Haitao Liu^1,2,*, Jiaming Wang¹, Xiuliang Zhang¹, Yanji Jiang², Qian Xiao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2747-2772, 2024, DOI:10.32604/cmes.2024.047129

Abstract The expansion chamber serves as the primary silencing structure within the exhaust pipeline. However, it can also act as a sound-emitting structure when subjected to airflow. This article presents a hybrid method for numerically simulating and analyzing the unsteady flow and aerodynamic noise in an expansion chamber under the influence of airflow. A fluid simulation model is established, utilizing the Large Eddy Simulation (LES) method to calculate the unsteady flow within the expansion chamber. The simulation results effectively capture the development and changes of the unsteady flow and vorticity inside the cavity, exhibiting a high level of consistency with experimental… More > Graphic Abstract

Shen Yan¹, Dajiang Geng^2,*, Ning Dai³, Mingjian Long², Zhicheng Bai²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2893-2915, 2024, DOI:10.32604/cmes.2024.046931

Abstract According to the design specifications, the construction of extended piles involves traversing the tunnel’s upper region and extending to the underlying rock layer. To address this challenge, a subterranean transfer structure spanning multiple subway tunnels was proposed. Deliberating on the function of piles in the transfer structure as springs with axial and bending stiffness, and taking into account the force balance and deformation coordination conditions of beams and plates within the transfer structure, we established a simplified mechanical model that incorporates soil stratification by combining it with the Winkler elastic foundation beam model. The resolved established simplified mechanical model employed… More >

Weiya Shi^1,*, Shaowen Zhang², Shiqiang Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3209-3231, 2024, DOI:10.32604/cmes.2023.044863

Abstract In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy. Moreover, the sensitivity of small… More >

Yan Li, Qiyuan Wang^*, Kaidi Jia

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2469-2489, 2024, DOI:10.32604/cmc.2024.047822

Abstract Image description task is the intersection of computer vision and natural language processing, and it has important prospects, including helping computers understand images and obtaining information for the visually impaired. This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images. Our method focuses on refining the reward function in deep reinforcement learning, facilitating the generation of precise descriptions by aligning visual and textual features more closely. Our approach comprises three key architectures. Firstly, it utilizes Residual Network 101 (ResNet-101) and Faster Region-based Convolutional Neural Network (Faster R-CNN) to extract average… More >

Xiao Feng^1,2,3,*, Zheng Yuan¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1881-1895, 2024, DOI:10.32604/cmc.2023.047243

Abstract First, we propose a cross-domain authentication architecture based on trust evaluation mechanism, including registration, certificate issuance, and cross-domain authentication processes. A direct trust evaluation mechanism based on the time decay factor is proposed, taking into account the influence of historical interaction records. We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data. We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record. Then we propose an autoencoder-based trust clustering algorithm. We perform feature… More >

Liqiu Ren, Zhanying Li^*, Xueyu He, Lingyan Kong, Yinghao Zhang

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2829-2845, 2024, DOI:10.32604/cmc.2024.047028

Abstract For underwater robots in the process of performing target detection tasks, the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model, which is prone to issues like error detection, omission detection, and poor accuracy. Therefore, this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7) underwater target detection algorithm. To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase, we have added a Convolutional Block Attention Module (CBAM) to the backbone network. The Reparameterization Visual Geometry Group (RepVGG) module is inserted into the… More >

Haipeng Sun^1,2, Xueyan Ding^1,2,*, Jian Sun^1,2, Hua Yu³, Jianxin Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2721-2739, 2024, DOI:10.32604/cmc.2024.046449

Abstract Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition, we present a deep learning-based approach for Yi character detection and recognition. In the detection stage, an improved Differentiable Binarization Network (DBNet) framework is introduced to detect Yi characters, in which the Omni-dimensional Dynamic Convolution (ODConv) is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features, thereby improving the accuracy of Yi character detection. Then, the feature pyramid network fusion module is used to further extract Yi character image features, improving target recognition… More >

Yunhong Zhang^1,2,*, Yonghui Yang^1,2, Jiawei Mao^1,2

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 185-212, 2024, DOI:10.32604/phyton.2023.046811

Abstract Alginate oligosaccharides (AOS) enhance drought resistance in wheat (Triticum aestivum L.), but the definite mechanisms remain largely unknown. The physiological and transcriptome responses of wheat seedlings treated with AOS were analyzed under drought stress simulated with polyethylene glycol-6000. The results showed that AOS promoted the growth of wheat seedlings and reduced oxidative damage by improving peroxidase and superoxide dismutase activities under drought stress. A total of 10,064 and 15,208 differentially expressed unigenes (DEGs) obtained from the AOS treatment and control samples at 24 and 72 h after dehydration, respectively, were mainly enriched in the biosynthesis of secondary metabolites (phenylpropanoid biosynthesis,… More >

Jingtao Hu¹, Wenjing Chen¹, Yanyan Duan¹, Yingjing Ru¹, Wenqing Cao¹, Pingwei Xiang², Chengzhi Huang², Li Zhang², Jingsheng Chen¹, Liping Gan^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 227-245, 2024, DOI:10.32604/phyton.2024.046521

Abstract Abiotic stress, including flooding, seriously affects the normal growth and development of plants. Mulberry (Morus alba), a species known for its flood resistance, is cultivated worldwide for economic purposes. The transcriptomic analysis has identified numerous differentially expressed genes (DEGs) involved in submergence tolerance in mulberry plants. However, a comprehensive analyses of metabolite types and changes under flooding stress in mulberry remain unreported. A non-targeted metabolomic analysis utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS) was conducted to further investigate the effects of flooding stress on mulberry. A total of 1,169 metabolites were identified, with 331 differentially accumulated metabolites (DAMs) exhibiting up-regulation in… More >

Dongmei Chen¹, Peipei Cao¹, Lijie Yan¹, Huidong Chen¹, Jia Lin¹, Xin Li², Lin Yuan³, Kaihua Wu^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 261-275, 2024, DOI:10.32604/phyton.2024.046331

Abstract Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea. Traditional tea-picking machines may compromise the quality of the tea leaves. High-quality teas are often handpicked and need more delicate operations in intelligent picking machines. Compared with traditional image processing techniques, deep learning models have stronger feature extraction capabilities, and better generalization and are more suitable for practical tea shoot harvesting. However, current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks. We propose a tea shoot instance segmentation model based on multi-scale mixed attention… More >