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Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

Environmental science and Ecotechnology 2024年第3期19卷 100-108页

作者： Bo Xu Haofei Yu Zongbo Shi Jinxing Liu Yuting Wei Zhongcheng Zhang Yanqi Huangfu Han Xu Yue Li Linlin Zhang Yinchang Feng Guoliang Shi State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission ResearchCollege of Environmental Science and EngineeringNankai UniversityTianjin300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER) College of Environmental Science and EngineeringNankai UniversityTianjin300350China Department of Civil Environmentaland Construction EngineeringUniversity of Central FloridaOrlandoFLUSA School of Geography Earth and Environment Sciences University of BirminghamBirminghamB152TTUK State Key Laboratory of Precision Measuring Technology and Instruments Tianjin Key Laboratory of air Pollutants Monitoring TechnologySchool of Precision Instrument and Opto-electronics EngineeringTianjin UniversityTianjin300072China Gigantic Technology(Tianjin)Co. LtdTianjin300072China College of Computer Science Nankai UniversityTianjin300350China China National Environmental Monitoring Centre Beijing100012China

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion *** process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning ***,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient *** urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory *** approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual ***,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest *** research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

关键词： Machine learning Data driven Theoretical approach Domain knowledge Guide

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CorPN: Corner Prediction Network for Visual Tracking

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Li, Qun Zhang, Haijun Yang, Kai Dai, Wei Ma, Jianghong Liu, Yuping the Department of Computer Science Harbin Institute of Technology Shenzhen Xili University Town Shenzhen518055 China the School of Computer Science and Artificial Intelligence WuHan Textile University Wuhan430200 China the School of Information and Control Engineering Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China the School of Microelectronics Fudan University Shanghai200433 China

Object tracking is a well-established task in the field of computer vision. Despite numerous efforts over the last few decades, object tracking remains a formidable challenge, largely due to the complexity of the scenes encountered in video sequences. In line with the powerful capabilities of Transformers for object feature extraction and fusion, as well as drawing inspiration from CornerNet, this paper introduces a new Transformer-based anchor-free method designed for object tracking called CorPN (i.e., Corner Prediction Network). Specifically, a Transformer-based backbone is proposed to extract and fuse deep features in a Siamese network architecture. Furthermore, our methodology aims to emulate the intuitive cognitive process by which humans identify the bounding-box corners of objects. CorPN turns out to be a more effective and robust tracking method because it jointly leverages multi-level feature aggregation, corner prediction, and probabilistic prediction incorporated in the tracking head. In extensive evaluations on public datasets, our method achieves a state-of-the-art performance. In particular, our approach achieves an area-under-the-curve (AUC) of 85.2%, a precision score (P) of 84.5%, and a normalized precision score (Pnorm) of 89.6%, outperforming extant state-of-the-art trackers on these metrics. © 2025 IEEE. All rights reserved,

关键词： Object detection

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Study of the Influence of Rolling Modes on the Operation of Interconnected Electric Drives of a Rolling Mill 4

Study of the Influence of Rolling Modes on the Operation of ...

引用

4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Tytiuk, Valerii Busher, Victor Kalinin, Alexey Chornyi, Oleksii Druzhinin, Valeriy Halushko, Dmytro Department of Electromechanics Kryvyi Rih National University Kryvyi Rih Ukraine Dept. of Electrical Engineering and Electronics National University "Odessa Maritime Academy" Odessa Ukraine Department of Informative computer systems Abylkas Saginov Karaganda Technical University NPJSC Karaganda Kazakhstan Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrogradskyi National University Kremenchuk Ukraine Department of Power Engineering Karaganda Industrial University NPJSC Temirtau Kazakhstan Dept. of Electrical Engineering Ukrainian State University of Science and Technologies Dnipro Ukraine

ISBN: (纸本)9798350395532

The paper is devoted to study of the influence of rolling modes on the performance of powerful interconnected electric drives of a hot rolling mill under the action of an electromagnetic coupling circuit between the electric drives of the roughing and finishing groups. The action of the electromagnetic coupling circuit manifests itself in the form of the influence of impact loads of synchronous electric drives on the angular velocity of direct current electric drives of the finishing group of the mill. The parameters of the electromagnetic coupling circuit are determined by the power supply scheme and equipment parameters. Values of impact loads applied to a synchronous motor depend on the rolling process parameters, in particular, the chemical composition of the rolled steel and the temperature of the ingot. The paper presents the results of calculating the rolling power for various rolling mode parameters. Using mathematical modeling in Simulink, numerical estimates of the influence of rolling modes on the magnitude of the voltage drop in the power supply unit and the drop in the angular velocity of the electric drive of the finishing group were obtained on the example of a wide-strip hot rolling mill 1700 of ArcelorMittal Temirtau JSC. © 2023 IEEE.

