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

作者： Amal Hichri 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 Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.

关键词： Fault diagnosis Photovoltaic systems Embedded systems Accuracy Fault detection Decision making Artificial neural networks Hyperparameter optimization Transient analysis Genetic algorithms

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Decoding Sleep: Microphone-Based Snoring Analysis using Embedded Machine Learning for Obstructive Sleep Apnea Detection

Decoding Sleep: Microphone-Based Snoring Analysis using Embe...

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International Conference on Biosignals, images and Instrumentation (ICBSII)

作者： Delpha Jacob Priyanka Kokil Subramanian S Jayanthi Thiruvengadam Department of Biomedical Engineering College of Engineering and Technology SRM Institute of Science and Technology Chennai Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Chennai Department of Respiratory Medicine SRM Medical College Hospital and Research Centre Kattankulathur Tamil Nadu India

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Snoring, a recurring habit often disregarded within the Indian community, can signal a grave underlying issue of Obstructive Sleep Apnea (OSA). OSA is a severe sleep disorder characterized by recurrent interruptions in breathing for more than 10 seconds during sleep, typically due to partial or complete airway obstructions. Neglecting OSA can lead to a range of significant health risks, including increased likelihood of occupational accidents, motor vehicle accidents, heightened susceptibility to severe depression, cardiac and cerebrovascular diseases, and reduced life expectancy. The main objective of the study is to detect snoring while at sleep and also to classify it as normal snoring and OSA snoring. Arduino nano 33 BLE sense is used to capture the snore signal, it houses a built-in MP34DT05 sensor. The sensor has a signal-to-noise ratio of 64dB and sensitivity of - 26dBFS ± 3dB. This captures the sound signal of the individual, it is further processed to extract the Mel-filter bank energy features, Mel Frequency Cepstral Coefficients and Spectrogram features. The features are further used to build a model and the same is trained using edge impulse to classify the signal. The dataset is divided into training, testing, and validation sets, with 80% of the data allocated to training, 20% to testing, and an additional 20% within the training data set aside for validation purposes. The results for the two class classification (snoring and non snoring) indicate that the spectrogram-based approach achieved an accuracy rate of 96.9%, while the other two methods yielded accuracy rates of 93.8%. The accuracy for three class classification (normal, snoring and OSA snoring) using the Embedded Machine Learning (EML) approach is 88%. The proposed study demonstrates enhanced accuracy in identifying OSA by snoring compared to previous research. This autonomous system can facilitate the detection of OSA through the analysis of snoring patterns, subsequently alerting the subjec

关键词： Training Performance evaluation Accuracy Embedded systems Training data Machine learning Feature extraction

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Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization

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IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia 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 Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

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Explainable Earth Surface Forecasting under Extreme Events

arXiv

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arXiv 2024年

作者： Pellicer-Valero, Oscar J. Fernández-Torres, Miguel-Ángel Ji, Chaonan Mahecha, Miguel D. Camps-Valls, Gustau Universitat de València València46980 Spain Leipzig University Leipzig04103 Germany The Institute for Earth System Science and Remote Sensing Leipzig University Leipzig04103 Germany The Image and Signal Processing Group Leipzig University Leipzig04109 Germany The Helmholtz Centre for Environmental Research – UFZ Leipzig04318 Germany

With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystems. This is, however, impeded by the complexity of processing, visualizing, modeling, and explaining this data. To showcase how this challenge can be met, here we train a convolutional long short-term memory-based architecture on the novel DeepExtremeCubes dataset. DeepExtremeCubes includes around 40,000 long-term Sentinel-2 minicubes (January 2016-October 2022) worldwide, along with labeled extreme events, meteorological data, vegetation land cover, and topography map, sampled from locations affected by extreme climate events and surrounding areas. When predicting future reflectances and vegetation impacts through kernel normalized difference vegetation index, the model achieved an R2 score of 0.9055 in the test set. Explainable artificial intelligence was used to analyze the model’s predictions during the October 2020 Central South America compound heatwave and drought event. We chose the same area exactly one year before the event as counterfactual, finding that the average temperature and surface pressure are generally the best predictors under normal conditions. In contrast, minimum anomalies of evaporation and surface latent heat flux take the lead during the event. A change of regime is also observed in the attributions before the event, which might help assess how long the event was brewing before happening. The code to replicate all experiments and figures in this paper is publicly available at https://***/DeepExtremes/txyXAI. © 2024, CC BY.

