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Trends and drivers of anthropogenic NOx emissions in China since 2020

Environmental Science and Ecotechnology 2024年第5期21卷 287-293页

作者： Hui Li Bo Zheng Yu Lei Didier Hauglustaine Cuihong Chen Xin Lin Yi Zhang Qiang Zhang Kebin He Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and EcologyTsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084China State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation and Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing 100041China Laboratoire des Sciences du Climat et de l’Environnement LSCE/IPSLCEA-CNRS-UVSQUniversite Paris-SaclayGif-sur-YvetteFrance Center for Satellite Application on Ecology and Environment Ministry of Ecology and Environment of ChinaBeijing 100094China Institute of Future Human Habitats Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Nitrogen oxides(NOx),significant contributors to air pollution and climate change,form aerosols and ozone in the ***,timely,and transparent information on NOx emissions is essential for decision-making to mitigate both haze and ozone ***,a comprehensive understanding of the trends and drivers behind anthropogenic NOx emissions from Chinadthe world's largest emitterdhas been lacking since 2020 due to delays in emissions *** we show a consistent decline in China's NOx emissions from 2020 to 2022,despite increased fossil fuel consumption,utilizing satellite observations as constraints for NOx emission estimates through atmospheric *** reduction is corroborated by data from two independent spaceborne instruments:the TROPOspheric Monitoring Instrument(TROPOMI)and the Ozone Monitoring Instrument(OMI).Notably,a reduction in transport emissions,largely due to the COVID-19 lockdowns,slightly decreased China's NOx emissions in *** subsequent years,2021 and 2022,reductions in NOx emissions were driven by the industry and transport sectors,influenced by stringent air pollution *** satellite-based inversion system developed in this study represents a significant advancement in the real-time monitoring of regional air pollution emissions from space.

关键词： China's NOx emissions Pollution control Socio-economic drivers Atmospheric inversion

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Tissue Surface-enhanced Raman Spectroscopy Combined with Machine Learning Algorithms for Rapid Detection of Alveolar Echinococcosis

Tissue Surface-enhanced Raman Spectroscopy Combined with Mac...

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system Simulation Technology and its application (CCSSTA), IEEE China Conference on

作者： Jiahui Huang Xiangxiang Zheng Guodong Lv Xiaojing Li Guohua Wu Liang Xu Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China State Key Laboratory of Pathogenesis Prevention and Treatment of High Incidence Diseases in Central Asia The First Affiliated Hospital of Xinjiang Medical University Urumqi China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Alveolar echinococcosis (AE) remains a serious worldwide public health problem as a very harmful human tapeworm disease. Rapid and accurate diagnosis of AE is crucial for treatment and postoperative recovery. This study explored the applicability of tissue surface Raman spectroscopy (SERS) combined with machine learning for rapid AE detection. Specifically, liver tissue section samples were obtained by creating an AE mouse model and SERS spectra were acquired. Five kinds of machine learning methods were used to construct AE diagnosis model, among which the linear kernel support vector machine (SVM) model had the best classification result, and the accuracy rate reached $95.7 \%$. Then, the linear SVM model was subjected to feature significance analysis, which demonstrated that the biochemicals corresponding to the spectra of 641,716 and $1372 \mathrm{~cm}^{-1}$ might be potential biomarkers. Gaussian noise is further added to test the noise interference resistance of different machine learning algorithms, and it is found that SVM with Gaussian radial basis function (RBF) kernel function has better noise interference resistance. The results show that tissue SERS spectroscopy combined with SVM algorithm for rapid detection of AE has a promising application.

关键词： Support vector machines Analytical models Machine learning algorithms Accuracy Biological system modeling Surface resistance Raman scattering Interference Machine learning Kernel

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Image Segmentation Method of Crop Diseases Based on Improved Segnet Neural Network 17

Image Segmentation Method of Crop Diseases Based on Improved...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Yue, Youjun Li, Xuesong Zhao, Hui Wang, Hongjun Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin300384 China

