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Unintended side effect of the coal-to-gas policy in North China Plain: Migration of the sources and health risks of ambient PAHs

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Science of the Total Environment 2025年 958卷 178050页

作者： Li, Zhiyong Liu, Chen Ren, Zhuangzhuang Liu, Jinming Ma, Xiaohua Ning, Zhi Meng, Jianwei Liu, Aiqin Ma, Huichun Wang, Lei Chen, Lan Wang, Hao Kong, Shaofei Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China Hebei Key Lab of Mineral Resources and Ecological Environment Monitoring Hebei Research Center for Geoanalysis Baoding071051 China Department of Atmospheric Sciences School of Environmental Sciences China University of Geosciences Wuhan430074 China

Ongoing coal-to-gas (CTG) largely cut down both coal consumption and associated PM2.5. However, a knowledge gap still existed in CTG impacts on the other energy and organic pollutant emissions. Coupling on-site investigation with statistical yearbooks, we provided a more realistic energy evolutions before (BCTG), during (DCTG), and after (ACTG) the CTG for Hebei Province. Together, we examined the impacts of CTG derived energy conversion on PM2.5-bound PAHs at urban (UA)/suburban rural (SRA)/remote rural (RRA) sites in winter 2022. As expected, the consumptions of coal and natural gas (NG) far decreased and increased from BCTG to ACTG, respectively. Accidentally, biomass usage rose by 60.7%, and rural CTG acted as a main driver. Specially, SRA's NG-shortage and coal-stove demolition should be the main inducements, and RRA's coal-sale ban was another trigger in the early stage of CTG. ∑18PAHs and ∑8TPAHs stand for the sum of 18 PAHs and 8 toxic PAHs, respectively. ∑18PAHs (ng/m3) presented as SRA (81.8) > RRA (46.4) > UA (19.4). Biomass burning (BB) and NG combustion (NGC) contributed most to∑18PAHs of 31.0% and 23.1% at SRA, resulting in the highest ∑18PAHs, ∑18PAHs/PM2.5, and ∑8TPAHs/PM2.5, and incremental lifetime cancer risk values. Also, NGC has become the second largest contributor at UA. Variations in both diagnostic ratios and source-depend isomers further proved the prominence of NGC related PAHs at UA vs. SRA. Notably, RRA was least affected by the CTG, coal combustion (CC, 40.4%) and BB (32.6%) still occupied the top positions. In short, CTG gave rise to an upsurge in biomass usage, and the incremental PAHs emissions from BB vs. NGC. This study underlined that the priorities should be given to rural NG guarantee and subsidy retention, and biomass prohibition for further air quality improvement. © 2024 Elsevier B.V.

关键词： Coal

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Development and path planning of a novel unmanned surface vehicle system and its application to exploitation of Qarhan Salt Lake

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Science China(Information Sciences) 2019年第8期62卷 195-197页

作者： Zhibin XUE Jincun LIU Zhengxing WU Sheng DU Shihan KONG Junzhi YU College of Chemical Engineering Qinghai University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

Qarhan Salt Lake is rich in mineral resources, such as sodium chloride, potassium, and magnesium [1].At present, liquid mine exploiting is the main exploitation form for the Qarhan Salt Lake. Salt mining ships with mining equipments are widely used to exploit carnallite which is located at the bottom of pools. Conventionally, workmen utilize rude ferric sampler to collect carnallite samples

关键词： USV simulation Development and path planning of a novel unmanned surface vehicle system and its application to exploitation of Qarhan Salt Lake

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Current Sharing control Method for Fuel Cell Hybrid Electric Vehicles

Current Sharing Control Method for Fuel Cell Hybrid Electric...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Muhammad Maaruf Georgios Konstantinou Muhammad Khalid Control and Instrumentation Engineering Department Center for Smart Mobility and Logistics SDAIA-KFUPM Joint Research Center for Artificial Intelligence School of Electrical Engineering and Telecommunications UNSW Sydney Sydney Australia Electrical Engineering Department Center for Renewable Energy and Power Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

This paper considers fuel cell hybrid electric vehicles (FCHEV) including a proton exchange membrane (PEM) fuel cell as the main energy source, and lithium-ion battery and ultra-capacitor as energy storage systems to support the fuel cell. The two important objectives are to share the current demand of the traction motor among the energy sources and maintain the stability of the DC-bus voltage. The current sharing is based on an energy management scheme. A passivity-based control (PBC) approach is proposed to achieve current tracking based on the energy management and regulate the DC-bus voltage. The PBC is a nonlinear control approach that guarantees the stability of a closed-loop system by making it passive. The feasibility of the proposed control method is evaluated in MATLAB/Simulink.