关键词： Atmospheric pressure

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A Deep Learning-Based Pipeline for Multi-Class Motor imagery Problems with Small Portion of Labeled Datasets

A Deep Learning-Based Pipeline for Multi-Class Motor Imagery...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Neda Abdollahpour Mohammadreza Yazdchi Zahra Baharlouei Department of Biomedical Engineering Ragheb Isfahani Institute of Higher Education Isfahan Iran Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran

In this article, a new framework is proposed to address multi-class Motor imagery Brain-Computer Interface (MIBCI) problems containing a small portion of labeled datasets. In this framework, the combination of Independent Component Analysis (ICA), multi-class Common Spatial Pattern (CSP), and a functional Application Programming Interface (API) model assumes a pivotal role. In the feature extraction stage of the work, a concatenated altered signal affected by spatial weights is proposed for each trial in three frequency ranges. This distribution of features can both provide suitable feature maps for augmentation, preparing data for the deep learning analysis, and underscore distinguishable features of MI classes. In the classification stage, spatial and temporal features are dominated by using the effective combination of a one-dimensional Convolutional Neural Network (CNN) and a two-staged Bidirectional Long Short-Term Memory (BLSTM) in three branches containing different distributions of frequency. Given that, the model simultaneously learns past-to-future and future-to-past patterns in two stages. The experimental result on datasets 2a BCI-Competition IV illustrates that the proposed method can be liable, practical and more competitive than the other popular methods pointed out in this paper. All in all, the proposed framework can alleviate the issue of small portions of labeled datasets in MI problems.

关键词： Deep learning Biological system modeling Pipelines Independent component analysis Feature extraction Brain modeling Brain-computer interfaces

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EEG Artifact Removal Based on Brain Dipoles' Regions Using ICA and Dipfit in Motor imagery Tasks

EEG Artifact Removal Based on Brain Dipoles' Regions Using I...

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Iranian Conference of Biomedical Engineering (ICBME)

In this article, a new semi-automatic Electroencephalogram (EEG) artifact removal has been proposed for Motor imagery (MI) tasks to improve the system's performance. There are eight reference clusters whose locations have been calculated based on the precise coordinates of a copious amount of brain dipoles acquired from a large number of users performing MI tasks. In this method, called 8 Ref-Clusters, a kind of Blind Source Separation (BSS) algorithm along with the DIPFIT plugin of the EEGLAB platform take on a decisive role. These eight clusters demonstrate which dipoles are brain sources and which ones are artifacts to eliminate. In the case of improving the performance of a system for a particular subject, we defined a specific threshold that could alter the size of clusters in three dimensions. The elaborate threshold pointed out above is unquestionably user-dependent. Having made a comparison between results before and after applying the 8-Ref-Clusters on the BCI-Competition IV 2a datasets, the average performance increased by roughly 4% which is promising when the datasets used in these evaluations had been filtered, between 8 and 30 Hz, only before applying Independent Component Analysis (ICA). Making a comparison between the results of the proposed method and those of other CSP-based methods shows that applying the proposed artifact removal only before CSP can significantly enhance the performance of the system at about 15.6%, in the case of the mCSP method. All in all, the proposed artifact removal method is a semi-automatic method that is computationally fast and able to detect the various types of artifacts like a heartbeat, muscle and head movements, eye blink, line noise, and so on.

关键词： Training Head Heart beat System performance Independent component analysis Muscles Lead

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Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional Neural Networks

arXiv

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arXiv 2024年

作者： Wu, Fuzhi Wu, Jiasong Kong, Youyong Yang, Chunfeng Yang, Guanyu Shu, Huazhong Carrault, Guy Senhadji, Lotfi Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Bangladesh Laboratoire Traitement du Signal et de l'Image Univ Rennes France Centre de Recherche en Information Biomédicale Sino-français CRIBs France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Bangladesh

Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite the promising performance of transformer structures in numerous tasks, their intricate optimization complexities highlight the persistent need for refined CNN enhancements using limited resources. Responding to these complexities, we introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keeping their complexity unchanged. The MLFM efficiently preserves low-frequency information, enhancing performance in targeted computer vision tasks. Central to our MLFM is the Low-Frequency Memory Unit (LFMU), which stores various low-frequency data and forms a parallel channel to the core network. A key advantage of MLFM is its seamless compatibility with various prevalent networks, requiring no alterations to their original core structure. Testing on imageNet demonstrated substantial accuracy improvements in multiple 2D CNNs, including ResNet, MobileNet, EfficientNet, and ConvNeXt. Furthermore, we showcase MLFM's versatility beyond traditional image classification by successfully integrating it into image-to-image translation tasks, specifically in semantic segmentation networks like FCN and U-Net. In conclusion, our work signifies a pivotal stride in the journey of optimizing the efficacy and efficiency of CNNs with limited resources. This research builds upon the existing CNN foundations and paves the way for future advancements in computer vision. Our codes are available at https://***/AlphaWuSeu/MLFM. © 2024, CC BY-NC-ND.