ISBN: (纸本)9781728164151

In crop disease image segmentation, traditional convolutional neural networks have the problem of low image accuracy. For this reason, this paper proposes a segmentation method that combines conditional random fields with segnet networks. First, input the training set in the data set into the segnet network for training, and obtain the initial segmentation result and the network with updated parameters;secondly, the image segmented by the segnet network is sent to the conditional random field in the form of pixels and classification vectors corresponding to the pixels At the same time, construct an energy function to represent the relative relationship between pixels, and then keep training;in order to get the conditional random field in the training set, you need to adjust and optimize the image segmented by the segnet network to get it;then in the verification set, the segnet network and the weight parameters of the conditional random field model are further adjusted and optimized to obtain the final conditional random field and segnet network;finally, the final conditional random field and segnet network are applied to the crop disease image segmentation. The test results show that compared with the Otsu threshold and Grabcut segmentation methods, the segmentation accuracy P, recall rate R, and comprehensive evaluation index F of this method are all the maximum, and the average processing time of a single image is 0.86 s;compared with pspnet and deeplab methods, Under the premise that the segmentation accuracy P, the recall rate R, and the comprehensive evaluation index F are basically unchanged, the average processing time of a single image is significantly reduced. The results show that the best comprehensive effect can be achieved by segmenting crop disease images in complex environments using the method in this paper. © 2020 IEEE.

关键词： Image segmentation

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Fault Type identification in Transmission Lines based on VMD-FE and IGWO-KELM 17

Fault Type identification in Transmission Lines based on VMD...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Yue, Youjun Li, Lixin Wang, Hongjun Zhao, Hui Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin300384 China

ISBN: (纸本)9781728164151

In view of the damage of transmission line short-circuit faults and low fault identification rate, A novel fault identification method based on variational mode decomposition-fuzzy entropy and improved grey wolf algorithm - kernel extreme learning machine is proposed. First, the VMD is used to decompose the three-phase current after the fault to obtain a series of three-phase stable modal components;secondly, the fuzzy entropy value of each group of components is calculated separately as the fault characteristics of the transmission line;Finally, The fault characteristics of the transmission line are input to the kernel extreme learning machine for training to obtain the diagnosis model. The experimental results show that the method is not affected by the distance of the fault, the initial phase angle of the fault and the transition resistance, and can accurately identify the fault type. © 2020 IEEE.

关键词： Variational mode decomposition

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Research on the control system of six joint industrial robot based on CPAC 17

Research on the control system of six joint industrial robot...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Dong, Chao Xie, Bang Shao, Xin Complex System Control Theory and Application Key Laboratory 391 Binshui Xidao Xiqing District Tianjin300384 China

ISBN: (纸本)9781728164151

The defects of the existing commercial robot control system are analyzed and the feasibility of the open control system is studied. The robot control system was designed based on the CPAC development platform with the six-joint robot as the research object. Firstly, a kinematic model of the robot was established by the d-h linkage coordinate system method, and its motion control was described in detail from two aspects of forward and inverse kinematics. Secondly, the inverse kinematics of the robot was solved by using the RPY description method and PIPER criterion, and the correctness of the robot kinematics model was verified. Third, the robot control system software design. Finally, the function modules of the control system are verified through experiments. The experiments show that the robot runs with a small error of pose and posture, which can meet the requirements of the control system and meet the expected design objectives. © 2020 IEEE.

关键词： control systems

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The junction temperature measurement of insulated gate bipolar transistor based on multi-layer feed -forward neural network is presented 17

The junction temperature measurement of insulated gate bipol...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Dong, Chao Mao, Peiye Complex System Control Theory and Application Key Laboratory 391 Binshui Xidao Xiqing District Tianjin300384 China

ISBN: (纸本)9781728164151

For large power flow type insulated gate bipolar transistor (IGBT) saturation voltage drop and non-linear relation between the collector current and junction temperature of junction temperature prediction problem, set up a big electric saturated pressure drop down IGBT test system, access to settle moderate collector current and saturation nonlinear relation between the pressure drop curve, analyses the relation curve change rule the corresponding physical mechanism. The error back propagation (BP) neural network model was established by using Matlab software to predict the junction temperature. The comparison with the prediction results of the polynomial mathematical model shows that the relative error and prediction error of the neural network model are smaller than that of the polynomial mathematical model, and the prediction accuracy of the junction temperature is higher. It shows the feasibility of neural network for junction temperature prediction, so BP neural network model can be given priority when selecting junction temperature prediction model. © 2020 IEEE.

