检索结果-内蒙古大学图书馆

δ^(15)N-stable isotope analysis of NH_(x): An overview on analytical measurements, source sampling and its source apportionment

引用

Frontiers of Environmental Science & Engineering 2021年第6期15卷 175-185页

作者： Noshan Bhattarai Shuxiao Wang Yuepeng Pan Qingcheng Xu Yanlin Zhang Yunhua Chang Yunting Fang State Key Joint Laboratory of Environmental Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Yale-NUIST Center on Atmospheric Environment Nanjing University of Information Science&TechnologyNanjing 210044China CAS Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China

Agricultural sources and non-agricultural emissions contribute to gaseous ammonia(NH_(3))that plays a vital role in severe haze *** and quantitative contributions of these sources to ambient PM_(2.5)(particulate matter with an aerodynamic equivalent diameter below 2.5µm)concentrations remains *** nitrogen isotopic composition(δ^(15)N)of NH_(3)and NH_(4)+(δ^(15)N(NH_(3))andδ^(15)N(NH_(4)+),respectively)can yield valuable information about its sources and associated *** review provides an overview of the recent progress in analytical techniques forδ^(15)N(NH_(3))andδ^(15)N(NH_(4)+)measurement,sampling of atmospheric NH_(3)and NH_(4)+in the ambient air and their sources signature(e.g.,agricultural *** fuel),and isotope-based source apportionment of NH_(3)in urban *** study highlights that collecting sample that are fully representative of emission sources remains a challenge in fingerprintingδ^(15)N(NH_(3))values of NH_(3)emission ***,isotopic fractionation during NH_(3)gas-to-particle conversion under varying ambient field conditions(e.g.,relative humidity,particle pH,temperature)remains unclear,which indicates more field and laboratory studies to validate theoretically predicted isotopic fractionation are ***,this study concludes that lack of refinedδ^(15)N(NH_(3))fingerprints and full understanding of isotopic fractionation during aerosol formation in a laboratory and field conditions is a limitation for isotope-based source apportionment of NH_(3).More experimental work(in chamber studies)and theoretical estimations in combinations of field verification are necessary in characterizing isotopic fractionation under various environmental and atmospheric neutralization conditions,which would help to better interpret isotopic data and our understanding on NH_(x)(NH_(3)+NH_(4)+)dynamics in the atmosphere.

关键词： Aerosol ammonium Atmospheric gaseous ammonia Isotope fingerprinting Isotope-based source apportionment Ammonia gas-to-particle conversion

来源：评论

学校读者我要写书评

暂无评论

Landslide Sensitivity Mapping Based on One-Dimensional Residual Convolutional Neural Networks

Landslide Sensitivity Mapping Based on One-Dimensional Resid...

引用

2022 Chinese automation Congress, CAC 2022

作者： Ke, Yougang Lu, Chengda Wu, Min Yu, Wanke Niu, Ruiqing School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Institute of Geophysics and Geomatics China University of Geosciences Wuhan China

ISBN: (纸本)9781665465335

Landslide is a common geological disaster. Landslide sensitivity mapping (LSM) is the key technology for landslide monitoring, early warning and risk assessment. Deep learning shows good performance in feature extraction. This paper proposes a one-dimensional residual convolution neural network (1DRCNN), which takes Yunyang County, Chongqing City, China, located in the Three Gorges Reservoir area as the research area. Extracting 12 evaluation factors from multi-source remote sensing data and building a training set. The spatial probability of landslide occurrence is quantitatively predicted by the proposed model, and finally, the landslide sensitivity mapping is generated. Compared with the common machine learning models SVM and logistic regression, the results show that the AUC (area under curve) and accuracy of 1D-RCNN are 0.9860 and 93.38%, respectively, which proving that this method is effective and can provide a reference for disaster prevention and reduction. © 2022 IEEE.

关键词： Landslides

来源：评论

学校读者我要写书评

暂无评论

A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

引用

IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

A Simplified Variant for Graph Convolutional Network Based Knowledge Graph Completion Model 8

A Simplified Variant for Graph Convolutional Network Based K...

