Chenghai Yu, Zhilong Lu^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3261-3280, 2024, DOI:10.32604/cmc.2024.056413 - 18 November 2024

Abstract Railway turnouts often develop defects such as chipping, cracks, and wear during use. If not detected and addressed promptly, these defects can pose significant risks to train operation safety and passenger security. Despite advances in defect detection technologies, research specifically targeting railway turnout defects remains limited. To address this gap, we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments. To enhance detection accuracy, we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU (YOLO-VSI). The model employs a state-space model (SSM) to enhance the C2f module in the YOLOv8… More >

Rongzhuang Song¹, Yinbo Zhu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.012598

Abstract Kink defects are prevalent in nanocellulose. The existence of diverse kink patterns lacking molecular-scale resolution has caused uncertainty regarding the mechanisms governing the formation of different kinks in nanocellulose, including both reversible and irreversible kinks. The constraints resulting from these limitations often lead to significant confusion in exploring the structure-property relationships of nanocellulose. By integrating AFM experiments with molecular dynamics simulations, we examined the microstructure-dependent kink deformations in nanocellulose (Iβ phase) and the resultant local microstructural damages. In atomic force microscopy images, bent nanofibrils typically display minor curvatures, whereas kinked nanofibrils exhibit pronounced sharp bends,… More >

Haowen Liang¹, Jiaming Bai^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012563

Abstract Defects are pivotal in influencing the mechanical performance of the hydroxyapatite (HAp) porous structure. In vat photopolymerization (VP) fabrication, directly peeling HAp/polymer green structure from the platform is an efficient approach but often introduces defects, compromising the mechanical performance of sintered HAp scaffolds. The peeling process is a physical phenomenon where the photocured HAp/polymer green structure exhibits resistance against applied peeling forces, which is influenced by its modulus and toughness. In this study, the peeling behavior of cubic-pore HAp (CP-HAp) green structures with varying levels of modulus and toughness was investigated in detail. The characterization… More >

Sameh M. Said^1,2,*, Yasin Essa¹

Congenital Heart Disease, Vol.19, No.4, pp. 407-417, 2024, DOI:10.32604/chd.2024.055636 - 31 October 2024

Abstract Minimally invasive approaches for cardiac surgery in children have been lagging in comparison to the adult world. A wide range of the most common congenital heart defects in infants and children can be repaired successfully through a variety of non-sternotomy incisions. This has been shown to be associated with superior cosmetic results, shorter hospital stays, and rapid return to full activity compared to sternotomy. These approaches have been around for decades, but they have not been widely adopted for a variety of reasons. Right axillary thoracotomy is one of these approaches that we believe should More >

Shuang Kang¹, Yinchao He^1,2, Wenwen Li^1,*, Sen Liu²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 933-949, 2024, DOI:10.32604/cmc.2024.056614 - 15 October 2024

Abstract To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades (WTB), this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm. First, mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image. The algorithm employs entropy as the objective function to guide the iteration process of image enhancement, selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations, effectively enhancing the detail features of defect… More >

Yanxue Ma¹, Xiaoling Liu^1,*, Bing Wang², Ying Liu¹

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 871-889, 2024, DOI:10.32604/sdhm.2024.052683 - 20 September 2024

Abstract In the bridge technical condition assessment standards, the evaluation of bridge conditions primarily relies on the defects identified through manual inspections, which are determined using the comprehensive hierarchical analysis method. However, the relationship between the defects and the technical condition of the bridges warrants further exploration. To address this situation, this paper proposes a machine learning-based intelligent diagnosis model for the technical condition of highway bridges. Firstly, collect the inspection records of highway bridges in a certain region of China, then standardize the severity of diverse defects in accordance with relevant specifications. Secondly, in order… More >

Yuanyuan Wang^1,2,*, Jialong Huang¹, Md Sharid Kayes Dipu¹, Hu Zhao³, Shangbing Gao^1,2, Haiyan Zhang^1,2, Pinrong Lv¹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4973-4995, 2024, DOI:10.32604/cmc.2024.055839 - 12 September 2024

Abstract Printed circuit boards (PCBs) provide stable connections between electronic components. However, defective printed circuit boards may cause the entire equipment system to malfunction, resulting in incalculable losses. Therefore, it is crucial to detect defective printed circuit boards during the generation process. Traditional detection methods have low accuracy in detecting subtle defects in complex background environments. In order to improve the detection accuracy of surface defects on industrial printed circuit boards, this paper proposes a residual large kernel network based on YOLOv5 (You Only Look Once version 5) for PCBs surface defect detection, called YOLO-RLC (You… More >

Jielin Jiang^1,2,3,4,*, Chao Cui¹, Xiaolong Xu^1,2,3,4, Yan Cui⁵

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 725-744, 2024, DOI:10.32604/iasc.2024.036897 - 06 September 2024

Abstract In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose… More >

Tajmal Hussain, Jungpyo Hong^*, Jongwon Seok^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2099-2119, 2024, DOI:10.32604/cmc.2024.050884 - 15 August 2024

Abstract Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things (IoT) and artificial intelligence (AI). Quality control is an important part of today’s smart manufacturing process, effectively reducing costs and enhancing operational efficiency. As technology in the industry becomes more advanced, identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process. In this study, we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques, incorporating a global… More >

Risalina Myrtha^1,2,*, Hasanah Mumpuni^3,4, Real Kusumanjaya Marsam^3,4, Dyah Wulan Anggrahini^3,4, Anggoro Budi Hartopo^3,4, Lucia Kris Dinarti^3,4

Congenital Heart Disease, Vol.19, No.3, pp. 315-324, 2024, DOI:10.32604/chd.2024.051587 - 26 July 2024

Abstract Background and Objective: The most feared complication of uncorrected secundum Atrial Septal Defect (ASD) is pulmonary arterial hypertension (PAH). Pulmonary vascular resistance (PVR) is crucial in detecting precapillary pulmonary hypertension (PH) to guide the need for PAH-specific therapy. There is a change in the cut-off value of PVR according to the recently updated PH guideline. How echocardiographic PVR (PVR) correlates to PVR by right heart catheterization (RHC) (PVR) according to the new guidelines has not been known. The aim of this study is to determine the reliability of PVR in detecting PAH in Uncorrected Ostium… More > Graphic Abstract