关键词： Junction temperature

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application of Difficult Sample Mining based on Cosine Loss in Face Recognition 17

Application of Difficult Sample Mining based on Cosine Loss ...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Dong, Enzeng Qiao, Yifan Zhang, Zufeng Complex System Control Theory and Application Key Laboratory 391 Binshui Xidao Xiqing District Tianjin300384 China

ISBN: (纸本)9781728164151

Due to the development of deep convolutional neural networks, face recognition has made great progress, and its main goal is how to improve feature recognition capabilities. In this regard, several loss functions based on angular boundaries have been proposed to increase the feature margin between different classes. Although very good results have been achieved in this direction, there are still some problems. These loss functions only expand the feature margin from the perspective of real classification in training, and do not provide distinguishability for misclassified samples. In order to solve this problem, this paper improves on the original cosine loss function, and implements feature learning in the direction of difficult samples based on misclassified feature vectors. © 2020 IEEE.

关键词： Face recognition

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Short Term Power Load Forecasting Method Based on VMD-ResNet

Short Term Power Load Forecasting Method Based on VMD-ResNet

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International Conference on Power and Energy Technology (ICPET)

作者： Dahua Li Wenkai Chen Sun Wang He Tian Hao Chen Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China Tianjin Tongshi Zhiyan Technology Co. Ltd Tianjin China State Grid Tianjin Jinghai Power Supply Co. Ltd Tianjin China

The load of a region is often influenced by multiple factors, resulting in strong nonlinearity and temporal characteristics of load data, which increases the difficulty of accurately predicting load. To improve the accuracy of power load forecasting, this paper proposes a short-term power load forecasting method based on VMD-ResNet. Firstly, at the data level, the Variational Modal Decomposition (VMD) method is used to decompose historical loads and temperatures to obtain a more stable Intrinsic Mode Function (IMF). Then, different IMFs are integrated and input into the first stage prediction model along with seasonal and holiday information to extract internal features of the data for preliminary prediction; The second stage is a Deep Residual Network (ResNet) that uses the forecast data from the first stage as input to predict the load over the next 24 hours. The data adopts real load data from Panama City from January 2015 to June 2020. The average absolute percentage error (MAPE) of the VMD-ResNet load forecasting model is 1.3%. Compared with load forecasting networks such as SVR, LSTM, CNN-LSTM, ResNet, etc., the MAPE of the test set has decreased by 2.36%, 1.8%, 1.2%, and 0.8%, respectively. Experiments have shown that VMD-ResNet can deeply mine data characteristics and has good predictive performance.

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application of Hybrid Energy Storage Device in High Power Direct-current Power Supply 17

Application of Hybrid Energy Storage Device in High Power Di...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Liu, Yue Wang, Chunjie Chen, Peng Yin, Jinliang School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin Complex System Control Theory and Application Key Laboratory Tianjin China

ISBN: (纸本)9781728164151

The drop-away current of 1500V low-voltage direct-current circuit breaker used in metro DC traction power supply system needs to be checked regularly. The instantaneous power is very high when the detection device is working, and the field power supply is often difficult to meet the requirements. Therefore, an energy storage unit is needed to supply power to the detector. In this paper, a design scheme of high-power DC power supply with supercapacitor and battery as energy storage is proposed, and the energy management and discharge control strategies are studied in theory and simulation, which verify the effectiveness and feasibility of the scheme. © 2020 IEEE.

关键词： Drops

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Chaos Suppression of Power system Based on Finite-time Adaptive Sliding Mode control

Chaos Suppression of Power System Based on Finite-time Adapt...

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IEEE International Conference on Mechatronics and Automation

作者： Hongjun Wang Shuyi Li Hui Zhao Youjun Yue Key Laboratory of Control Theory and Application for Complex Systems in Tianjin Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China College of Engineering and Technology Tianjin Agricultural University 22 Jinjing Road Tianjin China

Under certain conditions, the power system will produce chaotic oscillation, which may impact the safety and stability of the power grid. Under the action of inductive load, the power system will generate a certain amount of reactive power. Excessive reactive power will lead to the deviation of the voltage phase angle of the power system. Usually, reactive compensation equipment is used to absorb the reactive power in the power system and adjust the voltage phase angle. In this article, the dynamics analysis of chaotic oscillation of the second-order power system model (SOPSM) is firstly carried out, and a global adaptive sliding mode controller based on finite time is designed by comprehensively using backstepping, fixed-time controller and adaptive sliding mode control methods (ASM), which not only realizes the suppression of chaotic oscillation of the power system, but also reduces the computational burden and improves the convergence speed of the system, Finally, MATLAB/Simulink numerical simulation shows that the designed controller can quickly restore the power system from chaotic state to equilibrium state, thus verifying the effectiveness and efficiency of the controller.

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