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International Conference on control and Robots (ICCR)

作者： Tianen Ma Jianqi An School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Ministry of Education of Engineering Research Centre for Earth Exploration Intelligent Technology Wuhan China

ISBN: (数字)9781728175621

ISBN: (纸本)9781728175638

Since the working condition affects the chemical reactions in a blast furnace (BF), the relationship between the blast-supply parameters and the gas utilization ratio (GUR) varies with different working conditions. This paper presents a GUR prediction model based on working condition classification. First, this paper analyzes the influence of working conditions on the state parameters of a BF. Then, a fuzzy clustering method is used to identify the working condition by using some state parameters. Next, the GUR prediction model was designed based on an extreme learning machine under different working conditions. Finally, the simulation results verified the effectiveness of this method.

关键词： Employee welfare Extreme learning machines Simulation Clustering methods Predictive models Blast furnaces Robots

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Environmental and health impacts of reduced PM2.5 and trace metals from ship emissions under low-sulfur fuel oil policy in Shanghai, China

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Environmental Pollution 2025年 377卷 126409页

作者： Dong, Xuyang Zhang, Yan Yu, Guangyuan Xiong, Yuqi Han, Zimin Huo, Juntao Huang, Cheng Kan, Haidong Zheng, Mei Ning, Zhi Xie, Bing National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary Department of Environmental Science and Engineering Fudan University Shanghai200438 China Shanghai200062 China MOE Laboratory for National Development and Intelligent Governance Shanghai Institute for Energy and Carbon Neutrality Strategy IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health Fudan University Shanghai200433 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China Shanghai200235 China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment Shanghai Institute of Infectious Disease and Biosecurity Fudan University Shanghai200032 China Children's Hospital of Fudan University National Centre for Children's Health Shanghai China SKL-ESPC and SEPKL-AERM College of Environmental Sciences and Engineering and Centre for Environment and Health Peking University Beijing100871 China Division of Environment and Sustainability The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste School of Ecological and Environmental Sciences East China Normal University Shanghai200241 China

ship emissions can significantly exacerbate air pollution in coastal cities, threatening public health;however, a low-sulfur fuel oil policy, which restricts sulfur content in marine fuels, can effectively mitigate such pollution-driven challenges. This study employed a high-resolution ship emission inventory to assess the impacts of the fuel oil switch on air quality and public health in Shanghai from 2017 to 2021. Results showed a 37.3 % reduction in primary PM2.5 emissions from ships, with even steeper declines of 46.7 and 91.6 % in Ni and V emissions, respectively, leading to notable air quality improvements. Health assessments revealed a reduction in premature mortality attributable to long-term exposure to the contribution of ships to atmospheric PM2.5 concentrations, with deaths decreasing from 630 cases in 2017 to 481 in 2021. Similarly, short-term exposure-related deaths fell from 43 to 29. The port and waterfront areas experienced the most pronounced health benefits. The non-carcinogenic risks posed by trace metals (Ni and V), which were detected along the Huangpu River in 2017, dropped to 0.1 by 2021. Nonetheless, the carcinogenic risk from V persisted as a concern for adults in 2021. An analysis of ship-influenced episodes showed that population-weighted concentrations and short-term premature mortality decreased by over 50 % in densely populated areas and key ports. Despite the low population density in port areas such as Wusongkou and Waigaoqiao, the human health risks linked to ship emissions remained significant. This study demonstrates the effectiveness of low-sulfur fuel oil policy in reducing emissions and health risks, providing a scientific basis for refined pollution control strategies in port cities. © 2025 Elsevier Ltd

关键词： Air quality

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

Intelligent Synthesis of Individual Rehabilitation Plans for...

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International Conference on Information Technologies in engineering Education (Inforino)

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

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

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.

