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Iranian Conference of Biomedical engineering (ICBME)

作者： Mohammad Almasganj Emad Fatemizadeh Department of Electrical Engineering Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran

Retinal optical coherence tomography (OCT) images are widely used to diagnose and grade macular diseases, such as age-related macular degeneration (AMD). However, manual interpretation of OCT images is time-consuming and subjective. Therefore, automated and accurate classification of OCT images is essential for assisting ophthalmologists in clinical decision-making. This paper proposes a pyramidal deep neural network that can diagnose normal and two types of AMD (dry and wet) in OCT images. Our network leverages features from different scales of a pre-trained convolutional neural network (CNN) and integrates them with two advanced versions of feature pyramid networks: bidirectional feature pyramid network (BiFPN) and path aggregation network (PANet). We evaluate our network on the NEH dataset and compare it with its predecessor. Our results show that our BiFPN-VGG16 and PAN-VGG16 models achieve accuracies of 94.S% and 95.0%, respectively, which are 2.8 to 3% higher than the previous models. Our approach demonstrates the potential of multi-scale feature networks for OCT image classification and can serve as an auxiliary diagnostic tool for ophthalmologists.

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Efficient Fault Detection in Nonlinear Industrial Process: A Reduced Kernel PCA-Based Spectral Clustering Approach

Efficient Fault Detection in Nonlinear Industrial Process: A...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Kais Bouzrara Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The challenges posed by nonlinearities in industrial systems necessitate innovative techniques that outperform the limitations of traditional methods such as principal component analysis (PCA). While Kernel Principal Component Analysis (KPCA) offers a robust solution to handle nonlinear data, its computational requirements are a significant issue, especially for large-sized datasets. In this work, we propose a novel technique, namely, reduced kernel principal component analysis-based spectral clustering (RKPCA SpC ), to monitor and detect faults in the benchmark Tennessee Eastman process. The suggested approach addresses the complexity associated with KPCA by reducing data size during the model training phase. This reduction involves retaining only the principal components, preserving informative features, and selecting pertinent samples without compromising the original data's content. The efficacy of the proposed method is evaluated through key performance metrics, including false alarm rate (FAR), missed detection rate (MDR), detection time delay (DTD), and computation time (CT). Additionally, gained execution time (GET), gained storage space (GSP), and loss function (LF) are considered, providing a comprehensive assessment of the developed paradigms' effectiveness. The results demonstrate the promising capabilities of our proposed scheme.

关键词： Training Measurement Technological innovation Fault detection Delay effects Computational modeling Complexity theory Kernel Monitoring Principal component analysis

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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Maintenance dynamic scheduling for a Tunisian railway transport system

Maintenance dynamic scheduling for a Tunisian railway transp...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Sarra Mellouli Anis Mhalla Simon Collart Dutilleul Hassani Messaoud Laboratory of Automatic Signal and Image Processing LARATSI National Engineering School of Monastir (ENIM) Monastir Tunisia Laboratory of Automation Electrical Systems Environment LAESE National Engineering School of Monastir (ENIM) Monastir Tunisia COSYS Department ESTAS Laboratory University Gustave Eiffel Lille France

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

The developments reported in this paper are devoted to the dynamic scheduling of rail transport systems. In this context, our study focuses on implementing an algorithm for solving railway planning with predictable or unexpected demands. The system modeling process uses P-time stochastic Petri nets. In order to improve the efficiency and punctuality of rail transport. A method for minimizing disruptions by managing maintenance is proposed. The paper's contribution is a scheduling algorithm that prioritizes maintenance tasks according to a dynamic criticality. This dynamic criterion incorporates real-time information provided by a supervisory rail system layer (a SCADA system). The STPN is used for modeling system operation. It supplies a mathematical framework for disturbance detection and prognosis. The presented work constitutes a first step towards adapting the digital twin concepts for the maintenance of weakly instrumented railway infrastructures.

关键词： Rails Job shop scheduling Heuristic algorithms SCADA systems Dynamic scheduling Prediction algorithms Rail transportation

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Constrained Energy Minimization (CEM) for Hyperspectral Target Detection: Theory and Generalizations

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷

作者： Chang, Chein-I. Information and Technology College Dalian116026 China University of Maryland Baltimore County Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

Target detection is a fundamental task of hyperspectral imaging where constrained energy minimization (CEM) has been widely used for subpixel target detection techniques. Due to its effectiveness, CEM has been generalized to various versions, such as kernel CEM (KCEM), kernel target-constrained interference-minimized filter (KTCIMF), ensemble cascaded CEM (ECEM), and hierarchical CEM (HCEM). Unfortunately, these generalizations overlooked the key design rationale behind CEM. This article revisits CEM for hyperspectral target detection (HTD) and proves how and why it works mathematically. Specifically, several new CEM generalizations are derived and particularly noteworthy. By including spatial information in an iterative process, KCEM, ECEM, and HCEM can be generalized to iterative KCEM (IKCEM), iterative KTCIMF (IKTCIMF), iterative ECEM (IECEM), and iterative HCEM (IHCEM). Also, by utilizing an iterative random training sampling (IRTS) to generate the desired target signature to be detected, these algorithms are further generalized to IRTS KCEM (IRTS-KCEM), IRTS ECEM (IRTS-ECEM), and IRTS HCEM (IRTS-HCEM). A comprehensive analysis along with comparative study on these generalizations is conducted through extensive experiments to demonstrate the effectiveness of IKCEM, IHCEM, and IECEM. © 1980-2012 IEEE.

