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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： A. Khodabakhsh A. Aghaee N. Niroomand H. Arabi H. Zaidi School of Electrical Engineering Sharif University of Technology Biomedical Signal and Image Processing Laboratory (BiSIPL) Tehran Islamic Republic of Iran School of Electrical and Computer Engineering Toronto Metropolitan University Car-diovascular Imaging Modeling and Biomechanics Group Toronto Ontario Canada Division of Nuclear Medicine & Molecular Imaging Genève Geneva University Hospital Genève Switzerland

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Anomaly detection plays a vital role in the early identification of brain tumors in MRI scans, and it directly impacts diagnostic accuracy and patient outcomes. Despite its importance, current methods often fall short in handling sparse labeled data and precisely localizing anomalies. In this study, an innovative method that integrates Generative Adversarial Networks (GANs) with few-shot learning and transfer learning techniques, offering a new perspective in handling scarce labeled data in medical image analysis. At the core of the proposed method is a vision Transformer-based generator, showcasing the dedication to advancing medical diagnostics technology. This generator, paired with a uniquely adapted discriminator benefiting from a pre-trained VGG16 network, enhances the model’s efficiency and accuracy in anomaly detection. The efficacy of the proposed method is demonstrated through its ability to distinguish between normal and patho- logical brain images obtained from a public dataset. In our study, the model achieved anomaly scores of approximately $0.14 \pm 0.18$ for normal images and $0.23 \pm 0.76$ for abnormal images. The enhanced precision achieved in anomaly detection and localization signifies a notable advancement beyond current methodologies. These outcomes pave the way for the creation of increasingly sophisticated and dependable diagnostic instruments, thereby facilitating more precise detection of brain tumors.

关键词： Adaptation models Accuracy Transfer learning Transformer cores Brain modeling Transformers Generators

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A Quantitative Metric of Confidence for Segmentation of Nuclei in Large Spatially Variable image Volumes

A Quantitative Metric of Confidence for Segmentation of Nucl...

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IEEE Workshop on Multimedia Signal processing

作者： Liming Wu Alain Chen Paul Salama Kenneth Dunn Seth Winfree Edward Delp Video and Image Processing Laboratory Purdue University West Lafayette IN USA School of Electrical and Computer Engineering Purdue University Indianapolis IN USA Division of Nephrology School of Medicine Indiana University Indianapolis IN USA Rocky Mountain Laboratories National Institute of Allergy and Infectious Diseases Hamilton MT USA

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Nuclei segmentation is an important step for quantitative analysis of fluorescence microscopy images. A large volume generally has many different regions containing nuclei with varying spatial characteristics. Automatically identifying nuclei that are challenging to segment can speed up the analysis of biological tissues. Here we show a segmentation technique that provides a metric of segmentation “confidence” for each segmented object in an image volume. This confidence metric can be used either to generate a “confidence map” for visual distinction of reliable from unreliable regions, or in the data space to identify questionable measurements that can be analyzed separately or eliminated from analysis. In an analysis of nuclei in a 3-dimensional image volume, we show that the confidence map correlates well with visual evaluations of segmentation quality, and that the confidence metric correlates well with F1 scores within subregions of the image volume. In addition, we also describe three visualization methods that can visualize the segmentation differences between a segmented volume and a reference volume.

关键词： image segmentation Visualization Statistical analysis Microscopy Biological tissues Data visualization Morphology Signal processing Biology Reliability

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Uncertain Fault Diagnosis of Grid-Connected PV Systems based Improved Data-Driven Paradigms 19

Uncertain Fault Diagnosis of Grid-Connected PV Systems based...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Dhibil, Khaled Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed National Engineering School of Monastir Research Laboratory of Automation Signal Processing and Image 5019 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha23874 Qatar Prince Sultan Univer-sity Department of Mathematics and Sciences Riyadh11586 Saudi Arabia Chemical Engineering Program Texas A&m University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The main idea behind this work is to diagnose Grid-Connected Photovoltaic (PV) systems. The uncertainty was treated by using the interval-valued data representation. The main interventions are threefold: first, interval ensemble techniques based on the combination of several models (SVM, KNN, and tree) into one improved model are proposed in order to isolate the different PV systems operating modes using interval raw data. Then, feature extraction and selection steps are proposed to improve the fault diagnosis results. Therefore, the interval KPCA (IKPCA) method is performed in order to extract and select the important characteristics. The proposed techniques were used to diagnose the GCPV system under different operating modes. The results demonstrated the superiority of the proposed methods. © 2022 IEEE.