关键词： Protons Lithium-ion batteries Fuel cells Traction motors Hybrid electric vehicles Resource management Fuels

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Simulation of Wind Turbine Vibrations 27

Simulation of Wind Turbine Vibrations

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27th International Workshop on Electric Drives: MPEI department of Electric Drives 90th Anniversary, IWED 2020

作者： Andriushchenko, Ekaterina Kallaste, Ants Vaimann, Toomas Rassolkin, Anton Heidari, Hamidreza Demidova, Galina L. Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia ITMO University Faculty of Control Systems and Robotics St. Petersburg Russia

ISBN: (纸本)9781728141589

In recent years, there has been an increasing interest in renewable energy. Particularly, wind energy is gaining more and more popularity in many countries. However, the main obstacles to wind turbines usage are the vibration and noise close to wind farms. Therefore, this paper is focused on developing a new method for mathematical simulation of vibrations of a wind turbine in order to find out and reduce the vibration level. Particularly, low-frequency vibration of a wind turbine, composed of a tower and rotor in the out-of-balance condition is considered. The proposed method of vibration simulation is based on the theory of elastic beams and the general vibration theory. The calculations are made for a wind turbine VTR 50 Arctic. The results of the calculations are the displacement of the tower top and the stress at the tower foundation. Furthermore, it is worth emphasizing that the method is quite flexible. It allows adding complexity into the system, for instance, external loads such as wind pressure, aerodynamic loads, seismic loads or operational loads. © 2020 IEEE.

关键词： Wind power

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Intelligent Synthesis of Individual Rehabilitation Plans for Patients after Joint Endoprosthesis Based on Computer Vision Technologies 7

Intelligent Synthesis of Individual Rehabilitation Plans for...

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Arseniev, D.G. Borisov, V.V. Misnik, A.E. Gomzhina, E.A. Shalukhova, M.A. Scientific Innovation Institute 'Mathematical Modeling and Intelligent Control Systems' Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia National Research University 'MPEI' Branch in Smolensk Department of Computer Engineering Smolensk Russia Information Technology Software Inter-State Educational Institution of Higher Education 'Belarusian-Russian University' Mogilev Belarus Early Medical Rehabilitation FSBI 'FCTOE' The Ministry of Health of the Russian Federation Smolensk Russia Inter-State Educational Institution of Higher Education 'Belarusian-Russian University Peter the Great St. Petersburg Polytechnic University Information Technology Software Mogilev Belarus

ISBN: (纸本)9798350371369

The article presents an innovative approach to the rehabilitation process for patients with joint endoprosthetics, focusing on the integration of a specialized software module based on computer vision technologies and the ontological approach. Developed for the Federal Centre for Traumatology, Orthopaedics, and Endoprosthesis in Smolensk, Russia, this system is designed to improve the quality of life for patients by offering personalized rehabilitation plans. The core of the study lies in the application of ontological models and meta-associative graphs for synthesizing individual rehabilitation plans, which streamline the recovery process and reduce the workload on healthcare professionals. This method allows for the processing of large amounts of information, providing detailed conclusions and recommendations for functional correction of the musculoskeletal system. The software utilizes computer vision to analyze patient movement and develop tailored rehabilitation strategies, overcoming the limitations of traditional diagnostic tools like MRI, which may not be suitable for all patients. By incorporating artificial intelligence and augmented reality, the system enhances the accuracy of patient assessments and the effectiveness of rehabilitation plans. Furthermore, the article discusses the application of this technology in the educational process at the Belarusian-Russian University, where students are introduced to cutting-edge software tools that assist in creating personalized rehabilitation plans. This educational integration not only enriches the students' learning experience but also fosters innovation in the development of new applications for computer vision in medical rehabilitation. The ontological approach, combined with modern technology, represents a significant advancement in the management of musculoskeletal system diseases, offering a more efficient, personalized approach to rehabilitation and recovery. © 2024 IEEE.

关键词： Application programs

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

Knowledge-aware Graph Transformer for Pedestrian Trajectory ...

