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Mapping anthropogenic emissions in China at 1 km spatial resolution and its application in air quality modeling

Science Bulletin 2021年第6期66卷 612-620,M0004页

作者： Bo Zheng Jing Cheng Guannan Geng Xin Wang Meng Li Qinren Shi Ji Qi Yu Lei Qiang Zhang Kebin He Institute of Environment and Ecology 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 China National Environmental Monitoring Center Beijing 100012China Center for Environmental Risk and Damage Assessment Chinese Academy of Environmental PlanningBeijing 100012China Center for Regional Air Quality Simulation and Control Chinese Academy of Environmental PlanningBeijing 100012China

New challenges are emerging in fine-scale air quality modeling in China due to a lack of high-resolution emission ***,only a few emission sources have accurate geographic locations(point sources),while a large part of sources,including industrial plants,are estimated as provincial totals(area sources)and spatially disaggregated onto grid cells based on proxies;this approach is reasonable to some extent but is highly questionable at fine spatial ***,we compile a new comprehensive point source database that includes nearly 100,000 industrial facilities in *** couple it with the frame of Multi-resolution Emission Inventory for China(MEIC),estimate point source emissions,combine point and area sources,and finally map China’s anthropogenic emissions of 2013 at the spatial resolution of 30’’*30’’(~1 km).Consequently,the percentages of point source emissions in the total emissions increase from less than 30%in the MEIC up to a maximum of 84%for SO_(2)in *** new point source-based emission maps show the uncoupled distribution of emissions and populations in space at fine spatial scales,however,such a pattern cannot be reproduced by any spatial proxy used in the conventional emissions *** new accurate high-resolution emission mapping approach reduces the modeled biases of air pollutant concentrations in the densely populated areas compared to the raw MEIC inventory,thus improving the assessment of population exposure.

关键词： Anthropogenic emission inventory High-resolution mapping Air quality modeling

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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Predicting web service QoS via combining matrix factorization with network location

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International Journal of u- and e- Service, Science and Technology 2014年第3期7卷 303-318页

作者： Zhou, Li Song, Zhibo Zhai, Suichu Xiao, Tan Yin, Yuyu School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex System Modeling and Simulation Ministry of Education China Hangzhou Power Supply Cooperation Hangzhou China Electric Engineering School Zhejiang University Hangzhou China

With the increasing abundance of Web Services across Internet, Quality of Service (QoS)-based service recommendation has become a hot issue. It is necessary to predict the missing values of QoS for service recommendation. Because Web services run on the Internet, their network locations may be anther critical factor for QoS prediction. Although there have existed many works on QoS prediction, few consider the influence of the network locations of users or Web Services. In this paper, we propose a novel collaborative QoS prediction framework with network location-based regularization (NLBR). We first elaborate the popular Matrix Factorization (MF) model for missing values prediction. Then, by taking advantage of the local connectivity between Web services users, we incorporate network location information to identify the neighborhood. We conduct the experiments on a public large-scale real-world QoS dataset, Experiments show that our proposed approaches have the better prediction performance compared with the existed approaches. © 2014 SERSC.

关键词： Web services

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Targeting Ultra-Emitters for Effective Methane Mitigation in China’s Coal Sector

ACS ES&T Air

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ACS ES&T Air 2024年第7期1卷 734-742页

作者： Helan Wang Bo Zheng Hui Li Qiang Zhang Kebin He Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and Ecology Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing 100084 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing 100084 China

The energy system fuels climate change as the largest emitter of global greenhouse gases, while a lack of transparent, infrastructure-level information undermines efforts to cut emissions. Coal mining has been the largest emitter of methane (CH4) in China over the past 10 years, which is not tracked at the mine level, and effective mitigation pathways have yet to be established. Herein, we develop a state-of-the-art CH4emission database, comprising 3343 active coal mines in China, by harmonizing multisource information. The results suggest that China’s coal mines emitted 15.8 ± 5.1 million metric tons CH4yr–1in 2019. Those with CH4emission intensities >2 standard deviations above the national mean accounted for 4% of China’s coal production, yet they generated 23% of China’s coal CH4emissions. We project a steady decline in China’s coal CH4emissions by >75% in 2100 compared to 2019 levels under three different scenarios, due to reduced coal demand during energy transition. Targeting ultra-emitters for mine closure will accelerate emission reductions in the midterm and achieve deep CH4emission cuts by >92% in 2100, which buys us time to control global warming. Our idealized mine closure strategy serves as a cost-effective CH4mitigation strategy to enable swift climate action.