关键词： Computer vision Musculoskeletal system Technological innovation Computational modeling Medical services Propulsion Trajectory

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Dynamic Security Evaluation Method for Power systems Considering Cascading Failures

Dynamic Security Evaluation Method for Power Systems Conside...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ren Liu Xingye Xu Wei Du Kaishun Xiahou Yihua Zhu Dongxu Chang Hengdao Guo CSG Key Laboratory for Power System Simulation Electric Power Research Institute of China Southern Power Grid Guangzhou China School of Electric power engineering South China University of Technology Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute of China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of China Southern Power Grid Intelligent Operation and Control for New Energy Power System Guangzhou China

With the increasing level of informatization and interconnection in the electric power system, the risk of external attacks on power systems is increasing. In response to the drawbacks of traditional static security assessment methods, this paper establishes two security metrics, which are voltage and power flow amplitude deviation security metric and power flow entropy security metric. Based on these security metrics, a dynamic security evaluation model that considers cascading failure is proposed. Simulation results demonstrate that the proposed security metrics can effectively reflect the security of cascading failure propagation, thereby providing valuable insights for decision-making by power system operators.

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Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment

arXiv

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arXiv 2024年

作者： Ahmad, Alwani Liyana Sanchez-Bornot, Jose Sotero, Roberto C. Coyle, Damien Idris, Zamzuri Faye, Ibrahima Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Perak Malaysia Department of Neurosciences Hospital Universiti Sains Malaysia Kelantan Malaysia Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Magee campus Derry LondonderryBT48 7JL United Kingdom Department of Radiology Hotchkiss Brain Institute University of Calgary CalgaryAB Canada The Bath Institute for the Augmented Human University of Bath BathBA2 7AY United Kingdom Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Universiti Teknologi PETRONAS Perak Malaysia Universiti Teknologi PETRONAS Perak32610 Malaysia

Background. Alzheimer's Disease (AD) represents a significant challenge in neurodegenerative disorders, necessitating early detection for effective intervention. Among neuroimaging methods, magnetic resonance imaging (MRI) is widely used because it is easy to apply in clinical practice and cost-effective, making it crucial for studying AD. Objective. This study aims to perform a comprehensive analysis of machine learning (ML) methods used in MRI-based biomarker selection and classification analysis. The goal is to study AD-related early cognitive decline by discriminating between healthy control (HC) participants who stayed stable and those unstable (uHC) who developed mild cognitive impairment (MCI) within five years. Methods. We utilized 3-Tesla (3T) MRI data from the Alzheimer's Disease Neuroinformatic Initiative (ADNI) and the Open Access Series of Imaging Studies 3 (OASIS-3), focusing on HC and uHC. Freesurfer’s recon-all, among other tools, was used to extract MRI-based anatomical biomarkers corresponding to semi-automatic segmented subcortical and cortical brain regions. We applied various ML techniques to select features and classify the data. These included methods from preliminary analysis performed in the MATLAB Classification Learner (MCL) app and more sophisticated methods like nested cross-validation and Bayesian optimization implemented in a customized pipeline to enhance classification performance for balanced and imbalanced datasets. Our pipeline was applied to both original imbalanced and randomly balanced datasets within a Monte Carlo analysis. Moreover, we implemented data harmonization approaches based on polynomial regression that enhanced the performance of ML and statistical methods. Complementary performance metrics, such as Accuracy (Acc), area under receiver operating characteristic curve (AROC), F1 score, and Matthew’s correlation coefficient (MCC), were used to evaluate the assessed methodologies. Results. In feature selection analyses,

关键词： Neurodegenerative diseases

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An overview of robust maritime situation awareness methods

An overview of robust maritime situation awareness methods

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International Conference on Transportation Information and Safety (ICTIS)

作者： Xueqian Xu Xinqiang Chen Bing Wu Tsz Leung Yip Intelligent Transport Systems Research Centre (ITSC) Wuhan University of Technology Wuhan china Institute of Logistics Science and Engineering Shanghai Maritime University shanghai china Department of Logistics and Maritime Studies Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

ISBN: (纸本)9781665497145

Maritime situation awareness is the core of ship navigation and determines the development direction of smart ships. Visual perception becomes an active yet important research task in maritime traffic. To aim that, this paper introduces three situation awareness methods based on visual images. The first method uses kernelized correlation filtering algorithms and Kalman filter models to solve ship occlusion in the image and achieve accurate ship tracking tasks; The second method uses kernelized correlation filtering and logarithmic polar coordinate transformation to solve the imaging size changes during ship navigation; The third method uses yolo algorithms to obtain the high-quality distance information from port videos. The experimental results and quantitative indicators show that each method can effectively achieve reliable maritime situational awareness tasks. The comprehensive realization of maritime situation awareness can better reduce risks and ensure the safety of ships.

