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  • Open Access

    REVIEW

    Analysing Recent Breakthroughs in Fault Diagnosis through Sensor: A Comprehensive Overview

    Sumika Chauhan, Govind Vashishtha*, Radoslaw Zimroz

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1983-2020, 2024, DOI:10.32604/cmes.2024.055633 - 31 October 2024

    Abstract Sensors, vital elements in data acquisition systems, play a crucial role in various industries. However, their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance. Early fault detection is therefore critical for minimizing downtime and ensuring system reliability. This paper delves into the contemporary landscape of fault diagnosis techniques for sensors, offering valuable insights for researchers and academicians. The papers begin by exploring the different types and causes of sensor faults, followed by a discussion of the various fault diagnosis methods employed in industrial sectors. The advantages and limitations More >

  • Open Access

    ARTICLE

    Quantitative Identification of Delamination Damage in Composite Structure Based on Distributed Optical Fiber Sensors and Model Updating

    Hao Xu1, Jing Wang2, Rubin Zhu2, Alfred Strauss3, Maosen Cao4, Zhanjun Wu1,*

    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 785-803, 2024, DOI:10.32604/sdhm.2024.051393 - 20 September 2024

    Abstract Delamination is a prevalent type of damage in composite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in composite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameterized finite element model of the composite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations. The radial basis function neural network surrogate model is then constructed More >

  • Open Access

    ARTICLE

    Machine Fault Diagnosis Using Audio Sensors Data and Explainable AI Techniques-LIME and SHAP

    Aniqua Nusrat Zereen1, Abir Das2, Jia Uddin3,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886 - 12 September 2024

    Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

  • Open Access

    ARTICLE

    A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

    Jiajie Shen1, Yan Wang1,*, Dongxu Zhang2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.047903 - 20 June 2024

    Abstract Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable devices. Previous work have achieved impressive performance in classifying steady locomotion states. However, it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion states. Due to the similarities between the information of the transitions and their adjacent steady states. Furthermore, most of these methods rely solely on data and overlook the objective laws between physical activities, resulting in lower accuracy, particularly when encountering complex locomotion modes such as transitions.… More >

  • Open Access

    ARTICLE

    Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners: A Recommendation System

    Ameni Ellouze1, Nesrine Kadri2, Alaa Alaerjan3,*, Mohamed Ksantini1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 351-372, 2024, DOI:10.32604/cmc.2024.048061 - 25 April 2024

    Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the… More >

  • Open Access

    ARTICLE

    An Ingenious IoT Based Crop Prediction System Using ML and EL

    Shabana Ramzan1, Yazeed Yasin Ghadi2, Hanan Aljuaid3, Aqsa Mahmood1,*, Basharat Ali4

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 183-199, 2024, DOI:10.32604/cmc.2024.047603 - 25 April 2024

    Abstract Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers have no proper knowledge to select which crop is suitable to grow according to the environmental factors and soil characteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sector of the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning, and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC), and environmental factors like temperature to improve crop yield.… More >

  • Open Access

    ARTICLE

    Novel Sustainable Cellulose Acetate Based Biosensor for Glucose Detection

    M. F. Elkady1,2,*, E. M. El-Sayed2, Mahmoud Samy3, Omneya A. Koriem1, H. Shokry Hassan4,5

    Journal of Renewable Materials, Vol.12, No.2, pp. 369-380, 2024, DOI:10.32604/jrm.2023.046585 - 11 March 2024

    Abstract In this study, green zinc oxide (ZnO)/polypyrrole (Ppy)/cellulose acetate (CA) film has been synthesized via solvent casting. This film was used as supporting material for glucose oxidase (GOx) to sensitize a glucose biosensor. ZnO nanoparticles have been prepared via the green route using olive leaves extract as a reductant. ZnO/Ppy nanocomposite has been synthesized by a simple in-situ chemical oxidative polymerization of pyrrole (Py) monomer using ferric chloride (FeCl3) as an oxidizing agent. The produced materials and the composite films were characterized using X-ray diffraction analysis (XRD), scanning electron microscope (SEM), Fourier transform infrared (FTIR) and… More > Graphic Abstract

    Novel Sustainable Cellulose Acetate Based Biosensor for Glucose Detection

  • Open Access

    REVIEW

    AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

    Zain Anwar Ali1,2,*, Dingnan Deng1, Muhammad Kashif Shaikh3, Raza Hasan4, Muhammad Aamir Khan2

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 1-34, 2024, DOI:10.32604/csse.2023.041866 - 26 January 2024

    Abstract Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in… More >

  • Open Access

    ARTICLE

    Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

    Chunlin Xiong1,2, Zhengqiu Weng3,4,*, Jia Liu1, Liang Gu2, Fayez Alqahtani5, Amr Gafar6, Pradip Kumar Sharma7

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3013-3032, 2024, DOI:10.32604/cmes.2023.031088 - 15 December 2023

    Abstract With the development of hardware devices and the upgrading of smartphones, a large number of users save privacy-related information in mobile devices, mainly smartphones, which puts forward higher demands on the protection of mobile users’ privacy information. At present, mobile user authentication methods based on human-computer interaction have been extensively studied due to their advantages of high precision and non-perception, but there are still shortcomings such as low data collection efficiency, untrustworthy participating nodes, and lack of practicability. To this end, this paper proposes a privacy-enhanced mobile user authentication method with motion sensors, which mainly… More >

  • Open Access

    ARTICLE

    Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

    Narit Hnoohom1, Sakorn Mekruksavanich2, Anuchit Jitpattanakul3,4,*

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 139-151, 2023, DOI:10.32604/iasc.2023.033940 - 05 February 2024

    Abstract Accidents are still an issue in an intelligent transportation system, despite developments in self-driving technology (ITS). Drivers who engage in risky behavior account for more than half of all road accidents. As a result, reckless driving behaviour can cause congestion and delays. Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem. Previous research has also collected and analyzed a wide range of data, including electroencephalography (EEG), electrooculography (EOG), and photographs of the driver’s face. On the other hand, driving a car is a complicated action that requires… More >

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