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A new ChatGPT-empowered,easy-to-use machine learning paradigm for environmental science

Eco-Environment & Health 2024年第2期3卷 131-136页

作者： Haoyuan An Xiangyu Li Yuming Huang Weichao Wang Yuehan Wu Lin Liu Weibo Ling Wei Li Hanzhu Zhao Dawei Lu Qian Liu Guibin Jiang State Key Laboratory of Environmental Chemistry and Toxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Biomedical Engineering Institute School of Control Science and EngineeringShandong UniversityJinan 250061China

The quantity and complexity of environmental data show exponential growth in recent ***-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental *** learning(ML)has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ***,due to the knowledge gap across different subjects,ML concepts and algorithms have not been well-popularized among researchers in environmental *** this context,we introduce a new research paradigm—“ChatGPT t ML t Environment”,providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML *** instance,each step involved in applying ML models to environmental sustainability,including data preparation,model selection and construction,model training and evaluation,and hyper-parameter optimization,can be easily performed with guidance from *** also discuss the challenges and limitations of using this research paradigm in the field of environmental ***,we highlight the importance of“secondary training”for future application of“ChatGPT t ML t Environment”.

关键词： Machine learning Environmental application ChatGPT Secondary training

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Skewed Pins Detection of Aviation Connectors Based on Multi-Modal Cooperative Perception

Skewed Pins Detection of Aviation Connectors Based on Multi-...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Li, Jiaming Ren, Cheng Xu, Jiaxin Wang, Yao Xu, Hanlin Guan, Xinping 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 Department of Automation Shanghai200240 China

ISBN: (纸本)9798331507886

Aviation electrical connectors are critically important components in electrical wiring interconnection systems. Currently, connector defect detection relies entirely on manual inspection, which is error-prone and time-consuming. Due to the unique structural characteristics of aviation connectors, single-modality sensors often struggle to simultaneously detect both pins and holes. To address this issue, we propose a 3D skewed pins detection (3D-SPD) algorithm that integrates both 2D and 3D modals features. Due to the low quality of the raw connector data, we propose a connector point cloud enhancement (CPCE) algorithm that combines multi-view point clouds for point cloud fusion enhancement. Based on enhanced data, we perform skewed pin detection. Experimental results indicate that CPCE and 3D-SPD are effective in detecting skewed pins, achieving an accuracy rate exceeding 99%. Our algorithm can be deployed as an AI assistant on the edge device for the skewed pin detection. © 2024 IEEE.

关键词： defect detection machine vision multi-modal cooperative perception skewed pin

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A Enhanced Feature Extraction for 3D Object Detection 25

A Enhanced Feature Extraction for 3D Object Detection

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Meng, Qingao Tong, Jigang Yang, Sen Xie, Tian Jin, Xin Tianjin University of Technology The School of Electrical Engineering and Automation Tianjin China Avic Xi'an Flight Automatic Control Research Institute National Key Laboratory of Science and Technology on Aircraft Control Xi'an China

ISBN: (纸本)9798350366600

3D object detection is crucial for autopilot and augmented reality applications. However, accurate feature extraction is the key to achieve high-precision detection, but traditional methods face the challenges of information loss and computational inefficiency. To address this, we propose an enhanced feature extraction method. We first divide the 3D space into voxel grids of varying scales. Then, using 3D sparse convolution, we extract features which are compressed along the Z axis to form BEV (Bird's Eye View) feature images. An efficient multi-scale attention mechanism is then employed to enhance these BEV features. The refined feature map is subsequently located and classified through a CT-stacking module and RPN (Region Proposal Network). Voxel features from the proposal and 3D backbone are passed through a voxel RoI pool to directly extract RoI features from the 3D features for further refinement. Finally, a cascaded attention network refines the proposal and strengthens confidence prediction. Experiments on test set validate our method effectiveness, showing improved accuracy over traditional voxel methods. Overall, this enhanced feature extraction technique offers a promising solution for 3D target detection, particularly in scenarios demanding high real-time performance and precision. © 2024 IEEE.

关键词： Feature extraction

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An Approach to Formation control of UAVs Based on Applying Adapted Kohonen Neural Network

An Approach to Formation Control of UAVs Based on Applying A...

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2023 IEEE Ural-Siberian Conference on Biomedical engineering, Radioelectronics and Information Technology, USBEREIT 2023

作者： Khachumov, Mikhail Ailamazyan Program Systems Institute of Ras Intelligent Control Laboratory Pereslavl-Zalessky Russia Federal Research Center' Computer Science and Control' of Ras Moscow Russia

