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  • Open Access

    ARTICLE

    Track Defects Recognition Based on Axle-Box Vibration Acceleration and Deep-Learning Techniques

    Xianxian Yin1, Shimin Yin1, Yiming Bu2, Xiukun Wei3,*

    Structural Durability & Health Monitoring, Vol.18, No.5, pp. 623-640, 2024, DOI:10.32604/sdhm.2024.050195 - 19 July 2024

    Abstract As an important component of load transfer, various fatigue damages occur in the track as the rail service life and train traffic increase gradually, such as rail corrugation, rail joint damage, uneven thermite welds, rail squats fastener defects, etc. Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit. In this paper, an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network, and the coexistence of the above-mentioned typical track defects in the track system… More >

  • Open Access

    ARTICLE

    An Intelligent Detection Method for Optical Remote Sensing Images Based on Improved YOLOv7

    Chao Dong, Xiangkui Jiang*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3015-3036, 2023, DOI:10.32604/cmc.2023.044735 - 26 December 2023

    Abstract To address the issue of imbalanced detection performance and detection speed in current mainstream object detection algorithms for optical remote sensing images, this paper proposes a multi-scale object detection model for remote sensing images on complex backgrounds, called DI-YOLO, based on You Only Look Once v7-tiny (YOLOv7-tiny). Firstly, to enhance the model’s ability to capture irregular-shaped objects and deformation features, as well as to extract high-level semantic information, deformable convolutions are used to replace standard convolutions in the original model. Secondly, a Content Coordination Attention Feature Pyramid Network (CCA-FPN) structure is designed to replace the… More >

  • Open Access

    ARTICLE

    Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

    Fawad Ali Shah1, Habib Akbar1, Abid Ali2,3, Parveen Amna4, Maha Aljohani5, Eman A. Aldhahri6, Harun Jamil7,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1385-1413, 2023, DOI:10.32604/csse.2023.036144 - 28 July 2023

    Abstract The detection of rice leaf disease is significant because, as an agricultural and rice exporter country, Pakistan needs to advance in production and lower the risk of diseases. In this rapid globalization era, information technology has increased. A sensing system is mandatory to detect rice diseases using Artificial Intelligence (AI). It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases. Deep Neural Network (DNN) is a novel technique that will help detect disease present on a rice leave… More >

  • Open Access

    REVIEW

    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

    Yanjun Yan, Kai Chen*, Hang Geng, Wenqian Fan, Xinrui Zhou

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1345-1379, 2023, DOI:10.32604/cmes.2023.027252 - 26 June 2023

    Abstract With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start More > Graphic Abstract

    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

  • Open Access

    ARTICLE

    Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

    Duo Ma1, 2, 3, Hongyuan Fang1, 2, 3, *, Binghan Xue1, 2, 3, Fuming Wang1, 2, 3, Mohammed A. Msekh4, Chiu Ling Chan5

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1267-1291, 2020, DOI:10.32604/cmes.2020.09122 - 28 May 2020

    Abstract The crack is a common pavement failure problem. A lack of periodic maintenance will result in extending the cracks and damage the pavement, which will affect the normal use of the road. Therefore, it is significant to establish an efficient intelligent identification model for pavement cracks. The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix. It has been widely used in geotechnical engineering, computer vision, medicine, and other fields. However, there are three major problems in the application of neural networks to… More >

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