关键词： Visualization Correlation Navigation Interference Cameras Safety Reliability

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Size-induced axial segregation of binary mixtures in rotating drum of different lengths

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Chemical engineering Science 2025年 316卷

作者： Jielong He Yuelei Wang Liuyimei Yang Jin Xu Qijun Zheng Sida Liu Zongyan Zhou Jiangxi Provincial Key Laboratory of Particle Technology Jiangxi University of Science and Technology Nanchang 330013 China Research Centre for Intelligent Mineral Processing & Metallurgy International Institute for Innovation Jiangxi University of Science and Technology Nanchang 330013 China Ganjiang Innovation Academy Chinese Academy of Sciences Ganzhou 341000 China Centre for Simulation and Modelling of Particulate Systems Monash-Southeast University Joint Research Institute Suzhou 215123 China ARC Research Hub for Smart Process Design and Control Department of Chemical and Biological Engineering Monash University Melbourne VIC 3800 Australia

This study investigates the size-induced axial segregation of binary mixtures in rotating drums, particularly focusing on the effect of drum length. The results indicate that long drums require more time to achieve stable segregation and form more segregation bands. The core region under the free bed surface remains predominantly filled with small particles, indicating that radial segregation occurs simultaneously with the axial segregation. The angle of repose of large particle bands is consistently greater than that of small particle bands, which drives the migration of large particles. Short drums are more sensitive to changes in rotational speed, while long drums with moderate rotational speed promote the formation of segregation bands. The width of small particle bands shows an increasing–decreasing-stabilizing trend with the drum length increasing. An analysis of particle velocities reveals that the characteristics of segregation bands are closely related to the relative velocity difference between large and small particles.

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General-purpose machine-learned potential for 16 elemental metals and their alloys

arXiv

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arXiv 2023年

作者： Song, Keke Zhao, Rui Liu, Jiahui Wang, Yanzhou Lindgren, Eric Wang, Yong Chen, Shunda Xu, Ke Liang, Ting Ying, Penghua Xu, Nan Zhao, Zhiqiang Shi, Jiuyang Wang, Junjie Lyu, Shuang Zeng, Zezhu Liang, Shirong Dong, Haikuan Sun, Ligang Chen, Yue Zhang, Zhuhua Guo, Wanlin Qian, Ping Sun, Jian Erhart, Paul Ala-Nissila, Tapio Su, Yanjing Fan, Zheyong Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China School of Materials Science and Engineering Hunan University Changsha410082 China MSP group QTF Centre of Excellence Department of Applied Physics Aalto University P.O. Box 15600 Aalto EspooFI-00076 Finland Chalmers University of Technology Department of Physics Gothenburg41926 Sweden National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Department of Civil and Environmental Engineering George Washington University WashingtonDC20052 United States Department of Electronic Engineering and Materials Science and Technology Research Center The Chinese University of Hong Kong N.T. Shatin999077 Hong Kong Department of Physical Chemistry School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Institute of Zhejiang University-Quzhou Quzhou324000 China College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Mechanics and Control of Mechanical Structures Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education Institute for Frontier Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong School of Science Harbin Institute of Technology Shenzhen518055 China College of Physical Science and Technology Bohai University Jinzhou121013 China Interdisciplinary Centre for Mathematical Modelling Department of Mathematical Sciences Loughborough University Leicestershire LoughboroughLE11 3TU United Kingdom

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science. Copyright © 2023, The Authors. All rights reserved.

关键词： Radiation damage

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An Early Stage researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

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Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 512-517页

作者： Tariku Sinshaw Tamir Gang Xiong Zhishuai Li Hao Tao Zhen Shen Bin Hu Heruye Mulugeta Menkir State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China China Ship Development and Design Center Wuhan 430064 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Debremarkos University Institute of Technology Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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