引用

8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Wang, Yashen Li, Qiang Zhang, Yi Zhang, Xuecheng Ju, Zhuoya Sun, Jian Beijing China Scientific and Technological Innovation Center of Ari Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Cetc Academy of Electronics and Information Technology Group Co. Ltd. Beijing China Khoury College of Computer Science Northeastern University United States Scientific and Technological Innovation Center of Ari Beijing China Qi An Xin Technology Group Inc. Beijing China

ISBN: (纸本)9798350331035

Knowledge graphs (KGs) serve as useful resources for various applications including machine learning, data mining, and artificial intelligence. Knowledge Graph Completion (KGC) aims at reasoning over known (observed) facts and infer the missing (unobserved) relationships, to improve KG's coverage. Many recent works have laid stress on leveraging Graph Convolutional Networks (GCNs) for KGC task, which obtain good representation learning ability for graph-structured data, and have achieved satisfactory results. However, GCN-based researches continue to improve these lines of models by introducing an increasingly complex feature space. Especially, the hyper-parameter space of conventional GCN-based KGC model is too huge, resulting in low computational efficiency and excessive storage pressure. To overcome this problem, this paper investigates a simplified and generic variant for GCN-based KGC model, and shows that simple changes in the architecture of the underlying architecture (with a Subtraction Operator and an Addition Operator), can compte with state-of-the-art GCN-based models. Therefore, this paper proposes a easy-to-accomplish plug-in unit, which could be loaded into any current GCN-based KGC architectures, and help them maximize the maintenance of native model's original performance while significantly reducing parameter scale. © 2023 IEEE.

关键词： Graph Convolutional Network Knowledge Graph Knowledge Graph Completion Representation Learning

来源：评论

学校读者我要写书评

暂无评论

A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot

引用

Frontiers of Information Technology & Electronic Engineering 2022年第8期23卷 1217-1228页

作者： Manjun TIAN Xiali LI Shihan KONG Licheng WU Junzhi YU First Research Institute of the Ministry of Public Security of PRC Beijing 100048China School of Information Engineering Minzu University of ChinaBeijing 100081China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

To tackle the problem of aquatic environment pollution,a vision-based autonomous underwater garbage cleaning robot has been developed in our *** propose a garbage detection method based on a modified YOLOv4,allowing high-speed and high-precision object ***,the YOLOv4 algorithm is chosen as a basic neural network framework to perform object *** the purpose of further improvement on the detection accuracy,YOLOv4 is transformed into a four-scale detection *** improve the detection speed,model pruning is applied to the new *** virtue of the improved detection methods,the robot can collect garbage *** detection speed is up to 66.67 frames/s with a mean average precision(mAP)of 95.099%,and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.

关键词： Object detection Aquatic environment Garbage cleaning robot Modified YOLOv4

来源：评论

学校读者我要写书评

暂无评论

Oscillation Trajectory Optimization and control Method for Two-link Underactuated Manipulators

Oscillation Trajectory Optimization and Control Method for T...

引用

第42届中国控制会议

作者： Lejun Wang Huiming Wang Xianlun Tang Zixin Huang Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University School of Electrical and Information Engineering Wuhan Institute of Technology

This paper provides an oscillation trajectory optimization and control method for two-link underactuated manipulators(UMs) in a vertical *** proposed method solves the problem that the UMs cannot always enter the balance region in the partitioning ***,we establish the system dynamic model,and analyze the system couple ***,we program an oscillation trajectory for the active link,and use the intelligent method to obtain the trajectory parameters,so ensuring the system can reach the area adjacent to the target position through tracking ***,we design the controller to realize the stable control at the target ***,the simulation results show the effectiveness and generality of the control strategy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PCEB-YOLOV8: An Improved PCB Defect Detection Method Based on YOLOV8

PCEB-YOLOV8: An Improved PCB Defect Detection Method Based o...

引用

2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Meihui Li Chunhe Song Xia Yu Mengmeng Chu Rui Li Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Shenyang University of Technology Shenyang China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Printed circuit board defect recognition is of great significance to ensuring the quality of electronic products. This paper proposes an improved PCB defect detection method based on YOLOV8. Firstly, the data augmentation method PredMix (prediction mix) is adopted to simulate scenarios such as overlapping and occlusion in images, where the displays are incomplete or blurred, thereby enhancing the robustness of the network model. Secondly, BiFPN is used to replace PAN-FPN in the fusion layer to strengthen the feature fusion capability. Finally, the CESE_C2f module is employed in the Backbone part, leveraging a lightweight attention mechanism to improve the detection effect while reducing the number of parameters. Experimental results show that compared with the original YOLOV8, the MAP value of this improved algorithm reaches $\mathbf{92.6}\%$, with a $\mathbf{2.3\%}$ increase in accuracy.