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International Conference on Intelligent Transportation

作者： Yu Liu Yuexin Zhang Kunming Li Yongliang Qiao Stewart Worrall You-Fu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Guangdong Polytechnic Normal University Guangzhou China Australian Centre for Field Robotics The University of Sydney NSW Australia Australian Institute for Machine Learning The University of Adelaide SA Australia Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities SUSTech Shenzhen China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.

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Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning

arXiv

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arXiv 2023年

作者： Wang, Runqing Wang, Gang Sun, Jian Deng, Fang Chen, Jie The National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Department of Control Science and Engineering Tongji University Shanghai201804 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China

Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the quality of solutions still has room for improvement relative to that by the exact methods such as OR-Tools. To address this issue, this paper presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making. The complex relationships between operations and machines are represented precisely and concisely, for which a dual-attention network (DAN) comprising several interconnected operation message attention blocks and machine message attention blocks is proposed. The DAN exploits the complicated relationships to construct production-adaptive operation and machine features to support high-quality decision-making. Experimental results using synthetic data as well as public benchmarks corroborate that the proposed approach outperforms both traditional PDRs and the state-of-the-art DRL method. Moreover, it achieves results comparable to exact methods in certain cases and demonstrates favorable generalization ability to large-scale and real-world unseen FJSP tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Study Assessing the Impact of Task Duration on Performance Metrics for Multi-robot Teams 21st

A Study Assessing the Impact of Task Duration on Performance...

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21th Annual Conference on Towards Autonomous Robotics, TAROS 20120

作者： Miyauchi, Genki Sklar, Elizabeth I. Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom Lincoln Institute for Agri-food Technology University of Lincoln Lincoln United Kingdom

ISBN: (纸本)9783030634858

The allocation of tasks to members of a team is a well-studied problem in robotics. Applying market-based mechanisms, particularly auctions, is a popular solution. We focus on evaluating the performance of the team when executing the tasks that have been allocated. The work presented here examines the impact of one such factor, namely task duration. Building on prior work, a new bidding strategy and performance metric are introduced. Experimental results are presented showing that there are statistically significant differences in both time and distance-based performance metrics when tasks have zero vs greater-than-zero duration. © 2020, Springer Nature Switzerland AG.

关键词： Industrial robots

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Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics

arXiv

引用

arXiv 2021年

作者： Wang, Ruigang Koelwijn, Patrick J.W. Manchester, Ian R. Tóth, Roland Australian Centre for Field Robotics Sydney Institute for Robotics and Intelligent Systems University of Sydney SydneyNSW2006 Australia Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set. This VCCM based nonlinear stabilization and performance synthesis approach, which is similar to linear parameter-varying (LPV) control approaches, allows to achieve exact guarantees of exponential stability and L2-gain performance on nonlinear systems with respect to all trajectories from the predetermined set, which is not the case with the conventional LPV methods. Simulation and experimental studies conducted in both fully- and under-actuated operating modes of the CMG show effectiveness of this approach compared to standard LPV control methods. © 2021, CC BY.

关键词： State feedback

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Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking

arXiv

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arXiv 2022年

作者： Wang, Guangming Peng, Chensheng Zhang, Jinpeng Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai 200240 China XLAB The Second Academy of China Aerospace Science And Industry Corporation Beijing100854 China

Multiple object tracking (MOT) is a significant task in achieving autonomous driving. Traditional works attempt to complete this task, either based on point clouds (PC) collected by LiDAR, or based on images captured from cameras. However, relying on one single sensor is not robust enough, because it might fail during the tracking process. On the other hand, feature fusion from multiple modalities contributes to the improvement of accuracy. As a result, new techniques based on different sensors integrating features from multiple modalities are being developed. Texture information from RGB cameras and 3D structure information from Lidar have respective advantages under different circumstances. However, it’s not easy to achieve effective feature fusion because of completely distinct information modalities. Previous fusion methods usually fuse the top-level features after the backbones extract the features from different modalities. In this paper, we first introduce PointNet++ to obtain multi-scale deep representations of point cloud to make it adaptive to our proposed Interactive Feature Fusion between multi-scale features of images and point clouds. Specifically, through multi-scale interactive query and fusion between pixel-level and point-level features, our method, can obtain more distinguishing features to improve the performance of multiple object tracking. Besides, we explore the effectiveness of pre-training on each single modality and fine-tuning on the fusion-based model. The experimental results demonstrate that our method can achieve good performance on the KITTI benchmark and outperform other approaches without using multi-scale feature fusion. Moreover, the ablation studies indicates the effectiveness of multi-scale feature fusion and pre-training on single modality. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

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