ISBN: (纸本)9798350336054

The paper gives a statement and considers the solution of an urgent scientific problem of formation control for a group of unmanned aerial vehicles (UAVs) operating in an unstable environment. To construct the reference trajectories and allocate UAVs to positions of a given structure, an original approach is proposed based on the adapted Kohonen neural network with a set of metrics, including Euclidean, Mahalanobis and Euclidean-Mahalanobis distances. To implement the movement of a UAV group in a complex environment, we apply the principles of intelligent-geometric control, which allows to combine flexible intelligent and precise geometric control methods within one concept. When moving along a given route, UAVs use a set of allowable control strategies. The developed modeling scheme is designed to take into account wind loads and possibly dangerous rapprochement of vehicles. To reflect the dynamics of UAVs, a special module is used that contains transfer functions integrated into a single stabilization system with an autopilot. The proposed approach has shown promise when simulating a series of formation control problems. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Diminishing ether-oxygen content of electrolytes enables temperature-immune lithium metal batteries

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science China Chemistry 2023年第9期66卷 2700-2710页

作者： Tao Liu Jingnan Feng Zhe Shi Huajun Li Weina Zhao Minglei Mao Xiangzhen Zhu Yong-Sheng Hu Hong Li Xuejie Huang Liquan Chen Liumin Suo Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101China Department of Nuclear Science and Engineering and Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control Guangdong Key Laboratory of Environmental Catalysis and Health Risk ControlSchool of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China

The reversibility of lithium(Li) metal anodes is highly susceptible to temperature,owing to the aggravated side reactions at high temperatures and serious Li dendrite growth at low *** it is extremely challenging to simultaneously realize the high Li reversibility in both low and high temperature ***,an oxygen-free solvent(n-hexane,HEX) assisted with the hexyl methyl ether and 1 mol L^(-1)lithium bis(fluorosulfonyl)imide is proposed to constitute an electrolyte for temperatureimmune lithium metal *** demonstrates that the HEX not only greatly suppresses the solvent reduction even at high temperatures but also weaken the Li~+-solvent interaction for the facile Li-ion desolvation,leading to high Li Coulombic efficiencies(99.59% at 25℃,99.30%at 60℃ and 98.75% at -30℃) and the dendrite-free Li plating from -30℃ to 60℃.Benefitting from the low density and temperature-immune properties of our electrolyte,the sulfurized polyacrylonitrile(3.8mAh cm^(-2))||Li(60 μm) pouch-cells deliver 278 Wh kg^(-1)energy density and maintain the stable performance over 50 cycles,and retain 248 and 320 Wh kg^(-1) energy density at -30℃ and 60℃,*** work provides a new perspective on the electrolyte design for wide-temperature Li metal batteries.

关键词： wide temperature range oxygen-free solvent dendrite-free Li plating high Coulombic efficiencies lithium metal batteries

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Where to Turn: Road Fork Detection in Sparse 3D Point Cloud 3rd

Where to Turn: Road Fork Detection in Sparse 3D Point Clou...

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Hu, Di Zhang, Kai Zhong, Yipan Xu, Jiachen Yuan, Xia Zhao, Chunxia School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Research Institute of Intelligence Southwest Research Institute of Information Control Chengdu611700 China

ISBN: (纸本)9789819711024

Intersection detection plays a crucial role in localizing and planning the path of autonomous vehicles in urban environments. This paper presents a novel approach, PVWO, for adaptive intersection detection in autonomous vehicles equipped with 3D LiDAR. Firstly, the algorithm removes ground and obstacles effectively, and eliminates noise points. Secondly, a polyarticular viewpoints beam model is applied to detect the type and location of intersections. Thirdly, a linear feature extraction approach in a rolling window is proposed to detect road width and optimize key parameters, enhancing model robustness across different scenes. Compared to parallel intersection detection algorithms, our method exhibits excellent performance under diverse road conditions. Experiments in sparse point clouds show an average precision exceeding 89% and an average processing time of approximately 88 ms/frame. These results demonstrate that our method can accurately detect intersections in real-time, across various road structures. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Extraction

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Inhibition of jarosite heterogeneous crystallization on anglesite via in-situ formation of competitive substrate

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Journal of Environmental sciences 2025年第3期149卷 394-405页

作者： Jiahui Wu Xu Yan Feiping Zhao Yong Ke Haiying Wang Wenchao Zhang Qingwei Wang Meiqing Shi Liyuan Chai School of Metallurgy and Environment Central South UniversityChangsha 410083China Chinese National Engineering Research Center for Control&Treatment of Heavy Metal Pollution Changsha 410083China State Key Laboratory of Advanced Metallurgy for Non-ferrous Metals Changsha 410083China

Heterogeneous crystallization is a common occurrence during the formation of *** leads to the encapsulation of valuable/hazardous metals within the primary phase,presenting significant challenges for waste treatment andmetal ***,we proposed a novel method involving the in-situ formation of a competitive substrate during the precipitation of jarosite waste,which is an essential process for removing iron in zinc *** observed that the in-situ-formed competitive substrate effectively inhibits the heterogeneous crystallization of jarosite on the surface of anglesite,a lead-rich phase present in the *** a result,the iron content on the anglesite surface decreases from34.8%to 1.65%.The competitive substrate was identified as schwertmannite,characterized by its loose structure and large surface ***,we have elucidated a novel mechanism underlying this inhibition of heterogeneous crystallization,which involves the local supersaturation of jarosite caused by the release of ferric and sulfate ions from the competitive *** local supersaturation promotes the preferential heterogeneous crystallization of jarosite on the competitive ***,during the formation of jarosite,the competitive substrate gradually vanished through a dissolution-recrystallization process following the Ostwald rule,where a metastable phase slowly transitions to a stable *** effectively precluded the introduction of impurities and reduced waste *** goal of this study is to provide fresh insights into the mechanism of heterogeneous crystallization control,and to offer practical crystallization strategies conducive to metal separation and recovery from solid waste in industries.

