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  • Open Access

    ARTICLE

    A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

    Shuai Chen1, Yinwei Ma2, Zhongshu Wang2, Zongmei Xu3, Song Zhang1, Jianle Li1, Hao Xu1, Zhanjun Wu1,*

    Structural Durability & Health Monitoring, Vol.18, No.2, pp. 125-141, 2024, DOI:10.32604/sdhm.2024.042594

    Abstract The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life. To this end, distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages, such as lightweight and ease of embedding. However, identifying the precise location of damage from the optical fiber signals remains a critical challenge. In this paper, a novel approach which namely Modified Sliding Window Principal Component Analysis (MSWPCA) was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors. The proposed method is able to extract signal… More > Graphic Abstract

    A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

  • Open Access

    ARTICLE

    Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

    Muhammad Aleem Raza1, Muhammad Anwar2, Kashif Nisar3, Ag. Asri Ag. Ibrahim3,*, Usman Ahmed Raza1, Sadiq Ali Khan4, Fahad Ahmad5

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3817-3834, 2023, DOI:10.32604/cmc.2023.032275

    Abstract With the help of computer-aided diagnostic systems, cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease. However, the early diagnosis of cardiac arrhythmia is one of the most challenging tasks. The manual analysis of electrocardiogram (ECG) data with the help of the Holter monitor is challenging. Currently, the Convolutional Neural Network (CNN) is receiving considerable attention from researchers for automatically identifying ECG signals. This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute (ANSI) standards and the Association… More >

  • Open Access

    ARTICLE

    Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

    Premanand S., Sathiya Narayanan*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 25-45, 2023, DOI:10.32604/cmc.2023.042590

    Abstract Machine Learning (ML) and Deep Learning (DL) technologies are revolutionizing the medical domain, especially with Electrocardiogram (ECG), by providing new tools and techniques for diagnosing, treating, and preventing diseases. However, DL architectures are computationally more demanding. In recent years, researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches, say for example, combining the convolutional layer blocks of Convolution Neural Networks (CNNs) into ML algorithms such as Extreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) resulting in CNN-XGBoost and CNN-KNN, respectively. However, these approaches are homogenous in the sense that they use a… More >

  • Open Access

    ARTICLE

    A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

    Maryam Bukhari1, Sadaf Yasmin1, Sheneela Naz2, Mehr Yahya Durrani1, Mubashir Javaid3, Jihoon Moon4, Seungmin Rho5,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1251-1279, 2023, DOI:10.32604/cmc.2023.040329

    Abstract Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics, which aid in the prevention of several diseases including heart-related abnormalities. In this context, regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram (ECG) signals has the potential to save many lives. In existing studies, several heart disease diagnostic systems are proposed by employing different state-of-the-art methods, however, improving such methods is always an intriguing area of research. Hence, in this research, a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals. The proposed framework extracts both linear and… More >

  • Open Access

    ARTICLE

    A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

    Guangfei Jia*, Fengwei Guo, Zhe Wu, Suxiao Cui, Jiajun Yang

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 383-405, 2023, DOI:10.32604/sdhm.2023.026885

    Abstract With the development of multi-signal monitoring technology, the research on multiple signal analysis and processing has become a hot subject. Mechanical equipment often works under variable working conditions, and the acquired vibration signals are often non-stationary and nonlinear, which are difficult to be processed by traditional analysis methods. In order to solve the noise reduction problem of multiple signals under variable speed, a COT-DCS method combining the Computed Order Tracking (COT) based on Chirplet Path Pursuit (CPP) and Distributed Compressed Sensing (DCS) is proposed. Firstly, the instantaneous frequency (IF) is extracted by CPP, and the speed is obtained by fitting.… More > Graphic Abstract

    A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

  • Open Access

    ARTICLE

    A Time-Varying Parameter Estimation Method for Physiological Models Based on Physical Information Neural Networks

    Jiepeng Yao1,2, Zhanjia Peng1,2, Jingjing Liu1,2, Chengxiao Fan1,2, Zhongyi Wang1,2,3, Lan Huang1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2243-2265, 2023, DOI:10.32604/cmes.2023.028101

    Abstract In the establishment of differential equations, the determination of time-varying parameters is a difficult problem, especially for equations related to life activities. Thus, we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations. In the proposed framework, the learnable factors and scale parameters are used to implement adaptive activation functions, and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks. In this paper, taking the electrophysiological differential… More >

  • Open Access

    ARTICLE

    Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

    Muhammad Tayyeb1, Muhammad Umer1, Khaled Alnowaiser2, Saima Sadiq3, Ala’ Abdulmajid Eshmawi4, Rizwan Majeed5, Abdullah Mohamed6, Houbing Song7, Imran Ashraf8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1677-1694, 2023, DOI:10.32604/cmes.2023.026535

    Abstract Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately. Currently, electrocardiogram (ECG) data is analyzed by medical experts to determine the cardiac abnormality, which is time-consuming. In addition, the diagnosis requires experienced medical experts and is error-prone. However, automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures. This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction to reduce computational complexity. ECG dataset containing averaged signals with window size 10 is… More >

  • Open Access

    ARTICLE

    Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

    C. S. S. Anupama1, Rafina Zakieva2, Afanasiy Sergin3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Chomyong Kim8, Yunyoung Nam8,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1453-1468, 2023, DOI:10.32604/iasc.2023.038321

    Abstract Gait is a biological typical that defines the method by that people walk. Walking is the most significant performance which keeps our day-to-day life and physical condition. Surface electromyography (sEMG) is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent. Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment. Several approaches are established in the works for gait recognition utilizing conventional and deep learning (DL) approaches. This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning… More >

  • Open Access

    ARTICLE

    Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

    Saeed Mohsen1,2,*, Sherif S. M. Ghoneim3, Mohammed S. Alzaidi3, Abdullah Alzahrani3, Ashraf Mohamed Ali Hassan4

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5271-5286, 2023, DOI:10.32604/cmc.2023.038758

    Abstract Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast… More >

  • Open Access

    ARTICLE

    Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

    Jiali Wang1, Bing Li2, Chengyu Qiu1, Xinyun Zhang1, Yuting Cheng1, Peihua Wang1, Ta Zhou3, Hong Ge2, Yuanpeng Zhang1,3,*, Jing Cai3,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4843-4866, 2023, DOI:10.32604/cmc.2023.037457

    Abstract Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can protect the security of source… More >

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