Haomiao Wang¹, Jinxi Wang², Qingmei Sui^2,*, Faye Zhang², Yibin Li¹, Mingshun Jiang², Phanasindh Paitekul³

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 91-110, 2024, DOI:10.32604/sdhm.2023.041522

Abstract Due to their robust learning and expression ability for complex features, the deep learning (DL) model plays a vital role in bearing fault diagnosis. However, since there are fewer labeled samples in fault diagnosis, the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields, which limits the diagnostic performance. To solve this problem, a novel transfer residual Swin Transformer (RST) is proposed for rolling bearings in this paper. RST has 24 residual self-attention layers, which use the hierarchical design and the shifted window-based residual self-attention. Combined with transfer learning techniques, the… 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 >

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 >

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 >

Jialin Yu^1,*, Xiaodi Zhang², Qi Zhong¹, Jian Feng¹

Energy Engineering, Vol.121, No.3, pp. 789-806, 2024, DOI:10.32604/ee.2023.043299

Abstract With a further increase in energy flexibility for customers, short-term load forecasting is essential to provide benchmarks for economic dispatch and real-time alerts in power grids. The electrical load series exhibit periodic patterns and share high associations with metrological data. However, current studies have merely focused on point-wise models and failed to sufficiently investigate the periodic patterns of load series, which hinders the further improvement of short-term load forecasting accuracy. Therefore, this paper improved Autoformer to extract the periodic patterns of load series and learn a representative feature from deep decomposition and reconstruction. In addition, a novel multi-factor attention mechanism… More >

Yongwang Yuan¹, Xiangwei Liu^2,3,*, Ke Lu^1,3

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1227-1252, 2024, DOI:10.32604/cmc.2023.046937

Abstract Predictive Business Process Monitoring (PBPM) is a significant research area in Business Process Management (BPM) aimed at accurately forecasting future behavioral events. At present, deep learning methods are widely cited in PBPM research, but no method has been effective in fusing data information into the control flow for multi-perspective process prediction. Therefore, this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion. Firstly, the first layer BERT network learns the correlations between different category attribute data. Then, the attribute data is integrated into a weighted event-level feature vector and input into the second layer… More >

Chengjun Wang^1,2, Fan Ding^2,*, Yiwen Wang¹, Renyuan Wu¹, Xingyu Yao², Chengjie Jiang¹, Liuyi Ling¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1481-1501, 2024, DOI:10.32604/cmc.2023.046876

Abstract The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots. Real-time identification of strawberries in an unstructured environment is a challenging task. Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy. To this end, the present study proposes an Efficient YOLACT (E-YOLACT) algorithm for strawberry detection and segmentation based on the YOLACT framework. The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism, pyramid squeeze shuffle attention (PSSA), for efficient feature extraction. Additionally, an attention-guided context-feature pyramid network (AC-FPN) is… More >

Yanmei Li^1,*, Jinghong Tang¹, Weiwu Ding¹, Jian Luo², Naveed Ahmad³, Rajesh Kumar⁴

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2023.046872

Abstract Alzheimer’s disease (AD) is a complex, progressive neurodegenerative disorder. The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice. In this study, we introduce an advanced diagnostic methodology rooted in the Med-3D transfer model and enhanced with an attention mechanism. We aim to improve the precision of AD diagnosis and facilitate its early identification. Initially, we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation, which are commonly observed in imaging datasets. Subsequently, an attention mechanism is incorporated to selectively focus on the salient… More >