关键词： Iterative methods

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Speech Stuttering Detection and Removal Using Deep Neural Networks 11th

Speech Stuttering Detection and Removal Using Deep Neural Ne...

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11th International Conference on Computer engineering and Networks, CENet2021

作者： Rajput, Shaswat Nersisson, Ruban Raj, Alex Noel Joseph Mary Mekala, A. Frolova, Olga Lyakso, Elena School of Electrical Engineering Vellore Institute of Technology Vellore India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou China School of Information Technology and Engineering Vellore Institute of Technology Vellore India St. Petersburg State University Saint Petersburg Russia

ISBN: (纸本)9789811665530

There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech *** format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model’s accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate (WER) for most of the test cases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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CNN based Emotion Detection in Cross Linguistic Children speech 24

CNN based Emotion Detection in Cross Linguistic Children spe...

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24th International Congress on Acoustics, ICA 2022

作者： Lyakso, Elena Frolova, Olga Ruban, Nersisson Mekala, A. Mary Joseph Raj, Alex Noel Matveev, Anton Matveev, Yuri The Child Speech Research Group St. Petersburg State University St. Petersburg Russia School of Electrical Engineering VIT Vellore India School of Information Technology & Engineering VIT Vellore India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronics Engineering College of Engineering Shantou University China

The goal of this cross-linguistic study is to analyze the speech of children in the age group of 8-12 years and automatic classification of children's emotions from various speech data in Russian and Tamil languages. A standardized approach for the collection of speech (spontaneous and acting speech - emotional words, phrases, and meaningless texts) was used. Two emotional child speech corpora include the emotional speech of 95 Russian children and 40 Indian children were created. Annotation of the emotional speech was carried out in four categories "joy - neutral - sadness – anger" by two speech specialists of the same nationality of the child. The set of 2505 labeled audio files of Russian children and 418 labeled audio files of Tamil children were used. The SVM classifier shows slightly better results in Russian language, and the MLP classifier in Tamil. Inter-Cultural approach (mixed dataset of Russian and Indian speech) revealed that the accuracy of recognition of all emotions remains higher. In Cross-Cultural approach, on samples of Tamil speech, anger state is recognized better, and in samples of Russian speech – sad state. Using 5 Layered CNN Model, the accuracy was revealed for Russian speech is higher than for Indian children. © 2022 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Emotion Recognition

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Reduced KPCA based Ensemble Learning Approach for Fault Diagnosis of Grid-Connected PV Systems 19

Reduced KPCA based Ensemble Learning Approach for Fault Diag...

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

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

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The main objective of this work is to develop novel fault diagnosis techniques using ensemble learning and multivariate statistical techniques. The proposed methods are capable of identifying and classifying PV faults. First, an ensemble learning (EL) method that combines several base models is proposed. Next, kernel principal components analysis (KPCA) and reduced KPCA are proposed to extract and select the pertinent characteristics from raw data. Then, the extracted significant characteristics are transmitted to the EL model for classification purposes. The main idea behind these proposals is to provide the best accuracy and also improve the results in terms of computation time. The diagno-sis results demonstrated the efficiency of the proposed frameworks. © 2022 IEEE.

关键词： Multivariant analysis

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Overview of EMG signal Preprocessing and Classification for Bionic Hand Control

Overview of EMG Signal Preprocessing and Classification for ...

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Information Technologies & Smart Industrial Systems (ITSIS), IEEE

作者： Monaam Ayachi Hassene Seddik Electrical department RIFTSI research laboratory: intelligent robotics reliability and image signal processing Higher National Engineering School of Tunis (ENSIT) University of Tunis Tunis

This paper deals with the field of Biological signal especially one of the most used signals in the field of prosthetic devices, which is the EMG signal. After introducing this kind of signal, we will talk about different techniques used for preprocessing it in order to prepare it for classification tasks. We Will then overview different noises that affect those sensible type of signals, different techniques applied for filtering them and the useful envelope detection methods that can be applied on EMG. The next part of this paper will look at some famous feature extraction and classification techniques that proved themselves useful when dealing with classification tasks of biological signals. Finally, we will present the mechanical design of a multiarticulate multifunctional prosthetic hand, the promising classification results that can gives 98.3% and our perspectives.

关键词： Support vector machines Filtering Shape Feature extraction Electromyography Biology Noise measurement

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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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