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Characteristics of Labile Organic Carbon Fractions Under Different Types of Subsidence Waterlogging Areas in the Coal Mining Area with High Ground Water Table: A Case Study in Xinglongzhuang Coal Mine, China

SSRN

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SSRN 2023年

作者： Fu, Yanhua Hu, Zhenqi Zhu, Qi Rong, Ying School of Economics and Management Tianjin Chengjian University Tianjin300384 China School of Environment Science and Spatial Informatics China University of Mining and Technology Xuzhou221116 China Chinese Research Academy of Environmental Sciences National Engineering Laboratory for Lake Pollution Control and Ecological Restoration State Environmental Protection Key Laboratory for Lake Pollution Control Beijing100012 China The Research Center of System and Industrial Technology Zhongyuan University of Technology Zhengzhou451191 China

The restoration of subsidence waterlogging areas has the potential to facilitate carbon accumulation in the coal mining areas with high ground water table. Nevertheless, the impact of restoration and management strategy on the soil organic carbon (SOC) and the labile organic carbon(LOC) have not been sufficiently demonstrated. In this study, SOC and LOC fractions, including light fraction organic carbon(LFOC), particulate organic carbon(POC), and dissolved organic carbon(DOC) within the 0-50cm profiles, were determined in the reference cropland(RC) and three types of subsidence waterlogging areas including aquaculture waterlogging area(AWA), deep waterlogging area(DWA) and shallow waterlogging area(SWA) in Xinglongzhuang Coal Mine, Shandong Province, China. Our results showed that compared with the reference cropland SOC and LOC decreased by 4.13%, 25.03%, and 77.91% in SWA, DWA, and AWA, respectively. The vital depletion was in the 0-10cm soil layer, which indicates that the recovery of total functional pools of SOC after subsidence and the succession of ecological systems takes a longer time. The liability of POC was highest among the three types of labile organic fractions which could indicate the mining subsidence triggered the soil redistribution and impact the carbon accumulation. With comparatively less interference on the soil profile, the liability of LFOC and POC decreased with soil depth, while DOC increased in DWA and SWA. And there was a large discrepancy in AWA, with the liability of LOC fractions fluctuating because of intensive engineering soil disturbance. Labile organic C fractions were closely associated and correlated with soil properties, especially TN and TP. Overall, the restoration and management have a vital impact on the soil organic carbon pool, it is suggested that shallow water, natural geomorphology-based topography, less soil disturbance, well development of vegetation communities, and continuous monitoring of soil and water properties

关键词： Subsidence

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Vertical Transport of Ultrafine Particles and Turbulence Evolution Impact on New Particle Formation at the Surface & Canton Tower

SSRN

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SSRN 2023年

作者： Wu, Hao Li, Zhanqing Hai, Shangfei Gao, Yang Jiang, Jingkun Zhao, Bin Cribb, Maureen Zhang, Dongmei Pu, Dongyang Liu, Mengqi Wang, Chunling Lan, Jing Wang, Yuying Key Laboratory of China Meteorological Administration Atmospheric Sounding School of Electrical Engineering Chengdu University of Information Technology Chengdu610225 China Department of Atmospheric and Oceanic Science University of Maryland College ParkMD20742 United States College of Oceanic and Atmospheric Sciences Ocean University of China Qingdao266100 China Frontiers Science Center for Deep Ocean Multispheres and Earth System Key Laboratory of Marine Environmental Science and Ecology Ministry of Education Ocean University of China Qingdao266100 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing100084 China College of Global Change and Earth System Science Beijing Normal University Beijing100875 China Guangzhou Climate and Agrometeorology Center Guangzhou511430 China Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration Nanjing University of Information Science & Technology Nanjing210044 China

New particle formation (NPF) is a key process occurring in the planetary boundary layer (PBL). Newly formed particles are an important source of aerosols and cloud condensation nuclei (CCN) that influence clouds and climate, while the distribution of these new particles at different altitudes has rarely been studied. In-situ measurements of ultrafine particles (UFP) observed at the ground and at the top of the Canton Tower (454 m) located in downtown Guangzhou in southern China were analyzed using the measurements of multiple meteorological and physicochemical quantities, as both observed during a field campaign and simulated with the WRF-chem model. We found that turbulence and NPF characters vary considerably with heights, with UFP concentration diminishing by half from the surface to the tower top. This indicates the UFP transports upward from the ground in the lower boundary layer. A consistent relationship is established between the occurrences of NPF and the evolution of turbulence. The correlation between the exchange ratio at the tower top has correlated well with nucleation growth, suggesting that turbulence can play an important role in the episodes of NPF growth, whose growth rate is closely related to the turbulence exchange ratio, effectively dictating the ultrafine particle concentration before and during the lockdown period. A new mechanism is thus hypothesized: NPF happens eailer near the surface and grows faster at the upper PBL, attributed to condensable vapors being transported by turbulent vertical mixing in the boundary layer. Model simulations using the WRF-Chem model reveal that the exchange ratio changed the NPF parameters, supporting the proposed mechanism that the evolution of the PBL variation has a significant impact on NPF, which should not be omitted in the NPF research, since this physical factor could be a dominant one in the NPF mechanism. © 2023, The Authors. All rights reserved.

