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  • Open Access

    ARTICLE

    MPFracNet: A Deep Learning Algorithm for Metacarpophalangeal Fracture Detection with Varied Difficulties

    Geng Qin1, Ping Luo1, Kaiyuan Li1, Yufeng Sun1, Shiwei Wang1, Xiaoting Li1,2,3, Shuang Liu1,2,3, Linyan Xue1,2,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 999-1015, 2023, DOI:10.32604/cmc.2023.035777 - 06 February 2023

    Abstract Due to small size and high occult, metacarpophalangeal fracture diagnosis displays a low accuracy in terms of fracture detection and location in X-ray images. To efficiently detect metacarpophalangeal fractures on X-ray images as the second opinion for radiologists, we proposed a novel one-stage neural network named MPFracNet based on RetinaNet. In MPFracNet, a deformable bottleneck block (DBB) was integrated into the bottleneck to better adapt to the geometric variation of the fractures. Furthermore, an integrated feature fusion module (IFFM) was employed to obtain more in-depth semantic and shallow detail features. Specifically, Focal Loss and Balanced… More >

  • Open Access

    ARTICLE

    Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors

    Majid Hussain1,2,*, Tayab Din Memon3,4, Imtiaz Hussain5, Zubair Ahmed Memon3, Dileep Kumar2

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 435-470, 2022, DOI:10.32604/cmes.2022.020583 - 21 July 2022

    Abstract Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL)… More >

  • Open Access

    ARTICLE

    Deep Learning Based Power Transformer Monitoring Using Partial Discharge Patterns

    D. Karthik Prabhu1,*, R. V. Maheswari2, B. Vigneshwaran2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1441-1454, 2022, DOI:10.32604/iasc.2022.024128 - 25 May 2022

    Abstract Measurement and recognition of Partial Discharge (PD) in power apparatus is considered a protuberant tool for condition monitoring and assessing the state of a dielectric system. During operating conditions, PD may occur either in the form of single and multiple patterns in nature. Currently, for PD pattern recognition, deep learning approaches are used. To evaluate spatial order less features from the large-scale patterns, a pre-trained network is used. The major drawback of traditional approaches is that they generate high dimensional data or requires additional steps like dictionary learning and dimensionality reduction. However, in real-time applications,… More >

  • Open Access

    ARTICLE

    Reconstruction Technology of Flexible Structure Shape Based on FBG Sensor Array and Deep Learning Algorithm

    Kelong Huang, Jie Yan, Lei Zhang*, Faye Zhang, Mingshun Jiang, Qingmei Sui

    Structural Durability & Health Monitoring, Vol.16, No.2, pp. 179-194, 2022, DOI:10.32604/sdhm.2022.018202 - 25 April 2022

    Abstract A structural displacement field reconstruction method is proposed to aim at the problems of deformation monitoring and displacement field reconstruction of flexible plate-like structures in the aerospace field. This method combines the deep neural network model of the cross-layer connection structure with the fiber grating sensor network. This paper first introduces the principle of strain detection of fiber grating sensor, studies the mapping relationship between strain and displacement, and proposes a strain-displacement conversion model based on an improved neural network. Then the intelligent structure deformation monitoring system is built. By controlling the stepping distance of… More >

  • Open Access

    ARTICLE

    Improved Lightweight Deep Learning Algorithm in 3D Reconstruction

    Tao Zhang1,*, Yi Cao2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5315-5325, 2022, DOI:10.32604/cmc.2022.027083 - 21 April 2022

    Abstract The three-dimensional (3D) reconstruction technology based on structured light has been widely used in the field of industrial measurement due to its many advantages. Aiming at the problems of high mismatch rate and poor real-time performance caused by factors such as system jitter and noise, a lightweight stripe image feature extraction algorithm based on You Only Look Once v4 (YOLOv4) network is proposed. First, Mobilenetv3 is used as the backbone network to effectively extract features, and then the Mish activation function and Complete Intersection over Union (CIoU) loss function are used to calculate the improved More >

  • Open Access

    ARTICLE

    Object Detection for Cargo Unloading System Based on Fuzzy C Means

    Sunwoo Hwang1, Jaemin Park1, Jongun Won2, Yongjang Kwon3, Youngmin Kim1,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4167-4181, 2022, DOI:10.32604/cmc.2022.023295 - 07 December 2021

    Abstract With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to More >

  • Open Access

    ARTICLE

    Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

    Yuzhen Liu1,3, Xiaoliang Wang1,*, Xinghui She1, Ming Yi1, Yuelong Li1, Frank Jiang2

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2261-2276, 2021, DOI:10.32604/cmc.2021.015501 - 21 July 2021

    Abstract An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes, and help people live well in mosquito-infested areas. In this study, we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things. In our method, decision-making is controlled by a deep learning model, and the proposed method uses infrared sensors and an array of pressure sensors to collect data. Moreover the ZigBee protocol is used to… More >

  • Open Access

    ARTICLE

    Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

    Muhammad Tayyab1,*, Mohsen Marjani1, N. Z. Jhanjhi1, Ibrahim Abaker Targio Hashim2, Abdulwahab Ali Almazroi3, Abdulaleem Ali Almazroi4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1183-1200, 2021, DOI:10.32604/cmc.2021.017199 - 04 June 2021

    Abstract Deep learning (DL) algorithms have been widely used in various security applications to enhance the performances of decision-based models. Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models. The two most common active attacks are poisoning and evasion attacks, which can cause various problems, including wrong prediction and misclassification of decision-based models. Therefore, to design an efficient DL model, it is crucial to mitigate these attacks. In this regard, this study proposes a secure neural network (NN) model that provides data security during model training… More >

  • Open Access

    ARTICLE

    Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

    Pandia Rajan Jeyaraj1, Aravind Chellachi Kathiresan1, Siva Prakash Asokan1, Edward Rajan Samuel Nadar1, Hegazy Rezk2,3,*, Thanikanti Sudhakar Babu4

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 553-567, 2021, DOI:10.32604/cmc.2021.015128 - 22 March 2021

    Abstract The power transfer capability of the smart transmission grid-connected networks needs to be reduced by inter-area oscillations. Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks. This fact is more noticeable in smart grid-connected systems. The smart grid infrastructure has more renewable energy resources installed for its operation. To overcome this problem, a deep learning wide-area controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes. The proposed Deep Wide Area Controller (DWAC) uses the Deep Belief Network (DBN). The network… More >

  • Open Access

    ARTICLE

    Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

    Theyazn H. H. Aldhyani1,*, Melfi Alrasheed2, Mosleh Hmoud Al-Adaileh3, Ahmed Abdullah Alqarni4, Mohammed Y. Alzahrani4, Ahmed H. Alahmadi5

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2141-2160, 2021, DOI:10.32604/cmc.2021.014498 - 05 February 2021

    Abstract The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects. Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives. In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus. The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict confirmed numbers and death cases. The real time data used has been collected from the World Health Organization (WHO). In… More >

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