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Optomechanical dissipative solitons (vol 600, pg 75, 2021)

NATURE 2022年第7896期602卷 E24-E24页

作者： Zhang, Jing Peng, Bo Kim, Seunghwi Monifi, Faraz Jiang, Xuefeng Li, Yihang Yu, Peng Liu, Lianqing Liu, Yu-xi Alu, Andrea Yang, Lan Department of Electrical and Systems Engineering Washington University St. Louis MO USA Department of Automation Tsinghua University Beijing P. R. China Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Physics Program Graduate Center City University of New York New York NY USA State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang P. R. China Institute of Microelectronics Tsinghua University Beijing P. R. China

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Ontology and Rules for Characterization of Sensors and Actuators Devices in AADL Models

Ontology and Rules for Characterization of Sensors and Actua...

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Brazilian Symposium on Computing System engineering (SBESC)

作者： Diego Camara Sales Cristian Koliver Leandro Buss Becker UFSC Florianopolis SC Brazil Federal Institute of Amazon - IFAM Campus Manaus Distrito Industrial AM Brazil Graduate Program on Automation Systems Engineering (PGEAS) UFSC Florianopolis SC Brazil

ISBN: (数字)9781728182865

ISBN: (纸本)9781728182872

This paper presents a set of ontologies to organize concepts, categories, properties, and relationships that are typically used by designers for characterizing sensing and actuation devices that compose a given system architecture (SA). From the characterization of such devices, modeling can be done using the Architecture Analysis and Design Language (AADL). AADL supports modeling and analysis of functional and nonfunctional properties to evaluate quality attributes from ontology models. The paper also proposes an AADL property set that can be applied to sensors and actuators (S&A), based on previous researches with ontologies applied to sensor networks. Specific characteristics are provided to describe systems' quality attributes. This aims to be used for analyzing the designed architecture, and also its probable future evolution.

关键词： Ontologies Actuators Software Sensor phenomena and characterization Computer architecture Standards ISO Standards

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Cardinality compensation method based on information-weighted consensus filter using data clustering for multi-target tracking

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Chinese Journal of Aeronautics 2019年第9期32卷 2164-2173页

作者： Sunyoung KIM Changho KANG Changook PARK Department of Mechanical and Aerospace Engineering Seoul National UniversitySeoul 08826Republic of Korea BK21+Transformative Training Program for Creative Mechanical and Aerospace Engineers Seoul National UniversitySeoul 08826Republic of Korea Automation and Systems Research Institute Seoul 08826Republic of Korea Institute of Advanced Aerospace Technology Seoul 08826Republic of Korea

In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.

关键词： Cardinality compensation Cardinalized probability hypothesis density filter Data clustering Information-weighted consensus filter Multi-target tracking

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A weblab control experiment using the ball and beam system and multiobjective optimization 5

A weblab control experiment using the ball and beam system a...

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5th Experiment at International Conference, exp.at 2019

作者： Kagami, Ricardo Massao Kovalski Da Costa, Guinther Mendes, Luciano Antonio Freire, Roberto Zanetti Industrial and Systems Engineering Graduate Program-PPGEPS Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil Department of Control and Automation Engineering Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil

ISBN: (纸本)9781728136370

y associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. Control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant. © 2019 IEEE.

关键词： Multiobjective optimization

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Correction: Optimization of misfit calcium cobaltite oxygen electrodes for solid oxide fuel cells through electrospinning processing

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Journal of Materials Chemistry A 2024年第40期12卷 27777-27777页

作者： Allan J. M. Araújo Itzhak I. Maor Laura I. V. Holz Meirav Mann-Lahav Vadim Beilin Armin Feldhoff Gideon S. Grader Francisco J. A. Loureiro Department of Mechanical Engineering TEMA – Centre for Mechanical Technology and Automation University of Aveiro 3810-193 Aveiro Portugal LASI – Intelligent Systems Associate Laboratory Guimarães Portugal Wolfson Department of Chemical Engineering Technion – Israel Institute of Technology 3200003 Haifa Israel Institute of Physical Chemistry and Electrochemistry Leibniz University Hannover 30167 Hannover Germany The Nancy & Stephan Grand Technion Energy Program (GTEP) Technion – Israel Institute of Technology 3200003 Haifa Israel

Correction for ‘Optimization of misfit calcium cobaltite oxygen electrodes for solid oxide fuel cells through electrospinning processing’ by Allan J. M. Araújo et al., J. Mater. Chem. A, 2024, https://***/10.10...

Correction for ‘Optimization of misfit calcium cobaltite oxygen electrodes for solid oxide fuel cells through electrospinning processing’ by Allan J. M. Araújo et al., J. Mater. Chem. A, 2024, https://***/10.1039/D4TA04085F.

