Zifeng Yu¹, Xianfeng Li^1,*, Lianpeng Sun², Jinjun Zhu², Jianxin Lin³

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 435-451, 2024, DOI:10.32604/cmc.2023.046685 - 30 January 2024

Abstract Urban sewer pipes are a vital infrastructure in modern cities, and their defects must be detected in time to prevent potential malfunctioning. In recent years, to relieve the manual efforts by human experts, models based on deep learning have been introduced to automatically identify potential defects. However, these models are insufficient in terms of dataset complexity, model versatility and performance. Our work addresses these issues with a multi-stage defect detection architecture using a composite backbone Swin Transformer. The model based on this architecture is trained using a more comprehensive dataset containing more classes of defects.… More >

Soonshin Seo^1,2, Ji-Hwan Kim^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2833-2856, 2023, DOI:10.32604/cmc.2023.042816 - 26 December 2023

Abstract Automatic speech recognition (ASR) systems have emerged as indispensable tools across a wide spectrum of applications, ranging from transcription services to voice-activated assistants. To enhance the performance of these systems, it is important to deploy efficient models capable of adapting to diverse deployment conditions. In recent years, on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios. However, these methods often confront substantial trade-offs, particularly in terms of unstable accuracy when reducing the model size. To address challenges, this study introduces two crucial empirical findings. Firstly,… More >

Ying Bai^1,*, Xiaoti Ren², Hong Li¹

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2999-3018, 2023, DOI:10.32604/iasc.2023.040124 - 11 September 2023

Abstract The combination of the Industrial Internet of Things (IIoT) and digital twin (DT) technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance. However, conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information. The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model. In this paper, a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state. Firstly, a global real-time feedback and the dynamic adjustment mechanism… More >

Abdelaziz A. Abdelhamid^1,3,*, Sultan R. Alotaibi²

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 917-933, 2022, DOI:10.32604/cmc.2022.027653 - 18 May 2022

Abstract Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools. In this paper, we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model. The proposed ensemble model is composed of two levels of regression models. The first level consists of three strong models namely, random forest, support vector regression, and light gradient boosting machine. Whereas the second level is based on the ElasticNet regression model, which… More >