Xin Wen^*, Xiao Zheng, Yu He

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4371-4388, 2025, DOI:10.32604/cmc.2025.060661 - 06 March 2025

Abstract Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation. However, existing detection methods often struggle with challenges such as complex defect morphology, texture similarity, and fuzzy edges, leading to poor accuracy and missed detections. In order to resolve these problems, we propose MSCM-Net (Multi-Scale Cross-Modal Network), a multiscale cross-modal framework focused on detecting rail surface defects. MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps, effectively capturing and enhancing features at different scales for each modality. To… More >

Yi-Feng Luo^1,*, Jyuan-Fong Yen², Wen-Cheng Su³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3069-3087, 2025, DOI:10.32604/cmes.2025.061180 - 03 March 2025

Abstract This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules. Improper use of batteries can lead to electrolyte decomposition, resulting in the formation of lithium dendrites. These dendrites may pierce the separator, leading to the failure of the insulation layer between electrodes and causing micro short circuits. When a micro short circuit occurs, the electrolyte typically undergoes exothermic reactions, leading to thermal runaway and posing a safety risk to users. Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention. To address this issue, the article More >

Conghao Niu^1,*, Dezhi Han¹, Bing Han², Zhongdai Wu²

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1723-1748, 2024, DOI:10.32604/csse.2024.056736 - 22 November 2024

Abstract The high coverage and all-weather capabilities of Synthetic Aperture Radar (SAR) image ship detection make it a widely accepted method for maritime ship positioning and identification. However, SAR ship detection faces challenges such as indistinct ship contours, low resolution, multi-scale features, noise, and complex background interference. This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images, incorporating key structures to enhance performance. The YOLOv8 backbone is replaced by the Slim Backbone (SB), and the Delete Medium-sized Detection Head (DMDH) structure is eliminated to concentrate on shallow features. Dynamically adjusting the… More >

Chunming Wu¹, Shupeng Zheng^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, DOI:10.32604/cmc.2024.049665 - 20 June 2024

Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >

Xiangyan Tang^1,2, Chengchun Ruan^1,2,*, Xiulai Li^2,3, Binbin Li^1,2, Cebin Fu^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 983-1003, 2024, DOI:10.32604/cmc.2024.047541 - 25 April 2024

Abstract Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to… More >

Yi Zhang^*, Jiamin Guo, Huanghua Peng

Energy Engineering, Vol.120, No.11, pp. 2489-2502, 2023, DOI:10.32604/ee.2023.029496 - 31 October 2023

Abstract The windy environment is the main cause affecting the efficiency of offshore wind turbine installation. In order to improve the stability and efficiency of single-blade installation of offshore wind turbines under high wind speed conditions, the Stewart platform is used as an auxiliary tool to help dock the wind turbine blade in this paper. In order to verify the effectiveness of the Stewart platform for blade docking, a blade docking simulation system consisting of the Stewart platform, wind turbine blade, and wind load calculation module was built based on Simulink/Simscape Multibody. At the same time, More >

WENQI CHEN^1,#, XIAOYANG CHU^2,#, YANG ZENG^3,#, YOUSHENG YAN⁴, YIPENG WANG⁴, DONGLAN SUN¹, DONGLIANG ZHANG⁵, JING ZHANG^1,*, KAI YANG^4,*

BIOCELL, Vol.47, No.7, pp. 1561-1569, 2023, DOI:10.32604/biocell.2023.028851 - 21 June 2023

Abstract Background: Receptor tyrosine kinase-like orphan receptor 2 (ROR2) has a vital role in osteogenesis. However, the mechanism underlying the regulation of ROR2 in osteogenic differentiation is still poorly comprehended. A previous study by our research group showed that a novel compound heterozygous ROR2 variation accounted for the autosomal recessive Robinow syndrome (ARRS). This study attempted to explore the impact of the ROR2: c.904C>T variant specifically on the osteogenic differentiation of BMSCs. Methods: Coimmunoprecipitation (CoIP)-western blotting was carried out to identify the interaction between ROR2 and Wnt5a. Double-immunofluorescence staining was used for determining the expressions and co-localization… More > Graphic Abstract

Shang Zhang¹, Jie Wang², Benjamin Rose⁵, Yushan Yang³, Qingfeng Ding¹, Bengang Zhang^4,*, Chunlei Dong^2,*

Journal of Renewable Materials, Vol.11, No.6, pp. 2881-2891, 2023, DOI:10.32604/jrm.2023.028160 - 27 April 2023

Abstract To study the static bending creep properties of glass fiber reinforced wood, glass fiber reinforced poplar (GFRP) specimens were obtained by pasting glass fiber on the upper and lower surfaces of Poplar (Populus euramevicana, P), the performance of Normal Creep (NC) and Mechanical Sorptive Creep (MSC) of GFRP and their influencing factors were tested and analyzed. The test results and analysis show that: (1) The MOE and MOR of Poplar were increased by 17.06% and 10.00% respectively by the glass fiber surface reinforced composite. (2) The surface reinforced P with glass fiber cloth only exhibits the… More >

XIN TIAN, XIANGLING HE^*, SHUQIN QIAN, RUNYING ZOU, KEKE CHEN, CHENGGUANG ZHU, ZEXI YIN

BIOCELL, Vol.47, No.5, pp. 1085-1093, 2023, DOI:10.32604/biocell.2023.027090 - 10 April 2023

Abstract Background: The immunomodulatory effects of mesenchymal stem cells (MSCs) and their exosomes have been receiving increasing attention. This study investigated the immunoregulatory effects of human amniotic mesenchymal stem cells (hAMSCs) and their exosomes on phytohemagglutinin (PHA)-induced peripheral blood mononuclear cells (PBMCs). Methods: The hAMSCs used in the experiment were identified by light microscopy and flow cytometry, and the differentiation ability of the cells was determined by Oil Red O and Alizarin Red staining. The expressions of transforming growth factor (TGF)-β, indoleamine 2,3-dioxygenase (IDO), cyclooxygenase-2 (COX-2), hepatocyte growth factor (HGF), and interleukin (IL)-6 were detected by quantitative… More >

Zhao Yang¹, Yuan Zhang¹, Lei Zhu^2,*, Lei Huang¹, Fangyu Hu³, Yanping Du¹, Xiaowei Li¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2889-2904, 2023, DOI:10.32604/cmc.2023.037297 - 31 March 2023

Abstract High-frequency (HF) and ultrahigh-frequency (UHF) dual-band radio frequency identification (RFID) tags with both near-field and far-field communication can meet different application scenarios. However, it is time-consuming to calculate the return loss of a UHF antenna in a dual-band tag antenna using electromagnetic (EM) simulators. To overcome this, the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory (MSCNN-LSTM) for predicting the return loss of UHF antennas instead of EM simulators. In the proposed MSCNN-LSTM, the MSCNN has three branches, which include three convolution layers with different kernel… More >