关键词： Heterogeneous crystallization Jarosite waste Competitive substrate Zinc hydrometallurgy

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Machine learning-driven optimization of plasma-catalytic dry reforming of methane

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Journal of Energy Chemistry 2024年第9期96卷 153-163页

作者： Yuxiang Cai Danhua Mei Yanzhen Chen Annemie Bogaerts Xin Tu Department of Electrical Engineering and Electronics University of LiverpoolLiverpool L693GJUK Research Group PLASMANT Department of ChemistryUniversity of AntwerpUniversiteitsplein 1BE-2610 Wilrijk-AntwerpBelgium College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816JiangsuChina

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental *** address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML *** ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM *** model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM *** found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/***,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM *** results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction *** hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

关键词： Plasma catalysis Machine learning Process optimization Dry reforming of methane Syngas production

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Epidemiological Features and Spatial-Temporal Clustering of Visceral Leishmaniasis—China,2011–2022

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China CDC weekly 2024年第46期6卷 1201-1205页

作者： Zhengbin Zhou Zhuowei Luo Gaiqin Pan Yuanyuan Li Zhongqiu Li Qin Liu Yuwan Hao Limin Yang Zelin Zhu Qiang Wang Yi Zhang Shizhu Li National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases National Institute of Parasitic Diseases at Chinese Center for Disease Control and PreventionChinese Center for Tropical Diseases ResearchNHC Key Laboratory of Parasite and Vector BiologyWHO Collaborating Center for Tropical DiseasesNational Center for International Research on Tropical DiseasesMinistry of Science and TechnologyChina Beijing Center for Disease Control and Prevention BeijingChina Liaocheng People’s Hospital Liaocheng CityShandong ProvinceChina School of Global Health Chinese Center for Tropical Diseases Research-Shanghai Jiao Tong University School of MedicineShanghaiChina

What is already known about this topic?Visceral leishmaniasis(VL),transmitted by sandflies,is a zoonotic disease of public health importance in central and western *** is added by this report?A number of VL hotspots were identified in the border areas of Shanxi-Hebei,Shanxi-Shaanxi,Gansu-Sichuan,as well as the southern Xinjiang provinciallevel administrative division(PLAD).Mountain zoonotic visceral leishmaniasis(MT-ZVL)expanded rapidly in the mountainous regions of Shaanxi,Shanxi,Henan,Hebei,Beijing,and Gansu PLADs from 2011 to 2022.A notable resurgence of MT-ZVL has occurred,with 671 cases in 66 historically endemic *** are the implications for public health practice?Actions are recommended to restrain the rapid expansion of MT-ZVL:Firstly,implement One Health approaches,such as timely diagnosis and treatment of patients,promoting insecticide-impregnated dog collars,and restraining the number of dogs in endemic ***,surveillance-response systems for sandflies and infective dogs in potential risk areas should be strengthened.

关键词： restrain expanded Spatial

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Effect of Mold Wall Thickness on the Defect Band of AlSi10MgMn in High-Pressure Die Casting Technology 75

Effect of Mold Wall Thickness on the Defect Band of AlSi10Mg...

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75th World Foundry Congress, WFC 2024

作者： Sun, Zhenyu Yu, Wenbo Li, Junjie Zheng, Weichen Xiong, Shoumei Center of Materials Science and Engineering School of Mechanical and Electronic Control Engineering Beijing Jiaotong University Beijing100044 China School of Materials Science and Engineering Tsinghua University Beijing100084 China

In order to investigate the defect band within the casting, the design of a stepped mold with a thickness gradient of 1mm (the thickness of its five stepped areas were 5mm, 4mm, 3mm, 2mm and 1mm), the selection of AlSi10MgMn alloys to carry out high pressure die casting experiments. Statistical analysis reveals that the band thicknesses of 2mm 、 3mm and 4mm die castings were measured to be in the range 7 - 18 mean grains wide, which is substantial evidence on that defect bands form due to strain localization in partially solidified alloys during HPDC. In addition, the defect band of 1mm, 2mm and 5mm areas are relatively indistinct. For 1mm and 2mm area, the filling and solidification process of castings plays a deterministic role. But for 5mm area, the distribution of ESCs in band is the key reason. At the same, the distribution of ESCs also makes the defect bands in the 3mm and 4mm regions well defined. Finally, no additional porosity occurs in the AlSi10MgMn alloys, which display defect bands of positive macrosegregation. © 2024 75th World Foundry Congress, WFC 2024. All rights reserved.

关键词： Die casting

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