Tiantian Yu¹, Youping Lv¹, Hao Zhong², Ming Liu¹, Pingyuan Gai¹, Zeju Jiang¹, Peng Zhang¹, Xingkai Zhang^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.8, pp. 1759-1772, 2024, DOI:10.32604/fdmp.2024.049035 - 06 August 2024

Abstract A new measurement device, consisting of swirling blades and capsule-shaped throttling elements, is proposed in this study to eliminate typical measurement errors caused by complex flow patterns in gas-liquid flow. The swirling blades are used to transform the complex flow pattern into a forced annular flow. Drawing on the research of existing blockage flow meters and also exploiting the single-phase flow measurement theory, a formula is introduced to measure the phase-separated flow of gas and liquid. The formula requires the pressure ratio, Lockhart-Martinelli number (L-M number), and the gas phase Froude number. The unknown parameters More >

Zulqar Nain¹, B. Shahana², Shehzad Ashraf Chaudhry³, P. Viswanathan⁴, M.S. Mekala¹, Sung Won Kim^1,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5705-5721, 2023, DOI:10.32604/cmc.2023.033194 - 28 December 2022

Abstract Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and… More >

Jiaxiu Dong^1,2,3, Jianhua Liu⁴, Niannian Wang^1,2,3,*, Hongyuan Fang^1,2,3, Jinping Zhang¹, Haobang Hu^1,2,3, Duo Ma^1,2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 541-564, 2021, DOI:10.32604/cmes.2021.015875 - 22 July 2021

Abstract Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, cracks have been paid more attention, since cracks often cause major engineering and personnel safety incidents. Current manual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model based on the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The model proposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposal network (RPN), a… More >