关键词： Turbulence

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Parameter estimation survey for multi-joint robot dynamic calibration case study

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Science China(Information Sciences) 2019年第10期62卷 102-116页

作者： Shaolin ZHANG Shuo WANG Fengshui JING Min TAN The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Accurate model parameters are the basis of robot dynamics. Many linear and nonlinear models have been proposed to calibrate the inertial parameters and friction parameters of multi-joint ***, methods of choosing a model and calculating its parameters still have few summaries. This paper reviews typical linear/nonlinear models and different calculation methods for robot dynamic *** simulations, the features of different methods are analyzed, including torque error, parameter error, model adaptability, solution time, and anti-interference ability of the calibration results. Finally, an experiment performed on a six-degree-of-freedom industrial manipulator is used as an example to illustrate how to select the model for a specified robot. These comparisons and experiments provide references for the parameter calibration of multi-joint robots.

关键词： dynamic parameter calibration friction calibration robot dynamics industrial manipulator dynamic models

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Hand Position Tracking based on Optimized Consistent Extended Kalman Filter

Hand Position Tracking based on Optimized Consistent Extende...

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Chinese control and Decision Conference, CCDC

作者： Lin Tian Wenchao Xue Long Cheng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Key Laboratory of Systems and Control National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665478977

This paper proposes a hand position tracking algorithm based on optimized consistent extended Kalman filter (CEKF). By introducing the previous work of the authors and analyzing the parameter of the original CEKF algorithm, the key parameter that is negatively correlated with the degree of the estimation of uncertain dynamics is determined. Then, two metaheuristic methods, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) are used to optimize the original CEKF algorithm. To quantify the performance of the algorithms, the root-mean-square error (RMSE) is employed as the performance index. Finally, the numerical simulation and practical experiment of the hand position tracking are carried out, and the optimized algorithm achieves 9.52% and 10.94% improvements of the performance, respectively.

关键词： Heuristic algorithms Metaheuristics Stability criteria Estimation Numerical simulation Performance analysis Kalman filters

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A Novel Two-Stage Framework for 2D/3D Registration in Neurological Interventions

A Novel Two-Stage Framework for 2D/3D Registration in Neurol...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Huang, De-Xing Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Ma, Ning Yan, Long The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa China The Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing100070 China

ISBN: (纸本)9781665481090

2D/3D medical image registration of pre-operative volumes and intra-operative images plays an important role in neurological interventions. However, vast space of transformation parameters makes this task incredibly challenging. In this paper, a novel two-stage framework for 2D/3D registration is proposed. It consists of two basic modules: CNN regression for preliminarily estimating transformation parameters, and centroid alignment for further reducing estimation errors to get refined images. Experimental results on two patients show the proposed framework can achieve superior performances to baseline methods with an inference rate of 22 FPS, satisfying real-time requirements (612 FPS) in clinical practice. Extensive ablation studies prove the effectiveness of centroid alignment in significantly improving registration accuracies. These promising results indicate the framework has the potential to be integrated into navigation frameworks for neurological interventions, facilitating treatments of physicians. © 2022 IEEE.

关键词： Medical imaging

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Air pollution characteristics and their relationship with emissions and meteorology in the Yangtze River Delta region during 2014–2016

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Journal of Environmental Sciences 2019年第9期31卷 8-20页

作者： Tao Ma Fengkui Duan Kebin He Yu Qin Dan Tong Guannan Geng Xuyan Liu Hui Li Shuo Yang Siqi Ye Beiyao Xu Qiang Zhang Yongliang Ma State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing Key Laboratory of Indoor Air Quality Evaluation and Control Tsinghua University Beijing 100084 China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing 100084 China National Satellite Meteorological Center Beijing 100081 China College of Resources and Environmental Sciences China Agricultural University Beijing 100094 China

With rapid economic growth and urbanization, the Yangtze River Delta(YRD) region in China has experienced serious air pollution challenges. In this study, we analyzed the air pollution characteristics and their relationship with emissions and meteorology in the YRD region during 2014–2016. In recent years, the concentrations of all air pollutants, except O3,decreased. Spatially, the PM2.5, PM10, SO2, and CO concentrations were higher in the northern YRD region, and NO2 and O3 were higher in the central YRD region. Based on the number of non-attainment days(i.e., days with air quality index greater than 100), PM2.5 was the largest contributor to air pollution in the YRD region, followed by O3, PM10, and ***, particulate matter pollution has declined gradually, while O3 pollution *** conditions mainly influenced day-to-day variations in pollutant concentrations. PM2.5 concentration was inversely related to wind speed, while O3 concentration was positively correlated with temperature and negatively correlated with relative *** air quality improvement in recent years was mainly attributed to emission *** 2014–2016, PM2.5, PM10, SO2, NOx, CO, NH3, and volatile organic compound(VOC)emissions in the YRD region were reduced by 26.3%, 29.2%, 32.4%, 8.1%, 15.9%, 4.5%, and0.3%, respectively. Regional transport also contributed to the air pollution. During regional haze periods, pollutants from North China and East China aggravated the pollution in the YRD region. Our findings suggest that emission reduction and regional joint prevention and control helped to improve the air quality in the YRD region.

