检索结果-内蒙古大学图书馆

signal, Control and Communication (SCC), International Conference on

作者： Marwa Yousfi Tarek Garna Hassani Messaoud Research Laboratory of Automatic Signal and Image Processing University of Monastir Monastir Tunisia Higher Institute of Applied Science and Technology of Sousse University of Sousse Sousse Tunisia

ISBN: (纸本)9781665402712

In this paper, robust multiple model-based LSDP (Loop-Shaping Design Procedure) controller is proposed for a SISO system where each linear submodel is represented in the form of a SISO ARX (Auto Regressive with exogenous input) Laguerre model. The proposed controller guarantees robustness in term of stability and reference tracking of the SISO system in presence of parametric uncertainties. Simulation example on the SISO CSTR (Continuous Stirred Tank Reactor) Benchmark is given to show the efficiency and the robustness of the proposed controller in handling the parametric uncertainties of the process.

关键词： Uncertainty Perturbation methods Process control Benchmark testing Numerical simulation Robustness Stability analysis

来源：评论

学校读者我要写书评

暂无评论

Real-Time Fault Detection and Diagnosis Method for Industrial Chemical Tennessee Eastman Process

Real-Time Fault Detection and Diagnosis Method for Industria...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Institut Superieur des Sciences Appliques et de Technologie de Kasserine Kairouan University Tunisia The Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria The Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The accurate detection and diagnosis of faults are critical for maintaining optimal operation and ensuring the reliability of industrial processes. Notably, the topic of online fault detection and diagnosis has recently presented a significant challenge. This work mainly deploys a neural network technique for the comprehensive detection and diagnosis of faults within the Tennessee Eastman Process (TEP) on a low-computational power system, the Raspberry Pi board. The devolved methodology showcases a remarkable level of accuracy (94.50%) in diagnosing the various TEP faults, affirming its robustness and effectiveness. To elevate the practical applicability of the proposed approach, a meticulous investigation into the implementation of the suggested approach on a Raspberry Pi 4 card was undertaken. The successful realization of this implementation not only highlights the adaptability of the approach but also paves the way for its seamless integration into practical industrial applications.

关键词： Accuracy Embedded systems Fault detection Neural networks Electrical fault detection Real-time systems Robustness Power system reliability Information technology Chemicals

来源：评论

学校读者我要写书评

暂无评论

Real-Time Fault Detection Scheme for Industrial Chemical Tennessee Eastman Process

Real-Time Fault Detection Scheme for Industrial Chemical Ten...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The key idea behind this study is to integrate a moving window dynamic PCA (MW-DPCA) methodology for fault detection within the Tennessee Eastman process (TEP) into a low-computational power system, the Raspberry Pi 4 card, for real-time application. Indeed, the paramount importance of real-time fault detection (FD) in intricate industrial processes presents a critical challenge. Various data-driven techniques have been developed to ensure safety, maintain operational stability, and optimize productivity in such processes. Principal Component Analysis (PCA) is a fundamental data-driven technique that utilizes dimensionality reduction to extract the most informative features from high-dimensional data, simplifying analysis and potentially revealing underlying fault patterns. However, PCA primarily focuses on static relationships and may miss crucial temporal dynamics for fault identification. This is where dynamic PCA (DPCA) excels. By incorporating lagged values of variables, DPCA captures the temporal evolution of features, enabling a more comprehensive understanding of process behavior and improving the detection of faults involving dynamic changes. In order to address the stochastic measurements, a moving average filter tool is also employed. The results obtained and the successful realization of this implementation demonstrate the adaptability of the approach and pave the way for its seamless integration into practical industrial applications.

关键词： Fault detection Power system dynamics Power system stability Feature extraction Electrical fault detection Real-time systems Stability analysis Robustness Safety Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

HU-Net: Hessian-incorporated U-Net for CBCT projection-domain metal segmentation with guidance from SAM

HU-Net: Hessian-incorporated U-Net for CBCT projection-domai...

