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检索条件"任意字段=25th International Conference on Methods and Models in Automation and Robotics"

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Approach to Bearing Fault Diagnosis: CNN-Based Classification Across Different Preprocessing Techniquese

Approach to Bearing Fault Diagnosis: CNN-Based Classificatio...

引用

international Carpathian Control conference (ICCC)

作者： Urszula Jachymczyk Paweł Knap Patryk Balazy Szymon Podlasek Krzysztof Lalik Faculty of Mechanical Engineering and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

this paper presents a rigorous investigation into the efficacy of diverse preprocessing methods for bearing fault classification, leveraging the comprehensive CWRU dataset. Four distinct approaches were explored: raw data analysis, Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). the study introduces a Convolutional Neural Network (CNN) as the underlying algorithm for fault classification. through extensive experimentation and analysis, we assess the performance of CNN in conjunction with each preprocessing technique. the results provide valuable insights into the strengths and limitations of raw data and frequency-domain representations, highlighting the impact on the accuracy of fault classification in machinery health monitoring applications, which was decided to be the main score in models evaluation. this comparative analysis can not only contribute to the advancement of condition monitoring but also assist practitioners in selecting optimal preprocessing methods for their specific needs.

关键词： Condition monitoring Vibrations Continuous wavelet transforms Accuracy Data analysis Fast Fourier transforms Process control

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Robust control of intelligent robots by parametrized state feedback methods

Robust control of intelligent robots by parametrized state f...

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international conference on Emerging Technologies and Factory automation (ETFA)

作者： M. Rouff P. Makany Laboratoire Universitaire Des Sciences appliquées de Cherbourg - UCBN-EIC Cherbourg Octeville France

ISBN: (纸本)0780372417

Intelligent robots must be able to be faced to many new difficult tasks. this implies the use of many parameters in the control laws. We show that if these parameters are slowly varying before the dynamics of the grand motion axes, the classical decoupling methods remain valid with parametrized laws, but if parameters dynamic are non negligible before the decoupling dynamic, we are in the fast varying parameters case, and we show in this case that decoupling methods operators must be changed in order to include these dynamical effects, which lead by using the classical decoupling methods, to non-efficient control. the computation of the static and dynamic feedback laws are given in the case of nonlinear decoupling, linearization and rejection of perturbations. A robust example in robotics is given, and the contribution of the parameters dynamic is shown. We show also that this kind of robust robot model can be treated by differential flatness methods. An N link robot manipulator is considered in this way and the same operators appear. Finally, an application to the pseudo differential flatness of these models are presented by a closed loop approach.

关键词： Robust control Intelligent robots State feedback State-space methods Robustness Equations Orbital robotics Motion control Manipulator dynamics Ear

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Game Design Tools for ML Data Generation in CPS

Game Design Tools for ML Data Generation in CPS

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international conference on Autonomous Robots and Agents, ICARA

作者： Maria Krantz Niklas Widulle Oliver Niggemann Institute of Automation Technology Helmut-Schmidt-University Hamburg Germany

the high complexity of Cyber-Physical Systems (CPS) necessitates novel approaches for system analysis, planning, anomaly detection and testing. Machine Learning (ML) methods are promising because of their ability to find underlying relations even in large, complex and conflicting data. While existing CPS produce large data sets, these might not cover the appropriate time frame, or the desired configuration. therefore, the use of ML methods requires the use of simulation tools to generate the necessary data. there are numerous approaches to simulate CPS. However, they often have significant shortcomings regarding their expressiveness in regards to physical properties of system components, their scalability in the face of the ever-increasing complexity of CPS, their usability for simultaneous simulation of different aspects of CPS and interoperability between different simulation environments. Game and media creation tools have seen an impressive development in recent years with regards to their realistic representation of physical systems and simulation capabilities. these are already employed in some engineering challenges like training of algorithms for self-driving cars. they have huge potential for the application in simulation and analysis of CPS. In this work we provide an analysis of the shortcomings of currently used environments for modeling and simulation of CPS with regards to creating data for ML. We then analyze how currently existing limitations can be overcome by employing tools from game and media design, discussing possible use cases and applications of these tools. With this, we present a possible new direction of research which has the potential to improve modeling of CPS, especially with regards to their application for ML.

关键词： Training Analytical models Scalability Games Media Data models Complexity theory

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Ensemble Machine Learning methods for Unsteady Aerodynamics Modeling from Flight Data

Ensemble Machine Learning Methods for Unsteady Aerodynamics ...