关键词： Air pollution Spatiotemporal variation Meteorological condition Emission inventory Regional transport Yangtze River Delta

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Development and initial experiments of an intelligent dual-arm mobile robot - Baymax-I

Development and initial experiments of an intelligent dual-a...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Liu, Wei Wang, Haitao Wang, Rui Wen, Dayong Lu, Tao Wang, Shuo Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9781665436786

This paper addresses the design and control of an intelligent dual-arm mobile robot named Baymax-I. The mechanism design of Baymax-I is based on modular concepts, which is mainly composed of three parts, including main body, dual-arms and a mobile chassis. The main body is designed as human-like structure, carrying multiple sensors. UR3 manipulators are adopted as two arms. Each manipulator has six degrees of freedom. A two-finger grip pawl is mounted on the end of each manipulator. A MIR mobile chassis drives the Baymax-I motion with high maneuverability. As for software systems, the robot is designed based on hierarchical structure. The bottom driving layer is used to perform specific movements. The middle layer is responsible for processing sensor data. The top layer is algorithm execution layer. In the end, three experiments including SLAM, pressing the elevator and opening the fridge are presented to verify the feasibility and effectiveness of the proposed system design and control methods of the Baymax-I robot. © 2021 IEEE.

关键词： Mobile robots

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A novel CNN model for fine-grained classification with large spatial variants 5

A novel CNN model for fine-grained classification with large...

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2020 5th International Conference on Intelligent Computing and Signal Processing, ICSP 2020

作者： Wang, Junpeng Lu, Yanfeng State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

Convolutional Neural Networks (CNN) have achieved great performance in many visual tasks. However, CNN models are sensitive to samples with large spatial variants, especially severe in fine-grained classification task. In this paper, we propose a novel CNN model called ST-BCNN to solve these problems. ST-BCNN contains two functional CNN modules: Spatial Transform Network (STN) and Bilinear CNN(BCNN). Firstly, STN module is used to select key region in input samples and get it spatially modified. Since the adoption of STN will cause an information loss phenomenon called boundary loss, we design a brand-new IOU loss method to solve it. We make a theoretical analysis of the IOU loss method. Secondly, to discover discriminative features for fine-grained classification task, BCNN module is applied. BCNN interacts CNN features from different channels to produce more discriminative bilinear features than fully connected features of CNN. ST-BCNN works by reducing irrelevant spatial states and producing fine-grained features. We evaluate our model on 3 public fine-grained classification datasets with large spatial variants: CUB200-2011, Fish100 and UAV43. Experiments show that the IOU loss method can reduce boundary loss and make STN module output spatial transformed image appropriately. Our proposed ST-BCNN model outperforms other advanced CNN models on all three datasets. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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EiHi Net: Out-of-Distribution Generalization Paradigm

arXiv

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arXiv 2022年

作者： Wei, Qinglai Yuan, Beiming Chen, Diancheng State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Systems Engineering Macau University of Science and Technology 999078 China

This paper develops EiHi to solve the out of distribution (O.o.D.) problem in recognition, especially the distribution shift of background. EiHi net is a model learning paradigm that can be blessed on any visual backbone. Different from the traditional deep model, namely find out correlated relationship between sample features and corresponding categories, EiHi net suffers less from spurious correlations. Fusing SimCLR and VIC-Reg via explicit and dynamical establishment of original - positive - negative sample pair as a minimal learning element, EiHi net iteratively establishes a relationship close to the causal one between features and labels, while suppresses the spurious correlations. To further reinforce the ability of the proposed model on causal reasoning, we develop a human-in-the-loop strategy, with few guidance samples, to prune the representation space directly. Finally, it is shown that the developed EiHi net makes significant improvements in the most difficult and typical O.o.D. dataset NICO, compared with the current SOTA results, without any domain (e.g. background) information. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Empirical policy optimization for n-player Markov games

arXiv

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arXiv 2021年

作者： Zhu, Yuanheng Zhao, Dongbin Zhao, Mengchen Li, Dong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Huawei Noah's Ark Lab Beijing China

In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with environment. In multi-player Markov games (MGs), however, the interaction is non-stationary due to the behaviors of other players, so the agent has no fixed optimization objective. In this paper, we treat the evolution of player policies as a dynamical process and propose a novel learning scheme for Nash equilibrium. The core is to evolve one's policy according to not just its current in-game performance, but an aggregation of its performance over history. We show that for a variety of MGs, players in our learning scheme will provably converge to a point that is an approximation to Nash equilibrium. Combined with neural networks, we develop the empirical policy optimization algorithm, that is implemented in a reinforcement-learning framework and runs in a distributed way, with each player optimizing its policy based on own observations. We use two numerical examples to validate the convergence property on small-scale MGs with n ≥ 2 players, and a pong example to show the potential of our algorithm on large games. © 2021, CC BY-NC-ND.

关键词： Computation theory

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