引用

IEEE Conference on Cybernetics and Intelligent Systems

作者： Chen Jiang Tianling Lyu Gege Ma Yan Xi Yang Chen Wentao Zhu Zhejiang Lab Hangzhou China Hangzhou Institute for Advanced Study of UCAS Hangzhou China Shanghai First-Imaging Tech. Shanghai China Laboratory of Image Science and Technology Southeast University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Metal artifact is a prevailing factor reducing the image quality of CBCT in minimally invasive spine surgery. In CBCT, conventional metal artifact reduction algorithms pay more attention to the inpainting of the metal traces, but metal segmentation is also challenging. Despite the current success in image segmentation with deep learning, the substantial expense associated with annotating metal traces in the projection domain makes most of these approaches impractical for this task. To address this, we propose a Hessian-incorporated U-Net (HU-Net) for CBCT projection-domain metal segmentation with guidance from SAM. The proposed method has been tested on both digital phantom data and clinical CBCT data. According to the experimental results, our method has demonstrated notable improvement in the segmentation of metal traces across both datasets. This paper presents a feasible approach for metal trace segmentation in the projection domain with deep learning and has the potential to be applied in clinical applications.

关键词： Deep learning image quality image segmentation Minimally invasive surgery Mechatronics Metals Phantoms

来源：评论

学校读者我要写书评

暂无评论

Novel explanation of the Active Galactic Nuclei. The Power Source of Quasars as a result of vacuum polarization by the gravitational singularities on the distributional BHs horizon 9

Novel explanation of the Active Galactic Nuclei. The Power S...

引用

9th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2020

作者： Foukzon, Jaykov Men'Kova, Elena R Potapov, Alexander A Department of Mathematics Israel Institute of Technology Haifa3200003 Israel All-Russian Research Institute for Optical and Physical Measurements Moscow119361 Russia Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences Moscow125009 Russia JNU-IREE Joint Laboratory of Fractal Method and Signal Processing Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China

In this paper we argue that the current paradigm for AGN and quasars is essentially incomplete and a rivision is needed. Remind that the current paradigm for AGN and quasars is that their radio emission is explained by synchrotron radiation from relativistic electrons that are Doppler boosted through bulk motion. In this model, the intrinsic brightness temperatures cannot exceed 1011 to 1012 K. Typical Doppler boosting is expected to be able to raise this temperature by a factor of 10. The observed brightness temperature of the most compact structures in BL Lac, constrained by baselines longer than 5.3G, must indeed exceed 2 × 1013K and can reach as high as 3 × 1014K. This is difficult to reconcile with current incoherent synchrotron emission models from relativistic electrons, requiring alternative models such as emission from relativistic protons. However the proton, as we know, is 1836 times heavier than an electron and absolutely huge energy is required to accelerated it to sublight speed. These alternative models such as emission from relativistic protons can be suported by semiclassical gravity effect finds its roots in the singular behavior of quantum fields on curved distributional space- times presented by rotating gravitational singularities. © Published under licence by IOP Publishing Ltd.

关键词： Synchrotron radiation

来源：评论

学校读者我要写书评

暂无评论

The dual-path hypothesis for the emergence of anosognosia in Alzheimer’s disease

arXiv

引用

arXiv 2023年

作者： Andrade, Katia Guiyesse, Thomas Medani, Takfarinas Koechlin, Etienne Pantazis, Dimitrios Dubois, Bruno Sorbonne University Pitié-Salpêtrière Hospital Paris75013 France AP-HP Pitié-Salpêtrière Hospital Paris75013 France Signal & Image Processing Institute University of Southern California Los AngelesCA90089 United States École Normale Supérieure Laboratoire de Neurosciences Cognitives et Computationnelles 29 Rue d’Ulm Paris75005 France McGovern Institute for Brain Research Massachusetts Institute of Technology CambridgeMA United States