关键词： Hot rolling

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Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering

arXiv

引用

arXiv 2023年

作者： Lv, Wei Zhang, Chao Li, Huaxiong Jia, Xiuyi Chen, Chunlin The Department of Control Science and Intelligence Engineering Nanjing University Nanjing210093 China The Nanjing Institute of Agricultural Mechanization Ministry of Agriculture and Rural Affairs Nanjing210014 China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Incomplete multi-view clustering (IMVC) has received increasing attention since it is often that some views of samples are incomplete in reality. Most existing methods learn similarity subgraphs from original incomplete multi-view data and seek complete graphs by exploring the incomplete subgraphs of each view for spectral clustering. However, the graphs constructed on the original high-dimensional data may be suboptimal due to feature redundancy and noise. Besides, previous methods generally ignored the graph noise caused by the inter-class and intra-class structure variation during the transformation of incomplete graphs and complete graphs. To address these problems, we propose a novel Joint Projection Learning and Tensor Decomposition Based method (JPLTD) for IMVC. Specifically, to alleviate the influence of redundant features and noise in high-dimensional data, JPLTD introduces an orthogonal projection matrix to project the high-dimensional features into a lower-dimensional space for compact feature learning. Meanwhile, based on the lower-dimensional space, the similarity graphs corresponding to instances of different views are learned, and JPLTD stacks these graphs into a third-order low-rank tensor to explore the high-order correlations across different views. We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition based graph filter for robust clustering. JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic tensor models the true data similarities. An effective optimization algorithm is adopted to solve the JPLTD model. Comprehensive experiments on several benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art methods. The code of JPLTD is available at https://***/weilvNJU/JPLTD. Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

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Design and Comparison of Ammonia Reactors for Power-to-Ammonia

SSRN

引用

SSRN 2024年

作者： Rosbo, Joachim Weel Jensen, Anker Degn Jørgensen, John Bagterp Huusom, Jakob Kjøbsted Department of Chemical and Biochemical Engineering Technical University of Denmark Kongens Lyngby2800 Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby2800 Denmark 2-control ApS Herning7400 Denmark

This work investigates the design and performance of three general types of ammonia reactors for Power-to-Ammonia (P2A). Specifically, the reactors are an adiabatic quench cooled reactor (AQCR), adiabatic indirect cooled reactor (AICR) and internal direct cooled reactor (IDCR).We present rigorous steady-state and dynamic models for the three reactor types. Steady-state optimisation at nominal load shows that the AICR and IDCR yield the highest reactant conversion at 30.0 \% and 29.4\% respectively, while the reactant conversion in the AQCR is significantly lower at 26.1 \%. We perform open-loop simulations around the optimal operating point. This illustrates the unstable oscillatory dynamics of the AQCR, while the AICR and IDCR show stable and fast decaying oscillations. However, a stability analysis revealed that the optimal operating points are situated on the verge of reactor extinction for all the reactors. Thus, even minor disturbances pose a risk of extinguishing the reactors when optimally *** optimise the reactors over an operating window from 30\% to 130\% of nominal load relevant for P2A operations. The AICR and IDCR exhibit similar reactant conversions over this operational range and showcase adaptability to varying loads as significantly higher conversions are achieved at lower loads. In contrast, the AQCR exhibits a less pronounced increase in reactant conversion at low *** the location of the optimal operating points close to reactor extinction, we suggest operating the reactors at a 15 K higher feed temperature than optimal (safety margin). We show that a 15 K safety margin only decreases ammonia conversion slightly over the operating window and ensures stable operation even for a 10\% decrease in reaction rate. © 2024, The Authors. All rights reserved.

关键词： Ammonia

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Learning-guided iterated local search for the minmax multiple traveling salesman problem

arXiv

引用

arXiv 2024年

作者： He, Pengfei Hao, Jin-Kao Xia, Jinhui The School of Automation and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The Department of Computer Science LERIA Université d’Angers 2 Boulevard Lavoisier Angers49045 France

The minmax multiple traveling salesman problem involves minimizing the longest tour among a set of tours. The problem is of great practical interest because it can be used to formulate several real-life applications. To solve this computationally challenging problem, we propose a leaning-driven iterated local search approach that combines an aggressive local search procedure with a probabilistic acceptance criterion to find high-quality local optimal solutions and a multi-armed bandit algorithm to select various removal and insertion operators to escape local optimal traps. Extensive experiments on 77 commonly used benchmark instances show that our algorithm achieves excellent results in terms of solution quality and running time. In particular, it achieves 32 new best-known results and matches the best-known results for 35 other instances. Additional experiments shed light on the understanding of the composing elements of the algorithm. © 2024, CC BY-NC-ND.