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K-Loss Robust Diagnosability of Discrete-Event systems

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IFAC-PapersOnLine 2020年第4期53卷 250-255页

作者： Vinicius S.L. Oliveira Felipe G. Cabral Marcos V. Moreira COPPE-Electrical Engineering Program Federal University of Rio de Janeiro Cidade Universitária Ilha do Fundão Rio de Janeiro 21.945-970 RJ Brazil Department of Automation and Systems Federal University of Santa Catarina Campus Trindade Florianópolis 88.040-900 SC Brazil

Recently, the problem of robust diagnosis against intermittent loss of observations (RDILO) has been proposed in the literature, where a model for the plant subject to intermittent loss of event observations is presented, and fault diagnosability verification methods are proposed based on the new plant model. In this method it is assumed that some sensors of the system are reliable and are always capable of communicating their readings to the diagnoser, while the other sensors, or communication channels between sensors and diagnoser, are subject to intermittent failures. The case of unreliable communication of all observable events cannot be addressed using the RDILO since the model of the plant subject to intermittent loss of event observations also represents their permanent losses. In this paper, we formulate a different problem of robust diagnosis that considers only intermittent loss of observations, which allows considering the case that all communication channels between the plant and diagnoser are not reliable. The new formulation leads to a different notion of robust diagnosability, called K-loss robust diagnosability.

关键词： Fault diagnosis Robust diagnosability Discrete Event systems Automata Verifiers

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A Machine Learning Method for Vehicle Classification by Inductive Waveform Analysis ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13928-13932页

作者： Bruno R. Vasconcellos Marcelo Rudek Marcelo de Souza Pontifícia Universidade Católica do Paraná - PUCPR Control and Automation Egineering Curitiba PR 80215-901 Pontifícia Universidade Católica do Paraná - PUCPR Industrial and Systems Engineering Graduate Program Curitiba PR 80215-901 Fiscaltech Curitiba PR 81290-270

The classification of vehicles is a matter of great importance for traffic control and management, helping with traffic surveillance as well as in statistical data collection. Among the several vehicular classification techniques, the most popular uses inductive loop sensors, because they achieve high accuracy rate at low cost. This paper proposes 5 different vehicle classification models by inductive waveform analysis: KNN, SVC, Decision Tree, Random Forest, and Voting Classifier. A brief introduction to the mathematical basis of these models and the main forms of vehicle detection are also presented. The obtained results reached an accuracy of 94% and showed how inductive waveform analysis is still a valid option for vehicle classification.

关键词： Machine learning Intelligent transportation systems Information processing Decision support

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Evolutionary polynomial modeling for interpretable drought prediction and resilient resource management

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Ecological Informatics 2025年 90卷

作者： Francisco, Tulio J. da Silva Macêdo, Bruno Yaseen, Zaher Mundher Nikitin, Nikolay O. Bodini, Matteo Gorgoglione, Angela Saporetti, Camila M. Goliatt, L. Computational Modeling Program Federal University of Juiz de Fora MG Juiz de Fora 35501-170 Brazil Systems and Automation Engineering Graduate Program Federal University of Lavras MG Lavras 37200-000 Brazil Civil and Environmental Engineering Department King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia ITMO University St. Petersburg 197101 Russian Federation Dipartimento di Economia Management e Metodi Quantitativi Università degli Studi di Milano Via Conservatorio 7 Milano 20122 Italy Department of Fluid Mechanics and Environmental Engineering (IMFIA) School of Engineering Universidad de la República Montevideo 11300 Uruguay Department of Computational Modeling Polytechnic Institute Rio de Janeiro State University RJ Nova Friburgo 22000-900 Brazil Department of Computational and Applied Mechanics Federal University of Juiz de Fora MG Juiz de Fora 36036-900 Brazil

Droughts are natural hazards that exist in nature and can have a serious impact on the environment and society, which includes water shortages, crop failures, fires and, in some cases, soil manipulation. To assess and predict droughts, various methods, such as the Standardized Precipitation Index (SPI), were designed to segregate drought trends and excess rainfall over a period ranging from 3 to 48 months. This study proposes an innovative approach to predicting drought use, the Evolutionary Polynomial Expansion with Feature Selection (EPEFS) model, a hybrid method that integrates polynomial regression with feature selection to increase accuracy and interpretability. The methodology was applied to historical precipitation data from six meteorological stations in Türkiye, covering the period from 1971 to 2016. The drought index Standardized Precipitation Index (SPI) was used as the primary indicator, with predictions made for three different time scales: SPI-3, SPI-6 and SPI-12. Furthermore, a time series cross-validation strategy was employed to ensure performance assessment. The EPEFS model obtained R2 coefficients of 0.880, 0.903 and 0.929 for SPI-3, SPI-6 and SPI-12, respectively, surpassing the other models analyzed. Furthermore, the model presented less complexity in the generated expressions. The results suggest that the EPEFS model holds promise as a robust and interpretable tool for drought forecasting, with potential applications in early warning systems and mitigation strategies. © 2025 The Authors

关键词： Climate change Feature selection Machine learning Polynomial regression Standardized precipitation index

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A WebLab Control Experiment using the Ball and Beam System and Multiobjective Optimization

A WebLab Control Experiment using the Ball and Beam System a...

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Experiment@ International Conference (exp.at)

作者： Ricardo Massao Kagami Guinther Kovalski da Costa Luciano Antônio Mendes Roberto Zanetti Freire Industrial and Systems Engineering Graduate Program – PPGEPS Pontifícia Universidade Católica do Paraná – PUCPR Curitiba Brazil Department of Control and Automation Engineering Pontifícia Universidade Católica do Paraná - PUCPR Curitiba Brazil

By associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. Control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant.

关键词： Laboratories Mathematical model PD control Optimization Process control PI control

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