关键词： Biological databases Climate Coal Energy Hydrocarbons

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NWD-YOLOv5: A YOLOv5 Model for Small Target Detection Based on NWD Loss

NWD-YOLOv5: A YOLOv5 Model for Small Target Detection Based ...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Liliang Zhou Huaming Ran Rongling Xiong Ruijie Tan National Key Laboratory of Complex Aviation System Simulation Chengdu China The 10th Research Institute of China Electronics Technology Group Corporation Chengdu China School of Mathematics Southwest Jiaotong University Chengdu China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Target detection technology is vital in modern social management and urban security, with small target detection— accurately identifying tiny objects in large scenes—posing a key challenge for improving surveillance system accuracy and real-time performance. The YOLO series is still the mainstream target detection model, for the YOLOv5 model, as the IoU metric it adopts still uses the geometrical overlap of the detection frame as the basis of the loss calculation, while the experiments have proved that this metric is unable to effectively utilize the small-scale characteristics of small targets. Therefore, improving the loss metric in the existing framework can help the model to pay more attention to the spatial semantic features of small targets in the image in the training phase of the network, respectively, and thus improve the detection effect. In this paper, we propose the NWD-YOLOv5 model, i.e., we introduce the NWD loss that measures the difference in the distribution of the detection frames for the existing target detection framework YOLOv5, which ensures that the loss of the small targets is not neglected in the calculation of the forward propagation loss of the model, and we purposefully modify the structure of the feature extraction network in the original framework by adding a feature extraction layer for the detection of the small targets, which enhances the ability of the model to capture the subtle features thus improving the accuracy of small target detection. Through comparative experiments, we evaluated the detection performance of the original YOLOv5 model, the YOLOv5 model using only the NWD loss, the YOLOv5 model with only the additional small-target detection layer, and the full NWD-YOLOv5 model for small-targeted human bodies on the screened COCO2017 public dataset, respectively. The experimental results show that compared to the original YOLOv5 model, the model with only the NWD loss improves the mAP@.5 metrics on small-target detection by 16

关键词： YOLO Training Analytical models Accuracy Surveillance Semantics Feature extraction Propagation losses Loss measurement Security

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A Comprehensive Evaluation Framework for Deep Model Robustness

arXiv

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arXiv 2021年

作者： Guo, Jun Bao, Wei Wang, Jiakai Ma, Yuqing Gao, Xinghai Xiao, Gang Liu, Aishan Dong, Jian Liu, Xianglong Wu, Wenjun State Key Lab of Software Development Environment Beihang University Beijing China China Electronics Standardization Institute Beijing China Zhongguancun Laboratory Beijing China Institute of Unmanned System Beihang University Beijing China National Key Laboratory for Complex Systems Simulation Beijing China

Deep neural networks (DNNs) have achieved remarkable performance across a wide range of applications, while they are vulnerable to adversarial examples, which motivates the evaluation and benchmark of model robustness. However, current evaluations usually use simple metrics to study the performance of defenses, which are far from understanding the limitation and weaknesses of these defense methods. Thus, most proposed defenses are quickly shown to be attacked successfully, which results in the "arm race" phenomenon between attack and defense. To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i.e., data and model). Through neuron coverage and data imperceptibility, we use data-oriented metrics to measure the integrity of test examples;by delving into model structure and behavior, we exploit model-oriented metrics to further evaluate robustness in the adversarial setting. To fully demonstrate the effectiveness of our framework, we conduct large-scale experiments on multiple datasets including CIFAR-10, SVHN, and Im-ageNet using different models and defenses with our open-source platform. Overall, our paper provides a comprehensive evaluation framework, where re-searchers could conduct comprehensive and fast evaluations using the open-source toolkit, and the analytical results could inspire deeper understanding and further improvement to the model robustness. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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A system-of-systems architecture-driven modeling method for combat system effectiveness simulation

A system-of-systems architecture-driven modeling method for ...

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IEEE International Symposium on systems Engineering (ISSE)

作者： Xiaobo Li Weiping Wang Zhe Shu Ning Zhu Hua He Tianjun Liao Beijing Institute of System Engineering Beijing CN College of Information Systems and Management National University of Defense Technology Changsha Hunan P. R. China State Key Laboratory of Complex System Simulation Beijing Institute of Systems engineering Beijing China

ISBN: (纸本)9781509007943

Under the background of system-of-systems (SoS) counterwork, it is more apparent that the simulation modeling of the equipment should be domain-specific, formal, automatic and composable. Current equipment effectiveness simulation modeling approaches are mainly based on the methods and techniques from the modeling and simulation (M&S) community and software engineering community, and they are not capable to satisfy these four requirements systematically. Thus the systems engineering architecture model and domain knowledge should be used to support effectiveness simulation modeling more effectively. In this research, we propose an architecture-driven modeling method which conforms to the principle of architecture-driven development, uses ontology techniques to build equipment SoS architecture model and sub-domain ontology. This method performs architecture driven simulation modeling to realize the transformation from architecture models to simulation model frameworks; employs ontological metamodeling to design domain-specific modeling languages (DSML) based on the comprehensive usage of architecture models, sub-domain ontologies and M&S formalisms; integrates domain-specific simulation models from various domains using the model framework, and supports the composable development of simulation applications.