关键词： Benchmarking

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A Deep Learning Approach For Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants

A Deep Learning Approach For Faults Recognition of Dissolved...

引用

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Liliana Maria Ghinea Mihaela Miron Harsha Ratnaweera Department of Automatic Control and Electrical Engineering Department Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Faculty of Science and Technology Norwegian University of Life Sciences Oslo Norway

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing.

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Unveiling Transformer Perception by Exploring Input Manifolds

arXiv

引用

arXiv 2024年

作者： Benfenati, Alessandro Ferrara, Alfio Marta, Alessio Riva, Davide Rocchetti, Elisabetta Department of Environmental Science Policy Università degli Studi di Milano Milano Italy Department of Computer Science Università degli Studi di Milano Milano Italy Dipartimento di Scienze del Sistema Nervoso e del Comportamento Università di Pavia Pavia Italy Department of Control and Computer Engineering Politecnico di Torino Torino Italy

This paper introduces a general method for the exploration of equivalence classes in the input space of Transformer models. The proposed approach is based on sound mathematical theory which describes the internal layers of a Transformer architecture as sequential deformations of the input manifold. Using eigendecomposition of the pullback of the distance metric defined on the output space through the Jacobian of the model, we are able to reconstruct equivalence classes in the input space and navigate across them. We illustrate how this method can be used as a powerful tool for investigating how a Transformer sees the input space, facilitating local and task-agnostic explainability in computer Vision and Natural Language Processing tasks. © 2024, CC BY-SA.

关键词： Equivalence classes

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Low-disturbance Nonlinear control of TBM Cutterhead Speed in Coal Mine Tunneling

Low-disturbance Nonlinear Control of TBM Cutterhead Speed in...

引用

Annual Conference of Industrial Electronics Society

作者： Nannan Liu Tong Wang Jianmin Wang Yingda Fan School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Computer Science University of Pittsburgh Pittsburgh USA

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

To minimize the disturbance of the Tunnel Boring Machine (TBM) cutterhead on the surrounding rock during the coal mine roadway excavation process and ensure that the cutterhead rotation speed achieves fast tracking performance with minimal overshoot, we propose a direct adaptive robust control method for the cutterhead rotation speed hydraulic system based on inversion design. This method considers the strong disturbances such as loads and motion affecting the cutterhead hydraulic drive system, as well as the uncertainties in the cutterhead model. We establish the nonlinear model of the cutterhead hydraulic drive system and employ virtual control to reduce the model order. Using Lyapunov functions, we ensure the stability of the entire system and derive the control law for the cutterhead rotation speed controller, along with parameter-adaptive laws acting as parameter estimators. We validate the effectiveness of the proposed control strategy through joint simulation using AMESim and Simulink. The results show that the designed cutterhead rotation speed controller achieves high tracking accuracy and good adaptability.

关键词： Robust control Adaptation models Uncertainty Tracking Software packages Hydraulic drives Tunneling Stability analysis Coal mining Load modeling

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Abstracted Model Reduction: A General Framework for Efficient Interconnected System Reduction

arXiv

引用

arXiv 2024年

作者： Poort, Luuk Janssen, Lars A.L. Besselink, Bart Fey, Rob H.B. van de Wouw, Nathan Department of Mechanical Engineering in the Dynamics & Control group Eindhoven University of Technology Eindhoven5600 MB Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9700 AK Netherlands

This paper introduces the concept of abstracted model reduction: a framework to improve the tractability of structure-preserving methods for the complexity reduction of interconnected system models. To effectively reduce high-order, interconnected models, it is usually not sufficient to consider the subsystems separately. Instead, structure-preserving reduction methods should be employed, which consider the interconnected dynamics to select which subsystem dynamics to retain in reduction. However, structure-preserving methods are often not computationally tractable. To overcome this issue, we propose to connect each subsystem model to a low-order abstraction of its environment to reduce it both effectively and efficiently. By means of a high-fidelity structural-dynamics model from the lithography industry, we show, on the one hand, significantly increased accuracy with respect to standard subsystem reduction and, on the other hand, similar accuracy to direct application of expensive structure-preserving methods, while significantly reducing computational cost. Furthermore, we formulate a systematic approach to automatically determine sufficient abstraction and reduction orders to preserve stability and guarantee a given frequency-dependent error specification. We apply this approach to the lithography equipment use case and show that the environment model can indeed be reduced by over 80% without significant loss in the accuracy of the reduced interconnected model. © 2024, CC BY-NC-ND.

关键词： Structural dynamics

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