关键词： Computer vision

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Multi-frequency PolSAR image Fusion Classification Based on Semantic Interactive Information and Topological Structure

arXiv

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arXiv 2022年

作者： Cao, Yice Wu, Yan Li, Ming Zheng, Mingjie Zhang, Peng Wang, Jili The Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi’an710071 China The National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

Compared with the rapid development of single-frequency multi-polarization SAR image classification technology, there is less research on the land cover classification of multifrequency polarimetric SAR (MF-PolSAR) images. In addition, the current deep learning methods for MF-PolSAR classification are mainly based on convolutional neural networks (CNNs), only local spatiality is considered but the nonlocal relationship is ignored. Therefore, based on semantic interaction and nonlocal topological structure, this paper proposes the MF semantics and topology fusion network (MF-STFnet) to improve MF-PolSAR classification performance. In MF-STFnet, two kinds of classification are implemented for each band, semantic information-based (SIC) and topological property-based (TPC). They work collaboratively during MF-STFnet training, which can not only fully leverage the complementarity of bands, but also combine local and nonlocal spatial information to improve the discrimination between different categories. For SIC, the designed cross-band interactive feature extraction module (CIFEM) is embedded to explicitly model the deep semantic correlation among bands, thereby leveraging the complementarity of bands to make ground objects more separable. For TPC, the graph sample and aggregate network (GraphSAGE) is employed to dynamically capture the representation of nonlocal topological relations between land cover categories. In this way, the robustness of classification can be further improved by combining nonlocal spatial information. Finally, an adaptive weighting fusion (AWF) strategy is proposed to merge inference from different bands, so as to make the MF joint classification decisions of SIC and TPC. The effectiveness of the proposed modules is proved by ablation experiments on three measured MF-PolSAR datasets. In addition, the comparative experiments show that MF-STFnet can achieve more competitive classification performance than some state-of-the-art methods. © 2022, CC

关键词： image classification

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Deep Learning based Fault Diagnosis in a Grid-Connected Photovoltaic Systems 19

Deep Learning based Fault Diagnosis in a Grid-Connected Phot...

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

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kasserine Tunisia Electrical and Computer Engineering Program Texas A&m University at Qatar Doha23874 Qatar Prince Sultan University Department of Mathematics and Sciences Riyadh11586 Saudi Arabia Signal Processing and Image National Engineering School of Monastir Research Laboratory of Automation 5019 Tunisia Chemical Engineering Program Texas A&m University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Photovoltaic (PV) system fault diagnosis is crucial because it helps PV system operators reduce energy and income losses. It also decreases the risk of fire and electric shock from PV system failures. Thus, the implementation of fault diagnosis tool is required. The most well-known techniques are machine learning models. Although these techniques provided well in terms of diagnosis and classification accuracy, they rely on manual feature extraction, which is time consuming, expensive, and requires diagnostic skill. Manual feature extraction is a problem that has to be addressed. This paper therefore presents a PV system fault diagnosis techniques that can automatically extract features from raw data in order to classify PV system faults. The implemented methods include long short-term memory (LSTM) bidirectional long short-term memory (BiLSTM) networks, which are deep learning based algorithms. Finally, the presented fault diagnosis approaches showed a good diagnosis performances when applied to an emulated GCPV system. © 2022 IEEE.

关键词： Failure analysis

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Mitigation of Cross-modulation Effects in Radar Receivers with Memory

Mitigation of Cross-modulation Effects in Radar Receivers wi...

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IEEE International Conference on Radar

作者： Euan Ward Bernard Mulgrew Institute for Digital Communications Joint Research Institute in Signal and Image Processing School of Engineering and Electronics The University of Edinburgh Edinburgh UK

ISBN: (数字)9781728168135

ISBN: (纸本)9781728168142

Modern mass produced radar systems offer a wide variety of advantages over their bespoke predecessors however to achieve these benefits the linearity of their RF receivers is often sacrificed. This compromise leaves these modern sensors susceptible to unwanted cross-modulation effects and with the RF spectrum becoming increasingly crowded this problem is only going to become more pertinent. If mitigation techniques are to be developed they must correct for the nonlinear distortion applied to both the amplitude and phase of the returned radar signal. In order to accurately model nonlinear phase effects in the radar receiver we must consider memory. This paper studies how introducing nonlinear memory in the radar receiver affects the cross-modulation distortion generated. It also presents a simple cross-modulation mitigation technique developed in the communications literature and applies it to the radar cross-modulation problem. As a result the simple communications based mitigation technique successfully corrects for cross-modulation distortion generated from a memoryless nonlinear radar receiver. However, it is ineffective when nonlinear memory effects are introduced. This is a significant result as it suggests that if the nonlinearities generating cross-modulation effects in modern radar are found to have memory then sophisticated memory mitigation techniques will have to be developed.

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