P. Samuel Pakianathan^*, R. V. Maheswari

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2717-2736, 2023, DOI:10.32604/iasc.2023.029950 - 17 August 2022

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… More >

Mesfer Al Duhayyim^1,*, 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 - 28 July 2022

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… More >

Gaozheng Zhao¹, Changchao Liu¹, Lingyu Sun¹, Ning Yang², Lei Zhang¹, Mingshun Jiang¹, Lei Jia¹, Qingmei Sui^1,*

Structural Durability & Health Monitoring, Vol.16, No.1, pp. 81-96, 2022, DOI:10.32604/sdhm.2022.016905 - 11 February 2022

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 More >

Sihua Wang^1,2, Junjun Wang^1,2,*, Lijun Zhou^1,2, Long Chen^1,2, Lei Zhao^1,2

Energy Engineering, Vol.119, No.1, pp. 219-236, 2022, DOI:10.32604/EE.2022.016899 - 22 November 2021

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,

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Wan Hyun Cho¹, Sang Kyoon Kim², Myung Hwan Na¹, In Seop Na^3,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4003-4024, 2021, DOI:10.32604/cmc.2021.018758 - 24 August 2021

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… More >

Wei Fang^{1, 2}, Feihong Zhang^{1, *}, Yewen Ding¹, Jack Sheng³

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 217-231, 2020, DOI:10.32604/cmc.2020.06395 - 20 May 2020

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 More >

Tianyi Li¹, Xin Gu¹, Qing Zhang^1,*, 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 More >

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… More >