检索结果-内蒙古大学图书馆

2017 International Conference on Control, Automation and Diagnosis, ICCAD 2017

作者： Abdelwahed, Imen Ben Mbarek, Abdelkader Bouzrara, Kais Research Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia

ISBN: (纸本)9781509059874

This paper proposes an online identification of the MIMO ARX-Laguerre model by giving an analytical solution to optimize the Laguerre poles and using recursive identification of Fourier coefficients. Theses parameters characterizing MIMO ARX-Laguerre model are identified basing on the regularised square error. The Laguerre poles optimization and the Fourier coefficients identification procedures are realized on a sliding window in order to generate an online identification algorithm of the MIMO ARXâǍbŞLaguerre model. This algorithm is tested on a benchmark : the Continuous Stirred Tank Reactor (CSTR). © 2017 IEEE.

关键词： Poles

来源：评论

学校读者我要写书评

暂无评论

A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

引用

arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Modeling and Robustness Study of Railway Transport Networks Using P-Timed Petri Nets”

Journal of Engineering

引用

Journal of Engineering 2019年第1期2019卷

作者： Anis Mhalla Mouhaned Gaied Research Laboratory LARA in Automatic Control National Engineering School of Tunis (ENIT) University of Tunis el Manar Tunis Tunisiautm.rnu.tn Research Unit LARATSI Automatic Signal and Image Processing National Engineering School of Monastir Avenue Ibn El Jazzar 5019 Monastir Tunisiaenim.rnu.tn

来源：评论

学校读者我要写书评

暂无评论

Adaptive sliding mode control with application super-twisting algorithm of a Human-Driven Knee Joint Orthosis

Adaptive sliding mode control with application super-twistin...

引用

International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Rihab Bkekri Anouar Benamor Hassani Messaoud Samer Mohammed Yacine Amirat Laboratory of Automatic Signal and Image Processing University of Tunis Hussein Montfleury Tunis Laboratory of Images Signals and Intelligent Systems (LISSI) University of Paris-Est Créteil Vitry-Sur-Seine France

The paper concerns the development of a adaptive sliding mode control based on super twisting (ASTW) and can be applied to a Human-Driven Knee Joint Orthosis. Therefore, a model of the shank-orthosis system is given considering the human effort as an external torque acting on the system. The main objective of this paper is to bring together two approaches to reduce the phenomenon of chattering, adaptation of gain and high-order sliding mode control. The obtained controller, based on the well known second order sliding mode algorithm, does not require any information on the limits of the perturbation and its gradient except for their existence. Simulation results provided demonstrate the effectiveness of the proposed approach.

关键词： Sliding mode control Trajectory Stability analysis Adaptation models Torque Uncertainty Mathematical model

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Process Monitoring Using Genetic Algorithms

Lecture Notes in Mechanical Engineering

引用

Lecture Notes in Mechanical Engineering 2016年 113-124页

作者： Najeh, Tawfik Telmoudi, Achraf Jaber Nabli, Lotfi Research Laboratory of Automatic Signal and Image Processing School of Engineers of Monastir University of Monastir Monastir Tunisia

This paper suggests a new approach for fault detection using Genetic Algorithms (GAs). GAs are used to find the principal curve that summarize the data. The principal curve is a generation of linear Principal Component Analysis (PCA). Introduced by Hastie as a parametric curve, the original definition is based on the self-consistency property. The Hastie’s theory encloses weaknesses in case of complex data structures or data with intersections. The existing principal curves methods employ the first component of the data as an initial estimation of principal curve that passes satisfactorily through the middle of data. However the needing of an initial line is the major inconvenient of this approach. In this work, we extend this problem in two ways. First, we introduce a new method based on GAs to find the principal curve. Second, potential application of principal curves in fault detection is proposed. An example is presented to prove the efficiency of the proposed algorithm to fault detection of nonlinear process. © 2016, Springer International Publishing Switzerland.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Online identification of nonlinear system using a new kernel algorithm

Online identification of nonlinear system using a new kernel...

引用

International Conference on Systems and Control (ICSC)

作者： Okba Taouali Laboratory of Automatic Signal and Image Processing University of Monastir Tunisia

This paper proposes a new online kernel identification method of a nonlinear system in the Reproducing Kernel Hilbert Space. The proposed algorithm entitled online RKPLS-RN kernel method uses the Reduced Kernel Partial Least Square (RKPLS) technique in an offline phase to construct a RKHS model with reduced parameter number. Then the Regularized Network (RN) method is used on online phase to update the reduced parameters of the RKHS model. The considered measure of performance is the Normalized Means Square Error (NMSE). The proposed online kernel method is evaluated by handling the Cascades Tanks system.