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international conference on Control, automation and robotics (ICCAR)

作者： Ajit Kumar Ajoy Kanti Ghosh Dept. of Mechanical and Aerospace Engineering IITRAM Ahmedabad Gujarat INDIA Dept. of Aerospace Engineering IIT Kanpur U.P. India

ISBN: (数字)9798350373172

ISBN: (纸本)9798350373189

In this paper, two ensemble machine learning-based methods, Bagging and Boosting, are applied to model the unsteady aerodynamics of an aircraft from flight test data. Bagging generates a predictive model by combining multiple decision trees that have been trained using the bootstrap on different input-output datasets. Boosting generates a predictive model by growing trees sequentially based on previously grown trees, with each decision tree fitting on a modified version of the data. the effectiveness of these two data-driven methods is investigated and validated by estimating the standard research aircraft's force and moment coefficients. the proposed methods' estimated results are statistically analysed and found to be highly correlated with measured data and to have a significantly lower root mean squared error (RMSE). Furthermore, these estimated aerodynamic force and moment coefficients are compared to the estimated coefficient model from the most commonly used maximum likelihood estimation method (MLE). the estimated results were found to be on par with the MLE predicted aerodynamic models. Moreover, Bagging and Boosting-based methods do not require the solution of the equation of motion, which is advantageous for generalised nonlinear modelling applications such as load estimation, aeroelasticity, and fault diagnosis, detection, and identification.

关键词： Maximum likelihood estimation Machine learning algorithms Force Predictive models Aerodynamics Boosting Mathematical models

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Multi-Sensor-based Predictive Control for Autonomous Parking in Presence of Pedestrians

Multi-Sensor-based Predictive Control for Autonomous Parking...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： David Pérez-Morales Olivier Kermorgant Salvador Domínguez-Quijada Philippe Martinet LS2N Laboratoire des Sciences du Numérique de Nantes École Centrale de Nantes Nantes France INRIA Sophia Antipolis Valbonne France

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

this paper explores the feasibility of a Multi-Sensor-Based Predictive Control (MSBPC) approach in order to have constraint-based backward non-parallel (perpendicular and diagonal) parking maneuvers capable of dealing with moving pedestrians and, if necessary, performing multiple maneuvers. Our technique relies solely in sensor data expressed relative to the vehicle and therefore no localization is inherently required. Since the proposed approach does not plan any path and instead the controller maneuvers the vehicle directly, the classical path planning related issues are avoided. Real experimentation validates the effectiveness of our approach.

关键词： Robot sensing systems Robots Path planning Kinematics Data models Automobiles Transmission line matrix methods

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Reactive rhythm activities and offline classification of imagined speeds of finger movements

Reactive rhythm activities and offline classification of ima...

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international conference on Bioinformatics and Biomedical Engineering

作者： Fu, Yunfa Xu, Baolei Pei, Lili Li, Hongyi State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Graduate School of the Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424450893

Reactive rhythm bands to imagined speeds of index finger movement and offline classification of imagined speeds were explored in the paper. EEG was recorded when 4 subjects executed motor imagery of two tasks at first-person perspective that involved left index fingers at two speeds (4Hz and 1Hz, trained and paced by metronome). Relatively prominent rhythmic activities around 10˜13 Hz and 24˜26 Hz over C3, 12˜13 Hz over Cz, and 11˜13 Hz, 8˜10Hz, and 25˜26 Hz over C4 were found by spectrum analysis. the most significant band 9- 13Hz was used to construct the feature space. the Fisher discriminant analysis method (FDA), multi-layer perceptron neural network (MLP), and distinction sensitive learning vector quantization (DSLVQ) were applied in offline identification of imagined speeds respectively. the best average misclassification rates between fast and slow movement imagination by only three derivations C3, Cz, and C4 with FDA, MLP, and DSLVQ were 27.5±1%, 31.2±3.7%, and 34.6% at 3260 ms respectively. the results showed that FDA in this study was better than MLP and DSLVQ. this study demonstrated that discrimination for imagined speeds of finger movement was possible and feasible. Classification of imagined movement speeds with high accuracy based on EEG is also possible through improving methods in the paper. the study may provide a strategy to realize fine control of robots by brain-controlled robot interface. © 2011 IEEE.

关键词： Speed

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Evaluating the Impact of One-Shot Structured Pruning: A Noise Robustness Perspective

Evaluating the Impact of One-Shot Structured Pruning: A Nois...

