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Machine Learning based Multiscale Reduced Kernel PCA for Nonlinear Process Monitoring

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Machine Learning based Multiscale Reduced Kernel PCA for Non...

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Khaled Dhibi Radhia Fezai Kais Bouzrara Majdi Mansouri Abdelmalek Kouadri Mohamed-Faouzi Harkat Laboratory of Automatic Signal and Image Processing School of Engineers of Monastir University of Monastir Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Fault detection (FD) is fundamental for monitoring several chemical processes. Thus, this paper introduces a novel structure multiscale reduced kernel principal component analysis (MS-RKPCA). The proposed FD method aims to address the problem of great computation time and significant storage memory space by using a data reduction structure based on the Euclidean distance metric. Additionally, to further enhance the RKPCA method, a multiscale representation of data will be used. The enhanced MS-RKPCA method uses the wavelet coefficients of the reduced data at each scale to enhance the fault detection results. The detection performance of the proposed MS-RKPCA method is evaluated using the Tennessee Eastman Process (TEP). The effectiveness of the enhanced method is evaluated in terms of the missed detection rates (MDR), false alarms rates (FAR) and computation time (CT). The results demonstrate that the developed technique is more effective for fault detection mostly in terms of computation time and memory storage space.

关键词： Fault detection Computational modeling Euclidean distance Wavelet analysis Kernel Principal component analysis Wavelet coefficients

Complex-valued Iris Recognition Network

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arXiv 2020年

作者： Nguyen, Kien Fookes, Clinton Sridharan, Sridha Ross, Arun Image and Video Research Laboratory SAIVT School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD4000 Australia The Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represent its biometric content. This necessitates the extraction and processing of phase information that cannot be effectively handled by a real-valued neural network. In this regard, we design a fully complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture. We show a strong correspondence of the proposed complex-valued iris recognition network with Gabor wavelets that are used to generate the classical IrisCode;however, the proposed method enables a new capability of automatic complex-valued feature learning that is tailored for iris recognition. We conduct experiments on three benchmark datasets - ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - and show the benefit of the proposed network for the task of iris recognition. We exploit visualization schemes to convey how the complex-valued network, when compared to standard real-valued networks, extracts fundamentally different features from the iris texture. © 2020, CC BY-NC-SA.

关键词： Biometrics

Fault detection of uncertain nonlinear process using interval-valued data-driven approach

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Fault detection of uncertain nonlinear process using interva...

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Khaled Dhibi Radhia Fezai Kais Bouzrara Majdi Mansouri Abdelmalek Kouadri Mohamed-Faouzi Harkat Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Kernel PCA (KPCA) has been extensively applied in fault detection (FD) field. However, it is constantly not optimal for uncertain systems and is not designed to handle large-scale process monitoring. Thus, a nonlinear fault detection (FD) method based interval reduced KPCA (IRKPCA) is developed for fault detection. The proposed IRKPCA technique uses interval-valued Euclidean distance as a criterion to maintain only the more pertinent measurements. The FD abilities of the IRKPCA technique is assessed using the Tennessee Eastman Process (TEP). The effectiveness of the proposed technique is assessed in terms of computation time (CT), false alarm rate (FAR)and missed detection rate (MDR).

关键词： Uncertain systems Uncertainty Fault detection Tools Nonlinear systems Kernel Principal component analysis

Machine Learning based Reduced Kernel PCA for Nonlinear Process Monitoring

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Machine Learning based Reduced Kernel PCA for Nonlinear Proc...

2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Dhibi, Khaled Fezai, Radhia Mansouri, Majdi Kouadri, Abdelmalek Bouzrara, Kais Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar

ISBN: (纸本)9781728151847

Process monitoring is an essential part of industrial systems. It requires higher product quality and safety operations. Therefore, a nonlinear data-driven approach based reduced KPCA (RKPCA) for statistical monitoring of industrial processes is developed. RKPCA is a novel machine learning tool which merges dimensionality reduction and supervised learning. The use of classical KPCA for modeling and monitoring purposes can impose a high computational load when a large number of measurements are recorded. The main idea of the proposed RKPCA approach is to reduce the number of observations (samples) in the data matrix using the Euclidean distance between samples as dissimilarity metric so that only one observation is kept in case of redundancy. The Tennessee Eastman Process (TEP) is used to evaluate the fault detection abilities of the proposed RKPCA technique. The performance of the proposed method is evaluated and compared to the classical KPCA in terms of false alarms rates (FAR), missed detection rates (MDR) and computation times (CT). © 2019 IEEE.

关键词： Fault detection

STD-TR: End-to-End Spatio-Temporal Action Detection with Transformers

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STD-TR: End-to-End Spatio-Temporal Action Detection with Tra...

Chinese Automation Congress (CAC)

作者： Zexian Li Tian Wang Aichun Zhu Kexin Liu Peng Shi Hichem Snoussi School of Automation Science and Electrical Engineering Beihang University Beijing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China Institute of Artificial Intelligence Beihang University Beijing China School of Computer Science and Technology Nanjing Tech University Nanjing China School of Mathematics and Computer Science Fujian Normal University Fujian China Institute Charles Delaunay-LM2S University of Technology of Troyes Troyes France

ISBN: (纸本)9781665426480

Spatio-temporal action detection methods locate human actions in both spatial and temporal dimension, which usually follow a two-stage structure. In this paper, We propose STD-TR, a novel spatio-temporal action detection framework with an end-to-end transformer structure. STD-TR employs two branches to extract feature from video clip and key frame concurrently, then sends the aggregated feature to the transformer encoder-decoder. View spatio-temporal action detection as a set matching and prediction problem, STD-TR employs learned object queries to model the relation of feature context, and directly outputs all predictions at one inference time. Our method remove all hand-designed and can be optimized by a joint loss. Besides, a Hungarian algorithm and a upgraded linking strategy are used for bipartite set matching and action tube generation respectively. Convincing experiment result on challenging dataset demonstrates the superiority of our method.