关键词： Decision support systems Control systems

来源：评论

学校读者我要写书评

暂无评论

On the application of recursive principal component analysis method to fault detection and isolation

On the application of recursive principal component analysis...

引用

International Conference on Systems and Control (ICSC)

作者： I. Jaffel O. Taouali I. Elaissi H. Massaoud Jaffel is with the Laboratory of Automatic Signal and Image Processing (LARATSI) University of Monastir Tunisia

This communication suggests an extension of an online fault detection method [1] to fault isolation. This proposed method is titled Recursive Principal Component Analysis based on First Order Perturbation RPCA-FOP and it is based on first order perturbation theory (FOP) and partial PCA models where the eigenvalues and eigenvectors of the covariance matrix are updated taking into account the effect of new acquired data as a perturbation.

关键词： Principal component analysis Eigenvalues and eigenfunctions Fault detection Data models Matrices Covariance matrices Chemical reactors

来源：评论

学校读者我要写书评

暂无评论

Identification and observer synthesis of nonlinear systems using Laguerre multiple models

Identification and observer synthesis of nonlinear systems u...

引用

International Conference on Systems and Control (ICSC)

作者： Ghassen Marouani Abdelkader Mbarek Tarek Garna Hassani Messaoud Ghassen MAROUANI is with Research Laboratory of Automatic Signal and Image Processing University of Monastir Tunisia

This paper proposes a new nonlinear model entitled decoupled state Laguerre multiple models approach used for modeling nonlinear systems. This representation is a combination between the classical multiple models approach and the Laguerre orthogonal bases used for modeling the sub-systems. The goal of this combination is to reduce the complexity of the identification algorithm of multiple models approach and to make a parametric reduction. The resulting multiple models prods with its parameter complexity reduction and with its simple structure offers the possibility to applying online identification algorithms and, therefore, the synthesis of adaptive control algorithms. The parameter identification of the Laguerre multiple models is achieved by using the Newton-Raphson algorithm. The performances of the Laguerre multiple models and the proposed identification approach are illustrated by numerical simulations.

关键词： Nickel Biological system modeling Observers Mathematical model Complexity theory Linear systems

来源：评论

学校读者我要写书评

暂无评论

Nonlinear model based predictive control using multiple models approach expanded on Laguerre bases

引用

WSEAS Transactions on Systems and Control 2015年 10卷 113-126页

作者： Marouani, Ghassen Mbarek, Abdelkader Garna, Tarek Messaoud, Hassani Research Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Rue Ibn ELJazzar 5019 Monastir Tunisia

This paper proposes a nonlinear model based predictive control (NMPC) algorithm for nonlinear systems by using multiple models approach. To have a less complexity model we expand each linear sub-model on an orthogonal Laguerre basis, the characteristic pole of which should be optimized. In this paper we propose a pole optimization algorithm based on the Gauss-Newton method and we use the provided Laguerre multiple model (LMM) to synthesize a NMPC algorithm. The proposed pole optimization technique as well as the NMPC using LMM approach are validated on a chemical reactor. © 2015 World Scientific and Engineering Academy and Society. All rights reserved.

关键词： Nonlinear systems

来源：评论

学校读者我要写书评

暂无评论

Identification of optimal kernel parameters of RKHS model based on genetics algorithm

引用

WSEAS Transactions on Systems and Control 2015年 10卷 373-384页

作者： Taouali, Okba Tawfik, Najeh Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Tunisia Rue Ibn El Jazzar Monastir Tunisia

This paper proposes a new approach based on the Genetics Algorithm to determine the optimal kernel parameters of the Reproducing Kernel Hilbert Space (RKHS) model. These parameters are the width of the kernel function and the regularization parameter. The proposed meta-method has been tested to model some benchmarks such a benchmark of DC-Motor [21], a Wiener-Hammerstein benchmark [17], a Feedback’s Process Trainer PT326 [20] and a Continuous Stirred Tank Reactor CSTR [22] and the results are satisfactory. © 2015, World Scientific and Engineering Academy and Society. All rights reserved.

关键词： DC motors

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：