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robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Dieter Balemans Jens De Hoog thomas Huybrechts Jan Steckel Siegfried Mercelis IDLab - Faculty of Applied Engineering University of Antwerp - imec Antwerp Belgium CoSysLab - Faculty of Applied Engineering University of Antwerp Antwerp Belgium Flanders Make Strategic Research Centre Lommel Belgium

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

the increasing complexity of neural networks in combination with the growing interest in edge computing has led to a surge in the development of neural network compression techniques. these methods aim to reduce the computational complexity of the networks while maintaining a high level of performance. However, the impact of compression on the robustness of the network is often overlooked. therefore, in this work, we investigate the robustness of compressed neural networks by assessing the way a neural network behaves under different types and levels of noise. More specifically, we propose a robustness evaluation method that employs two different evaluation methodologies: a Multi-Level Monte Carlo simulation [1] and the CIFAR-100-C [2] dataset. We compare popular neural networks with their compressed versions, generated using an XAI-inspired one-shot structured channel pruning methodology. We systematically evaluate the robustness of the models pruned for different scenarios. Our results show that the pruned models can maintain their robustness properties in the presence of noise, and that specialization through pruning can improve a model's ability to handle corruptions.

关键词： Monte Carlo methods automation Noise Neural network compression Robustness Noise robustness Surges Computational complexity Robots Edge computing

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Adaptive backstepping control based on structural reliability for a turret-moored FPSO vessel

Adaptive backstepping control based on structural reliabilit...

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international conference on Advanced robotics (ICAR)

作者： Yuanhui Wang Yulong Tuo Simon X. Yang College of Automation Harbin Engineering University Harbin Heilongjiang Province China School of Engineering University of Guelph Guelph ON Canada

ISBN: (纸本)9781538631584

For floating production storage and offloading (FPSO) vessels, a positioning controller is necessary because only a mooring system alone cannot always keep the ship within a predefined region. this paper develops a new adaptive backstepping controller based on structural reliability index to deal with the model uncertainties of the FPSO vessel. Model uncertainties can be estimated by the adaptive law derived from the Lyapunov stability theory. the capacity of the mooring system can be fully utilized to position the FPSO vessel by adjusting the structural reliability index on the premise of ensuring the safety of mooring lines, and hence less control effort is needed for the positioning controller. the robustness of the proposed control algorithm is demonstrated through simulations using three sets of different system parameters. In addition, a comparison to the PID controller without the structural reliability shows that less control effort is needed using the proposed controller.

关键词： Reliability Backstepping Indexes Lyapunov methods Adaptation models Adaptive systems Marine vehicles

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Recognition of overlapping 2-D objects by local feature construction method

Recognition of overlapping 2-D objects by local feature cons...

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international conference on Pattern Recognition

作者： Y. Nakamura M. Nagao Department of Electrical Engineering Kyoto University Kyoto Japan

A method is described for the recognition of partially occluded 2-D objects. this method considers a set of corners, parallel lines, and so on as typical features of an object. Possible candidate models are estimated from these features, and structural matching is performed between these models and features obtained from a picture by checking the combinations of various features. Even if the whole structure is not obtained due to a partial occlusion, the system can infer an object if some unique features of the object are obtained. Partial shapes and extracted lines are matched in detail with model candidates when they are limited to one or a few.< >

关键词： Testing Computer vision Shape robotics and automation Robot vision systems Feature extraction Object detection Image recognition Image processing Smoothing methods

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A Predictive Model for Morphogenesis of a Robot Sequence Mimicking Protein Folding

A Predictive Model for Morphogenesis of a Robot Sequence Mim...

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IEEE international conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Zihao Ni Zhi Han Shixian Wang Yuping Gu Yongliang Yang School of Artificial Intelligence Shenyang University of Technology Shenyang Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

the swarm phenomena observed in nature have inspired many control algorithms for diverse robot swarm systems. However, most of these endeavors are qualitative simulations aimed at understanding the mechanisms underlying biological swarm behavior. Reliably predicting morphologies of robot swarms from their features remains a formidable challenge. In molecular biology, the 3D structure of a protein molecule, which is a main factor of its functions, is mainly determined by its sequence of amino acids. Inspired by this, our lab developed RoboFold, a robot swarm platform for developing protein-folding-inspired methods controlling the morphologies of robot swarms. Using the data collected from this platform, we proposed a deep learning approach to predict the morphologies of swarm robotics based on their initial sequences. the goal of this research is predicting the initial sequence of robots forming the desired morphology. Preliminary results presented here demonstrated the efficacy of this method and underscore the feasibility of applying protein-folding-inspired methods to program robot swarms.

关键词： Proteins three-dimensional displays Morphology Swarm robotics Predictive models Self-organizing networks Programming Prediction algorithms Reliability Robots

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