Although neurocognitive models have been proposed to explain anosognosia in Alzheimer's disease (AD), the neural cascade responsible for its origin in the human brain remains unknown. Here, we build on a mechanistic dual-path hypothesis that brings error-monitoring and emotional processing systems as key elements for self-awareness, with distinct impacts on the emergence of anosognosia in AD. Proceeding from the notion of anosognosia as a dimensional syndrome, ranging from the lack of concern about one's own deficits (i.e., anosodiaphoria) to the complete lack of awareness of deficits, our hypothesis states that (i) unawareness of deficits would result from a failure in the error-monitoring system, whereas (ii) anosodiaphoria would more likely result from an imbalance between emotional processing and error-monitoring systems. In the first case, a synaptic failure in the error-monitoring system, in which the cingulate cortex plays a major role, would have a negative impact on error (or deficits) awareness, preventing patients from becoming aware of their condition. In the second case, an impairment in the emotional processing system, in which the amygdala and orbitofrontal cortex play a major role, would prevent patients from monitoring the internal milieu for relevant errors (or deficits) and assigning appropriate value to them, thus biasing their impact on the error-monitoring system. Our hypothesis stems from two scientific premises. One comes from preliminary results in AD patients showing a synaptic failure in the error-monitoring system and decline of awareness at the time of diagnosis. Another comes from the somatic marker hypothesis, which proposes that emotional signals are critical to adaptive behavior. Further exploration will be of great interest to illuminate the foundations of self-awareness and improve our understanding of the underlying mechanisms of anosognosia in AD. © 2023, CC BY-NC-ND.

关键词： Errors

来源：评论

学校读者我要写书评

暂无评论

H∞interval observer design for uncertain discrete-time linear switched systems with unknown inputs

H∞interval observer design for uncertain discrete-time line...

引用

2021 International Conference on Control, Automation and Diagnosis, ICCAD 2021

作者： Marouani, Ghassen Dinh, Thach Ngoc Sehli, Naima Raissi, Tarek Wang, Zhenhua Messaoud, Hassani University of Monastir National Engineering School of Monastir Automatic Research Laboratory Signal and Image Processing Monastir5019 Tunisia Conservatoire National Des Arts Et Métiers CEDRIC-Lab Paris75141 France University of Tunis National Engineering School of Tunis Conception and Control of Systems Laboratory Le Belvedere 1002 Tunisia Harbin Institute of Technology School of Astronautics Harbin150001 China

ISBN: (纸本)9781665449687

This paper deals with unknown input interval observers for discrete-time linear switched systems. A new structure of interval observer is used to overcome the design difficulty of coordinate transformation often used in such context. The interval observer gains are computed by solving Linear Matrix Inequalities (LMI) derived from multiple quadratic Lyapunov functions (MQLF) under average dwell time switching signals. © 2021 IEEE.

关键词： Linear matrix inequalities

来源：评论

学校读者我要写书评

暂无评论

Restoration-enhanced reversible information steganography network for CT images in the Internet of Medical Things

引用

IEEE Transactions on Instrumentation and Measurement 2025年

作者： Chen, Kai Chen, Yang Coatrieux, Gouenou Wu, Jiasong Coatrieux, Jean-Louis School of Cyber Science and Engineering Southeast University Nanjing210096 China Southeast University Nanjing210096 China IMT Atlantique Inserm LaTIM UMR1101 Brest29000 France Laboratoire Traitement du Signal et de L'Image Université de Rennes 1 RennesF-35000 France Centre de Recherche en Information Biomédicale Sino-francais Rennes35042 France National Institute for Health and Medical Research RennesF-35000 France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Ministry of Education Nanjing210096 China Ministry of Education Nanjing210096 China