关键词： Viterbi algorithm Predictive models Feature extraction Transformers Prediction algorithms Inference algorithms Electron tubes

Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

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arXiv 2020年

作者： Azcona, Emanuel Besson, Pierre Wu, Yunan Punjabi, Arjun Martersteck, Adam Dravid, Amil Parrish, Todd B. Bandt, S. Kathleen Katsaggelos, Aggelos K. Image and Video Processing Laboratory Department of Electrical and Computer Engineering Northwestern University IL United States Lab Northwestern Memorial Hospital IL United States Neuroimaging Laboratory Department of Radiology Northwestern University IL United States Augmented Intelligence in Medical Imaging Northwestern University IL United States

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type. Copyright © 2020, The Authors. All rights reserved.

关键词： Mesh generation

THREE DIMENSIONAL BLIND image DECONVOLUTION FOR FLUORESCENCE MICROSCOPY USING GENERATIVE ADVERSARIAL NETWORKS

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arXiv 2019年

作者： Lee, Soonam Han, Shuo Salama, Paul Dunn, Kenneth W. Delp, Edward J. Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN United States Department of Electrical and Computer Engineering Indiana University Indianapolis IN United States Division of Nephrology School of Medicine Indiana University

Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted by various types of noise which exacerbate image quality at deeper tissue depth. Therefore, quantitative analysis of fluorescence microscopy in deeper tissue still remains a challenge. This paper presents a three dimensional blind image deconvolution method for fluorescence microscopy using 3-way spatially constrained cycle-consistent adversarial networks. The restored volumes of the proposed deconvolution method and other well-known deconvolution methods, denoising methods, and an inhomogeneity correction method are visually and numerically evaluated. Experimental results indicate that the proposed method can restore and improve the quality of blurred and noisy deep depth microscopy image visually and quantitatively. Copyright © 2019, The Authors. All rights reserved.

关键词： image reconstruction

One-dimensional quasi bound states in the continuum with large operation bandwidth in the ω~k space for nonlinear optical applications

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arXiv 2022年

作者： Sun, Kaili Jiang, Hui Bykov, Dmitry A. Van, Vien Levy, Uriel Cai, Yangjian Han, Zhanghua Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and Applications School of Physics and Electronics Shandong Normal University Jinan250358 China Image Processing Systems Institute Branch of the Federal Scientific Research Centre "Crystallography and Photonics" Russian Academy of Sciences 151 Molodogvardeyskaya st. Samara443001 Russia Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 2V4 Canada Department of Applied Physics The center for Nanoscience and Nanotechnology The Hebrew University of Jerusalem Jerusalem Israel

The phenomenon of bound state in the continuum (BIC) with infinite quality factor and lifetime has emerged in recent years in photonics as a new tool of manipulating light-matter interactions. However, most of the investigated structures only support BIC resonances at very few discrete points in the ω~k space. Even when the BIC is switched to a quasi-BIC(QBIC) resonance through perturbation, its frequency will still be located within a narrow spectral band close to that of the original BIC, restricting their applications in many fields where random or multiple input frequencies beyond the narrow band are required. In this work, we demonstrate that a new set of QBIC resonances can be supported by making use of a special binary grating consisting of two alternatingly aligned ridge arrays with the same period and zero-approaching ridge width difference on a slab waveguide. These QBIC resonances are distributed continuously over a broad band along a line in the ω~k space and can thus be considered as one-dimensional QBICs. With the Q factors generally affected by the ridge difference, it is now possible to choose arbitrarily any frequencies on the dispersion line to achieve significantly enhanced light-matter interactions, facilitating many applications where multiple input wavelengths are required, e.g. sum or difference frequency generations in nonlinear optics. Copyright © 2022, The Authors. All rights reserved.

关键词： Resonance

Reduced Gaussian process regression for fault detection of chemical processes

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Reduced Gaussian process regression for fault detection of c...

2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Fezai, Radhia Mansouri, Majdi Bouguila, Nasreddine Nounou, Hazem Nounou, Mohamed Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar Chemical Engineering Program Texas AM University at Qatar Doha Qatar

ISBN: (纸本)9781728151847

In this paper, a reduced Gaussian process regression (RGPR)-based generalized likelihood ratio test (GLRT) is proposed for fault detection in industrial systems. In contrast to the classical GPR technique, the RGPR model can handle domains with a large number of activities that require very large training sets. The developed RGPR-based GLRT method aims first to build a RGPR model, then, it consists to apply GLRT to the monitored residuals obtained from PGPR for fault detection purposes. The fault detection performance of the developed RGPR-based GLRT method is illustrated through the Tennessee Eastman process. The simulation results show that the RGPR-based GLRT method outperforms the conventional GPR-based GLRT technique in terms of miss detection rate and CPU-time. © 2019 IEEE.

关键词： Fault detection