Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (6)
  • Open Access

    ARTICLE

    An Experimental Analysis of Gas-Liquid Flow Breakdown in a T-Junction

    Lihui Ma1,*, Zhuo Han1, Wei Li1, Guangfeng Qi1, Ran Cheng2, Yuanyuan Wang1, Xiangran Mi3, Xiaohan Zhang1, Yunfei Li1

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1381-1392, 2024, DOI:10.32604/fdmp.2024.046405 - 27 June 2024

    Abstract When a gas-liquid two-phase flow (GLTPF) enters a parallel separator through a T-junction, it generally splits unevenly. This phenomenon can seriously affect the operation efficiency and safety of the equipment located downstream. In order to investigate these aspects and, more specifically, the so-called bias phenomenon (all gas and liquid flowing to one pipe, while the other pipe is a liquid column that fluctuates up and down), laboratory experiments were carried out by using a T-junction connected to two parallel vertical pipes. Moreover, a GLTPF prediction model based on the principle of minimum potential energy was… More >

  • Open Access

    ARTICLE

    Crop Disease Recognition Based on Improved Model-Agnostic Meta-Learning

    Xiuli Si1, Biao Hong1, Yuanhui Hu1, Lidong Chu2,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.036829 - 29 April 2023

    Abstract Currently, one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development. Therefore, further research in the field of crop disease and pest detection is necessary to address the mentioned problem. Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses, this article proposes a Model-Agnostic Meta-Learning (MAML) attention model based on the meta-learning paradigm. The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention (ECA)… More >

  • Open Access

    ARTICLE

    Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

    K. Jagadeesh1,*, A. Rajendran2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2017-2032, 2023, DOI:10.32604/csse.2023.029169 - 03 November 2022

    Abstract Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients. For lung cancer diagnosis, the computed tomography (CT) scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis. In present scenario of medical data processing, the cancer detection process is very time consuming and exactitude. For that, this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm. In the model, the input CT images are More >

  • Open Access

    ARTICLE

    An Improved Model to Characterize Drill-String Vibrations in Rotary Drilling Applications

    Yong Wang, Hongjian Ni*, Ruihe Wang, Shubin Liu

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1263-1273, 2022, DOI:10.32604/fdmp.2022.020405 - 27 May 2022

    Abstract A specific model is elaborated for stick-slip and bit-bounce vibrations, which are dangerous dynamic phenomena typically encountered in the context of rotary drilling applications. Such a model takes into account two coupled degrees of freedom of drill-string vibrations. Moreover, it assumes a state-dependent time delay and a viscous damping for both the axial and torsional vibrations and relies on a sawtooth function to account for the cutting force fluctuation. In the frame of this theoretical approach, the influence of rock brittleness on the stability of the drill string is calculated via direct integration of the More >

  • Open Access

    ARTICLE

    Improved Model of Eye Disease Recognition Based on VGG Model

    Ye Mu1,2,3,4, Yuheng Sun1, Tianli Hu1,2,3,4, He Gong1,2,3,4, Shijun Li1,2,3,4,*, Thobela Louis Tyasi5

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 729-737, 2021, DOI:10.32604/iasc.2021.016569 - 20 April 2021

    Abstract The rapid development of computer vision technology and digital images has increased the potential for using image recognition for eye disease diagnosis. Many early screening and diagnosis methods for ocular diseases based on retinal images of the fundus have been proposed recently, but their accuracy is low. Therefore, it is important to develop and evaluate an improved VGG model for the recognition and classification of retinal fundus images. In response to these challenges, to solve the problem of accuracy and reliability of clinical algorithms in medical imaging this paper proposes an improved model for early More >

  • Open Access

    ARTICLE

    Reentry Attitude Tracking Control for Hypersonic Vehicle with Reaction Control Systems via Improved Model Predictive Control Approach

    Kai Liu1, 2, Zheng Hou2, *, Zhiyong She2, Jian Guo2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 131-148, 2020, DOI:10.32604/cmes.2020.08124 - 01 January 2020

    Abstract This paper studies the reentry attitude tracking control problem for hypersonic vehicles (HSV) equipped with reaction control systems (RCS) and aerodynamic surfaces. The attitude dynamical model of the hypersonic vehicles is established, and the simplified longitudinal and lateral dynamic models are obtained, respectively. Then, the compound control allocation strategy is provided and the model predictive controller is designed for the pitch channel. Furthermore, considering the complicated jet interaction effect of HSV during RCS is working, an improved model predictive control approach is presented by introducing the online parameter estimation of the jet interaction coefficient for More >

Displaying 1-10 on page 1 of 6. Per Page