil Quality Wageningen University Wageningen Netherlands College of Natural Resources and Environment South China Agricultural University Guangzhou510642 China College of Resource and Environmental Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou341000 China

Atmospheric deposition is an essential cadmium (Cd) pollution source in agricultural ecosystems, entering crops via roots and leaves. In this study, atmospherically deposited Cd was simulated using cadmium sulfide nanoparticles (CdSN), and chili (Capsicum frutescens L.) was used to conduct a comparative foliar and root experiment. Root and foliar uptake significantly increased the Cd content of chili tissues and subcellular. Scanning electron microscopy and high-resolution secondary ion mass spectrometry showed that Cd that entered the leaves via stomata was fixed in leaf cells, and the rest was mainly through phloem transport to the other organs. In leaf, stem, and root cell walls, Cd signal intensities were 47.4%, 72.2%, and 90.0%, respectively. Foliar Cd uptake significantly downregulated purine metabolism in leaves, whereas root uptake of Cd inhibited stilbenoid, diarylheptanoid, and gingerol biosynthesis in roots. Root uptake contributed 94% Cd in fruits under simultaneous root and foliar uptake conditions attributed to xylem and phloem involvement in Cd translocation. Moreover, root uptake had a significant effect on tissue enzyme activity and fruit metabolic pathways than foliar uptake. These findings facilitate understanding of the absorption and accumulation of atmospheric Cd within plants, which is critical for choosing pollution control technologies and ensuring food security. © 2022, The Authors. All rights reserved.

关键词： Sulfur compounds

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Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta

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Nexus (New York, N.Y.) 2024年第2期1卷 100016页

作者： Mitchell Daneker Shengze Cai Ying Qian Eric Myzelev Arsh Kumbhat He Li Lu Lu Department of Statistics and Data Science Yale University New Haven CT 06511 USA. Department of Chemical and Biomolecular Engineering University of Pennsylvania Philadelphia PA 19104 USA. These authors contributed equally. Institute of Cyber-System & Control College of Control Science & Engineering Zhejiang University Hangzhou 310058 China. School of Chemical Materials and Biomedical Engineering University of Georgia Athens GA 30602 USA. Department of Mathematics University of Pennsylvania Philadelphia PA 19104 USA. Department of Mathematics ETH Zurich 8092 Zurich Switzerland. Wu Tsai Institute Yale University New Haven CT 06510 USA. Lead contact.

Aortic dissection is a life-threatening event that is responsible for significant morbidity and mortality in individuals ranging in age from children to older adults. A better understanding of the complex hemodynamic environment inside the aorta enables clinicians to assess patient-specific risk of complications and administer timely interventions. In this study, we propose to develop and validate a new computational framework, warm-start physics-informed neural networks (WS-PINNs), to address the limitations of the current approaches in analyzing the hemodynamics inside the false lumen (FL) of type B aortic dissection vessels reconstructed from apolipoprotein null mice infused with AngII, thereby significantly reducing the amount of required measurement data and eliminating the dependency of predictions on the accuracy and availability of the inflow/outflow boundary conditions. Specifically, we demonstrate that the WS-PINN models allow us to focus on assessing the 3D flow field inside FL without modeling the true lumen and various branched vessels. Furthermore, we investigate the impact of the spatial and temporal resolutions of MRI data on the prediction accuracy of the PINN model, which can guide the data acquisition to reduce time and financial costs. Finally, we consider the use of transfer learning to provide faster results when looking at similar but new geometries. Our results indicate that the proposed framework can enhance the capacity of hemodynamic analysis in vessels with aortic dissections, with the promise of eventually leading to improved prognostic ability and understanding of the development of aneurysms.

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Development of power drive technology for high lift system of commercial aircraft

Development of power drive technology for high lift system o...