关键词： Support vector machines

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Raspberry Pi-Based Monitoring System for Grid-Connected PV Systems using Deep Learning Technique*

Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

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

作者： Zahra Yahyaoui Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring *** obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Fault diagnosis Deep learning Photovoltaic systems Embedded systems Accuracy Economic indicators Bayes methods Environmental monitoring Long short term memory Optimization

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Exploration of Data Scene Characterization and 3D ROC Evaluation for Hyperspectral Anomaly Detection

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Remote Sensing 2024年第1期16卷

作者： Chang, Chein-I Chen, Shuhan Zhong, Shengwei Shi, Yidan Information and Technology College Dalian Maritime University Dalian116026 China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland BaltimoreMD21250 United States Department of Electrical Engineering National Cheng Kung University Tainan70101 Taiwan Department of Electrical Engineering Zhejiang University Hangzhou310027 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Whether or not a hyperspectral anomaly detector is effective is determined by two crucial issues, anomaly detectability and background suppressibility (BS), both of which are very closely related to two factors, the datasets used for a selected hyperspectral anomaly detector and detection measures used for its performance evaluation. This paper explores how anomaly detectability and BS play key roles in hyperspectral anomaly detection (HAD). To address these two issues, we investigate three key elements attributed to HAD. One is a selected hyperspectral anomaly detector, and another is the datasets used for experiments. The third one is the detection measures used to evaluate the effectiveness of a hyperspectral anomaly detector. As for hyperspectral anomaly detectors, twelve commonly used anomaly detectors were evaluated and compared. To address the appropriate use of datasets for HAD, seven popular and widely used datasets were studied for HAD. As for the third issue, the traditional area under a receiver operating characteristic (ROC) curve of detection probability—PD versus false alarm probability, PF, (AUC(D,F))—was extended to 3D ROC analysis where a 3D ROC curve was developed to generate three 2D ROC curves from which eight detection measures could be derived to evaluate HAD in all round aspects, including anomaly detectability, BS and joint anomaly detectability and BS. Qualitative analysis showed that many works reported in the literature which claimed that their developed hyperspectral anomaly detectors performed better than other anomaly detectors are actually not true because they overlooked these two issues. Specifically, a comprehensive study via extensive experiments demonstrated that these 3D ROC curve-derived detection measures can be further used to address the various characterizations of different data scenes and also to provide explanations as to why certain data scenes are not suitable for HAD. © 2023 by the authors.

关键词： Curve fitting

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Transfer Learning for Hypersonic Vehicle Trajectory Prediction

Transfer Learning for Hypersonic Vehicle Trajectory Predicti...

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IEEE Conference on Aerospace

作者： Emily R. Bartusiak Michael A. Jacobs Corbin F. Spells Moses W. Chan Mary L. Comer Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Lockheed Martin Corporation Sunnyvale California USA Lockheed Martin Corporation West Palm Beach Florida USA

Hypersonic glide vehicles (HGVs) introduce challenges in terms of predicting their future flight behavior because they fly at high speeds and maneuver during flight. Furthermore, there are limited examples of actual HGV flights, which impedes the development of prediction methods that model HGV trajectories. In this paper, we investigate transfer learning for HGV trajectory prediction to evaluate how stochastic grammars trained on a limited number of HGV trajectories perform on new, unseen HGV trajectories. Our analysis includes two datasets containing HGV trajectories that exhibit different maneuvers. One dataset contains trajectories that exhibit vertical maneuvers, which are behaviors related to changes in altitude. The second dataset exhibits both horizontal and vertical maneuvers, where horizontal maneuvers refer to changes in crossrange and downrange. The vertical dataset is also significantly smaller than the second dataset (i.e., the dataset that exhibits horizontal and vertical maneuvers). We develop a prediction method using the smaller, less complicated dataset to model HGV trajectories. Specifically, we use an unsupervised machine learning method based on stochastic grammars. Then, we demonstrate that the learned model can be used to predict HGV behavior and engageability for trajectories from the larger, more complicated dataset. Our results show that transfer learning improves prediction performance (even on unseen trajectory maneuvers) and can be used in limited data scenarios.

关键词： Deep learning Transfer learning Stochastic processes Training data Kinematics Predictive models Trajectory

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

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

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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

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

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

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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

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Deep Learning in Palmprint Recognition-A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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Reduced Neural Network Based Ensemble Approach for Fault Detection and Diagnosis of Wind Energy Converter Systems

SSRN

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SSRN 2022年

作者： Mansouri, Majdi Dhibi, Khaled Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Electrical and Computer Engineering Program Texas A&M University Qatar Qatar Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia

Fault Detection and Diagnosis (FDD) is very important to achieve the best operation and ensure the safe and continuous operation of wind energy conversion (WEC) systems. Over the last decade, artificial neural networks surpass classical machine learning models in a variety of FDD tasks. On the other hand, ensemble learning (EL) techniques have gained significant attention from the scientific community. EL is a technique that creates and combines multiple machine learning models in order to produce one optimal predictive model which gives improved results. The goal of this paper is to develop and validate effective neural networks based ensemble approach. First, an ensemble classifier based on neural networks techniques and using bagging, boosting, and random subspace combination techniques is proposed. Second, an improved extension of the proposed neural networks-based ensemble technique is presented. The main idea behind this proposal is to determine variable relationships via a data-reduction framework using Hierarchical K-means (H-K-means) clustering method in order to overcome the problem of computation time and storage cost. Finally, the results obtained from the proposed neural networks-based ensemble techniques are compared with other ensemble and single machine learning methods to illustrate and validate the advantages of the proposed techniques. © 2022, The Authors. All rights reserved.

关键词： Neural networks

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