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  • Open Access

    ARTICLE

    Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

    P. Samuel Pakianathan*, R. V. Maheswari

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2717-2736, 2023, DOI:10.32604/iasc.2023.029950

    Abstract This research investigates the dielectric performance of Natural Ester (NE) using the Partial Differential Equation (PDE) tool and analyzes dielectric performance using fuzzy logic. NE nowadays is found to replace Mineral Oil (MO) due to its extensive dielectric properties. Here, the heat-tolerant Natural Esters Olive oil (NE1), Sunflower oil (NE2), and Ricebran oil (NE3) are subjected to High Voltage AC (HVAC) under different electrodes configurations. The breakdown voltage and leakage current of NE1, NE2, and NE3 under Point-Point (P-P), Sphere-Sphere (S-S), Plane-Plane (PL-PL), and Rod-Rod (R-R) are measured, and survival probability is presented. The electric field distribution is analyzed using… More >

  • Open Access

    ARTICLE

    Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

    Mesfer Al Duhayyim1,*, Areej A. Malibari2, Marwa Obayya3, Mohamed K. Nour4, Ahmed S. Salama5, Mohamed I. Eldesouki6, Abu Sarwar Zamani7, Mohammed Rizwanullah7

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5473-5489, 2022, DOI:10.32604/cmc.2022.031976

    Abstract The skeletal bone age assessment (BAA) was extremely implemented in development prediction and auxiliary analysis of medicinal issues. X-ray images of hands were detected from the estimation of bone age, whereas the ossification centers of epiphysis and carpal bones are important regions. The typical skeletal BAA approaches remove these regions for predicting the bone age, however, few of them attain suitable efficacy or accuracy. Automatic BAA techniques with deep learning (DL) methods are reached the leading efficiency on manual and typical approaches. Therefore, this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with… More >

  • Open Access

    ARTICLE

    Aluminum Alloy Fatigue Crack Damage Prediction Based on Lamb Wave-Systematic Resampling Particle Filter Method

    Gaozheng Zhao1, Changchao Liu1, Lingyu Sun1, Ning Yang2, Lei Zhang1, Mingshun Jiang1, Lei Jia1, Qingmei Sui1,*

    Structural Durability & Health Monitoring, Vol.16, No.1, pp. 81-96, 2022, DOI:10.32604/sdhm.2022.016905

    Abstract Fatigue crack prediction is a critical aspect of prognostics and health management research. The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth, and it is widely used to predict the state of fatigue crack. This paper proposes a method of lamb wave-based early fatigue microcrack prediction with the aid of particle filters. With this method, which the changes in signal characteristics under different fatigue crack lengths are analyzed, and the state- and observation-equations of crack extension are established. Furthermore, an experiment is conducted to verify the feasibility… More >

  • Open Access

    ARTICLE

    Research on Flashover Voltage Prediction of Catenary Insulator Based on CaSO4 Pollution with Different Mass Fraction

    Sihua Wang1,2, Junjun Wang1,2,*, Lijun Zhou1,2, Long Chen1,2, Lei Zhao1,2

    Energy Engineering, Vol.119, No.1, pp. 219-236, 2022, DOI:10.32604/EE.2022.016899

    Abstract

    Pollution flashover accidents occur frequently in railway OCS in saline-alkali areas. To accurately predict the pollution flashover voltage of insulators, a pollution flashover warning should be made in advance. According to the operating environment of insulators along the Qinghai-Tibet railway, the pollution flashover experiments were designed for the cantilever composite insulator FQBG-25/12. Through the experiments, the flashover voltage under the influence of soluble contaminant density (SCD) of different pollution components, non-soluble deposit density (NSDD), temperature (T), and atmospheric pressure (P) was obtained. On this basis, the GA-BP neural network prediction model was established. P, SCD, NSDD, CaSO4 mass fraction (w(CaSO4)),… More >

  • Open Access

    ARTICLE

    Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset

    Wan Hyun Cho1, Sang Kyoon Kim2, Myung Hwan Na1, In Seop Na3,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4003-4024, 2021, DOI:10.32604/cmc.2021.018758

    Abstract Fruit processing devices, that automatically detect the freshness and ripening stages of fruits are very important in precision agriculture. Recently, based on deep learning, many attempts have been made in computer image processing, to monitor the ripening stage of fruits. However, it is time-consuming to acquire images of the various ripening stages to be used for training, and it is difficult to measure the ripening stages of fruits accurately with a small number of images. In this paper, we propose a prediction system that can automatically determine the ripening stage of fruit by a combination of deep neural networks (DNNs)… More >

  • Open Access

    ARTICLE

    A New Sequential Image Prediction Method Based on LSTM and DCGAN

    Wei Fang1, 2, Feihong Zhang1, *, Yewen Ding1, Jack Sheng3

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 217-231, 2020, DOI:10.32604/cmc.2020.06395

    Abstract Image recognition technology is an important field of artificial intelligence. Combined with the development of machine learning technology in recent years, it has great researches value and commercial value. As a matter of fact, a single recognition function can no longer meet people’s needs, and accurate image prediction is the trend that people pursue. This paper is based on Long Short-Term Memory (LSTM) and Deep Convolution Generative Adversarial Networks (DCGAN), studies and implements a prediction model by using radar image data. We adopt a stack cascading strategy in designing network connection which can control of parameter convergence better. This new… More >

  • Open Access

    ARTICLE

    Coupled Digital Image Correlation and Peridynamics for Full-Field Deformation Measurement and Local Damage Prediction

    Tianyi Li1, Xin Gu1, Qing Zhang1,*, Dong Lei1

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 425-444, 2019, DOI:10.32604/cmes.2019.06700

    Abstract Digital image correlation (DIC) measurement technique and peridynamics (PD) method have been applied in specific fields extensively owing to their respective advantages in obtaining full-field deformation and local failure of loaded materials and structures. This study provides a simple way to couple DIC measurements with PD simulations, which can circumvent the difficulties of DIC in dealing with discontinuous deformations. Taking the failure analysis of a compact tension specimen of aluminum alloy and a static three-point bending concrete beam as examples, the DIC experimental system firstly measures the full-field displacements, and then the PD simulation is applied on potential damage regions… More >

  • Open Access

    ARTICLE

    Sensitivity of Eigen Value to Damage and Its Identification

    B.K.Raghuprasad1, N.Lakshmanan2, N.Gopalakrishnan2, K.Muthumani2

    Structural Durability & Health Monitoring, Vol.4, No.3, pp. 117-144, 2008, DOI:10.3970/sdhm.2008.004.117

    Abstract The reduction in natural frequencies, however small, of a civil engineering structure, is the first and the easiest method of estimating its impending damage. As a first level screening for health-monitoring, information on the frequency reduction of a few fundamental modes can be used to estimate the positions and the magnitude of damage in a smeared fashion. The paper presents the Eigen value sensitivity equations, derived from first-order perturbation technique, for typical infra-structural systems like a simply supported bridge girder, modelled as a beam, an end-bearing pile, modelled as an axial rod and a simply supported plate as a continuum… More >

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