关键词： Accuracy Costs Printed circuits Diversity reception Production Feature extraction Data augmentation Data models Real-time systems Defect detection

来源：评论

学校读者我要写书评

暂无评论

Extraction of main factors affecting muscle fatigue in lower-limb rehabilitation 41

Extraction of main factors affecting muscle fatigue in lower...

引用

第41届中国控制会议

作者： Tiecheng Liu Rennong Wang Jinhua She Juan Zhao Hiroshi Hashimoto Daisuke Chugo Graduate School of Engineering Tokyo University of Technology School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Industrial Technology Advanced Institute of Industrial Technology Graduate School of Science and Technology Kwansei Gakuin University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This study extracted the main factors from a questionnaire,measurement results,and the analysis of sEMG signals to establish a method of evaluating muscle *** measured factors(rating of perceived exertion,heart rate,mileage) and five factors obtained from the analysis of sEMG signals(root mean square,mean absolute value,variance,times of zero crossing,median frequency) were *** the F-test,the correlation analysis,and the principal component analysis,we finally extracted six factors as the main factors that have a big influence on muscle fatigue.

关键词： correlation analysis F-test lower-limb rehabilitation muscle fatigue principal component analysis rehabilitation statistical analysis surface electromyography(sEMG)

来源：评论

学校读者我要写书评

暂无评论

Optimization of the Landslide Susceptibility Model based on Deep Forest and Twice-Sample

Optimization of the Landslide Susceptibility Model based on ...

引用

2022 Chinese automation Congress, CAC 2022

作者： Yang, Yuelin Yu, Wanke Liu, Zepeng Zhao, Lingran Lu, Chengda Wu, Min Niu, Ruiqing China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China China University of Geosciences Institute of Geophysics and Geomatics Wuhan China

ISBN: (纸本)9781665465335

In the analysis of regional landslide susceptibility, the geological data used usually have the characteristics of multiplicity and nonlinearity. At the same time, in the past researches, the selection of non-landslide units in the training set is usually random or subjective, and there is no guarantee sample accuracy. In this paper, the deep forest algorithm combined with the twice-sample method is used to evaluate the landslide susceptibility in the Zigui Badong area of the Three Gorges Reservoir area. First, 11 landslide impact factors were selected according to the geological data to initially construct a sample set, and the cleaned data set was obtained by twice-sample. After dividing the training set and the test set, the deep forest model was used to predict the landslide susceptibility and evaluate the accuracy. The results show that the AUC of the susceptibility prediction model built using the deep forest is 1%-10% higher than that of GBDT, MLP and other methods, while the Twice-Sample method improves the AUC by 2%. © 2022 IEEE.

关键词： Landslides

来源：评论

学校读者我要写书评

暂无评论

Fast control Strategy of Grid-forming Inverter Based on Power Feedforward 42

Fast Control Strategy of Grid-forming Inverter Based on Powe...

引用

42nd Chinese control Conference, CCC 2023

作者： Zhang, Sheng Fang, Zhijian Liu, Qine Xiang, Muchao Huang, Dingbang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China State Grid Hubei Electric Power Co. Ltd Xiangyang Power Supply Company Xiangyang441021 China Electric Power Scientific Research Institute of State Grid Hubei Electric Power Co. Ltd Wuhan430072 China

ISBN: (纸本)9789887581543

Grid-forming inverter is widely used in grid-connected systems of distributed generation because of its frequency and voltage support capacity and good stability in microgrid, but its large inertia will affect the dynamic response speed of grid-forming inverter. In order to solve this problem, this paper introduces the loop that affects the dynamic response of grid-forming inverter, and carries out small signal modeling for active loop, analyzes the dynamic performance indicators and determinants of typical second-order system. Thus, a method of adding power feedforward coefficient to the forward channel of the power loop is designed, and the response speed of the system with or without feedforward coefficient under the unit step response is compared. Finally, the simulation results show that adding the power feedforward coefficient can improve the response speed of the grid-forming inverter during startup and power switching, then achieves the effect of fast control. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Dynamic response

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：