关键词： Computational modeling Ontologies Computer architecture Analytical models Mathematical model

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Tesla Rdc Operation and Performance

SSRN

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SSRN 2024年

作者： Song, Feilong Wu, Yun Yang, Xingkui Zhou, Jianping Xu, Shida Kang, Jinhui Wang, Jiaojiao National Key Lab of Aerospace Power System and Plasma Technology Air Force Engineering University Xi’an710038 China Institute of Aeroengine Xi’an Jiaotong University Xi’an710049 China Beijing Aeronautical Technology Research Center Beijing100071 China Key Laboratory of Complex Aviation Simulation System Beijing100076 China

The suppression of pressure feedback and the control for detonation wave operating mode play a key role in improving the stagnation pressure gain of RDC. In this study, a unique Tesla RDC, in which Tesla pneumatic valve is introduced into the inlet structure and combustion chamber, has been tested compared with traditional configuration (RP-3 kerosene as fuel) at a flow rate of 1kg/s under the outlet throat to air inlet area ratio of 3. It is observed that the application of Tesla valve effectively controls the propagation direction of detonation wave and rotates clockwise as expected. At the same time, the percentage of pressure fluctuation to the static pressure in plenum decreases as a whole, and the lowest is only 4.2%. The maximum pressure gain obtained by equivalent available pressure method in all operating conditions is -26.1%. In addition, there is a linear relationship between thrust and air plenum pressure (Pt2), and when the Pt2 increases, the percentage of thrust to the required for the theoretical pressure gain of 0 increases gradually, up to 60.8% at the equivalence ratio of 0.65 and Pt2 of 2.47atm. It is predicted that a higher stagnation pressure gain will be achieved by carrying out a larger flow rate of 2.5kg/s and increasing Pt2 to 6.1atm. This work provides a new idea for RDC feedback suppression and mode control, and demonstrates the potential advantages of Tesla valve in RDC. © 2024, The Authors. All rights reserved.

关键词： Detonation

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Tesla Rdc Operation and Performance

SSRN

引用

SSRN 2024年

作者： Song, Feilong Wu, Yun Yang, Xingkui Zhou, Jianping Xu, Shida Kang, Jinhui Wang, Jiaojiao National.Key Lab of Aerospace Power System and Plasma Technology Air Force Engineering University Xi’an710038 China Institute of Aeroengine Xi’an Jiaotong University Xi’an710049 China Beijing Aeronautical Technology Research Center Beijing100071 China Key Laboratory of Complex Aviation Simulation System Beijing100076 China

关键词： Detonation

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Warming up a Backup Control Barrier Function

Warming up a Backup Control Barrier Function

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Chinese Automation Congress (CAC)

作者： Weijia Wang Tao Meng Xiaofeng Zhao Jiakun Lei Kun Wang Zhejiang University School of Aeronautics and Astronautics Hangzhou Zhejiang China Modeling and Simulation of Complex Systems National Key Laboratory Beijing China Hagnzhou International Innovation Institute of Beihang University Hangzhou Zhejiang China Beihang University Hangzhou Innovation Istitute Hangzhou Zhejiang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Backup control barrier function (Backup CBF) is a tractable formulation that ensure the feasibility of CBF-based quadratic programming (QP) problems through implicitly defined control invariant set. Backup CBF is based on two fundamental ingredients: a known backup set and a fixed backup policy, and the implicitly defined control invariant set is evaluated through forward integration by backup policy in a finite horizon online. However, there is a lack of systematic methods for providing the backup set and backup policy for a system with input constraints, making it difficult to synthesize a backup CBF. In this paper, we propose a method for second-order systems to obtain backup policy and backup set practically, rather than empirical choice, by constructing a more conservative explicit CBF-QP with auxiliary velocity constraints integrated. The proposed method warms up a backup CBF by an explicit conservative CBF-QP under mild assumptions, while without excessive conservatism, allowing the tasks of safety and feasibility to be guaranteed theoretically. We demonstrate the efficacy of the proposed method in simulation.

关键词： systematics Automation Control systems Safety Quadratic programming complex systems

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