Smart health and emotional care powered by the Internet of Medical Things (IoMT) are revolutionizing the healthcare industry by adopting several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The security of medical images containing private patient information distributed in IoMT is of paramount importance in social communication, and guaranteeing their reliability has attracted widespread attention. Some existing medical image steganography methods fail to guarantee the original image's integrity, authenticity, and lossless restoration. To solve this problem, a restoration-enhanced reversible adaptive information steganography network, MediSR-Net, is proposed. Specifically, MediSR-Net consists of an analysis module and an encoding module. The image degradation-restoration strategy is exploited by the analysis module to improve the effectiveness of information embedding and perceptual field to reduce the error of pixel prediction. An information distillation and attention machine-based approach are adopted to achieve degradation-restoration of CT medical images at any scale and focus more on the degradation-restoration effect with small magnification factors. The encoding module is responsible for the embedding and extraction of privacy information. Ten state-of-the-art are compared with the MediSR-Net method concerning steganographic effect, embedding rate, distortion analysis, and prediction error. The qualitative and quantitative results demonstrate the effectiveness and superiority of MediSR-Net. © 1963-2012 IEEE.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Advancing Cross-Subject Domain Generalization in Brain-Computer Interfaces with Multi-Adversarial Strategies

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Liu, Yici Qin, Lang Chen, Xin Jeannes, Regine Le Bouquin Coatrieux, Jean Louis Shu, Huazhong Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing210096 China RennesF-3502 France Southeast University INSERM Nanjing210096 China The First Affiliated Hospital With Nanjing Medical University Department of Radiology China Universit de Rennes 1 Laboratoire Traitement du Signal et de l’Image Rennes35000 France Centre de Recherche en Information Biomedicale Sino-Francais Rennes35042 France National Institute for Health and Medical Research Rennes35000 France

A cross-subject domain generalization (DG) approach with multi-adversarial strategies (DGMA) is introduced to reduce brain-computer interfaces (BCIs) systems’ dependency on high-quality, subject-specific EEG data, making it adaptable to unseen domains. DGMA leverages annotated training data from other subjects and consists of three modules: (1) Prefeature extraction, enhancing EEG signal separability through preprocessing, data augmentation, and tangent space mapping;(2) Distribution feature updater, aligning inter-subject feature distributions with marginal maximum mean discrepancy (MMD);(3) Multi-adversarial training, initially using gradient reversal layer (GRL) to amplify domain differences and classification loss, allowing the model to learn diverse domain-specific features before minimizing these differences to balance domain transferability and discriminability. DGMA is capable of better capturing domain-specific features while achieving stronger generalization compared to traditional methods focused solely on minimizing domain differences. Validated on four motor imagery datasets, DGMA achieved state-of-the-art accuracies of 76.1% on BCI Competition IV 2a and 72.4% on the 002-2014 dataset. Additional tests on a private fatigue dataset and the SEED dataset yielded accuracies of 99.5% and 86.6%, respectively. © 1963-2012 IEEE.

关键词： signal reconstruction

来源：评论

学校读者我要写书评

暂无评论

Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems

引用

IFAC-PapersOnLine 2022年第6期55卷 673-678页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

Fault detection and diagnosis techniques are increasingly important to ensure robust and resource efficient operation of Wind Energy Conversion (WEC) systems. In this context, this paper presents a Reduced Enhanced Gaussian Process Regression (REGPR)-based Random Forest (RF) technique (REGPR-RF) to identify and diagnose faults occurring in a nonlinear WEC systems. The proposed technique uses REGPR technique for features extraction and selection from raw sensor data. Then, these selected features are fed to RF classifier to reliably detect and classify faults. The use of REGPR to learn features avoid the dimension problems and improves the classification performance significantly with a small number of training data. The results obtained by REGPR-RF are compared to those obtained with other conventional classifiers (Support Vector Machines (SVM), Naive Bayes (NB), ...). The results show that the developed REGPR-RF technique achieve higher accuracy (99.99%) with small data sets.

关键词： Gaussian Process Regression (GPR) Noise-signal-Dependence Random Forest (RF) Feature Extraction Selection Fault Detection Diagnosis (FDD) Wind Energy Conversion (WEC) Systems

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：