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CSAA/IET International Conference on Aircraft Utility systems 2020, AUS 2020

作者： Liu, Tong Zhang, Yue Wang, Weida Ju, Jiachen Yu, Wenbin Department of Flight Control System Shanghai Aircraft Design and Research Institute 5188 Jinke Rd. Shanghai201210 China Department of Science and Technology Management Commercial Aircraft Corporation of China Ltd. 1919 Shibo Ave. Shanghai200126 China Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiaotong University 800 Dongchuan Rd. Shanghai200240 China

ISBN: (纸本)9781839535635

In this paper, the development of power driving technology for high lift system of modern commercial aircraft is presented. Three kinds of power driving technical schemes of high lift system are demonstrated together with their characteristics. Various applications of power drive technology on present mainstream aircrafts have been addressed and analyzed respectively. In addition to the analysis of existing power drive technology, this work presented that distributed electric power drive technology could be a potential future development trend. The paper can be read as a reference for selecting power driving technical scheme of high lift systems at conceptual design phase of a commercial aircraft. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Conceptual design

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Online Magnetization Surface Identification for a Switched Reluctance Motor

Online Magnetization Surface Identification for a Switched R...

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International Universities Power engineering Conference (UPEC)

作者： Alecksey Anuchin Andrei Bogdanov Galina Demidova Evgeniy Stolyarov Dimid Surnin Yuriy Vagapov Department of Electric Drives Moscow Power Engineering Institute Moscow Russia Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Faculty of Art Science and Technology Wrexham Glyndwr University Wrexham UK

ISBN: (数字)9781728110783

ISBN: (纸本)9781728110790

The precise control of switched reluctance motor requires information about the magnetization surface as a function of rotor angular position and current. The surface can be obtained performing offline tests; however, it demands specialized mechanical equipment and time for commissioning. Moreover, the magnetization surface may be changed with the varying of the rotor and stator temperatures, which affect their dimensions and, therefore, the airgap variation. That is why the online estimation of the magnetization surface is desirable. This paper proposes the identification method operating in parallel with the model predictive current control system utilizing the magnetization map. Any error between the referenced and obtained currents is used to correct the reference points of the two-dimensional array representing the magnetization surface. The proposed method was examined using a simulation model and operated online in parallel with the primary control system driving the electrical machine.

关键词： Magnetization Reluctance motors Rotors Pulse width modulation Couplings Torque

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Coarse particulate matter air quality in East Asia: implications for fine particulate nitrate

arXiv

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arXiv 2022年

作者： Zhai, Shixian Jacob, Daniel J. Shah, Viral Yang, Laura Hyesung Zhang, Qiang Sun, Yele Wang, Shuxiao Luo, Gan Yu, Fangqun Woo, Jung-Hun Kim, Younha Kim, Hwajin Dibb, Jack E. Lee, Taehyoung Han, Jin-Seok Li, Ke Liao, Hong John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Earth System Science Tsinghua University Beijing100084 China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China Atmospheric Sciences Research Center University at Albany AlbanyNY12226 United States Department of Civil and Environmental Engineering Konkuk University Seoul05029 Korea Republic of Laxenburg 2361 Austria Department of Environmental Health Sciences Graduate School of Public Health Seoul National University Seoul08826 Korea Republic of Institute for the Study of Earth Oceans and Space University of New Hampshire DurhamNH03824 United States Department of Environmental Science Hankuk University of Foreign Studies Yongin449791 Korea Republic of Department of Environmental and Energy Engineering Anyang University Anyang14028 Korea Republic of Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China

Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM) between 2.5 μm and 10 μm aerodynamic diameter, as inferred by the difference between PM10 and PM2.5 observations. This coarse PM averages 47 μg m-3 in the North China Plain (NCP) region in 2015-2019 and 21 μg m-3 in the Seoul Metropolitan Area (SMA). It is dominantly contributed by urban fugitive dust, rather than by natural dust as is often presumed. Concentrations decreased by 21% from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. This anthropogenic coarse PM is generally not included in air quality models but acts as a sink of nitric acid (HNO3), thus affecting fine particulate nitrate which is a major air quality concern in China and South Korea. GEOS-Chem model simulation of surface and aircraft observations from the KORUS-AQ campaign over the SMA in May-June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of PM1 nitrate. Anthropogenic coarse PM in the model increases the sensitivity of PM2.5 nitrate to ammonia (NH3) emission in winter. In summer, anthropogenic coarse PM directly affects PM2.5 nitrate by HNO3 uptake, and we find that the decrease of anthropogenic coarse PM over 2015-2019 offset the PM2.5 nitrate reductions expected from decreasing NOx and NH3 emissions. Copyright © 2022, The Authors. All rights reserved.

关键词： Nitrates

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