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Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Domain adaptation, the important branch of transfer learning, aims to solve the problem that the performance of traditional machine learning algorithms drops sharply when the training and test samples obey different data distributions. Transformer is a deep learning framework based on a self-attention mechanism, which has strong global feature extraction ability and modeling ability. In recent years, the combination of Transformer and domain adaptation has also become a research hotspot. Although many relative methods have been published, the review of Transformer application in domain adaptation has not been reported. In order to fill the gap in this field and provide reference for relevant research, this paper summarizes and analyzes some typical domain adaptation methods based on Transformer in recent years. This paper summarizes the concepts related to domain adaptation and the basic structure of the Transformer, sorts out various domain adaptation methods based on Transformer from four applications, i.e. image classification, image semantic segmentation, object detection and medical image analysis and compares the domain adaptation methods in image classification. Finally, the challenges of the current domain adaptation Transformer model are summarized, and the feasible research directions in the future are discussed. [ABSTRACT FROM AUTHOR]

A gear box fault diagnosis method based on improved time series gray image and deep learning is put forward in order to solve the problems such as complex actual working environment of gear box, inadequate performance of extracting features by traditional methods and gray image extraction features. After the vibration signal is decomposed into several intrinsic mode components (IMFs) by EEMD, the IMFs are divided into high-frequency and low-frequency components by cumulative mean criterion, in which the high-frequency components are denoised by wavelet threshold. The high frequency IMFs and low frequency IMFs after noise reduction are reconstructed, and the reconstructed signal is coded by gray image method. Two-dimensional improved time series gray image is sent to convolution neural network for training, so as to exert the advantages of convolution network in feature extraction of pictures and display the results by confusion matrix. Finally, the model results and different gray scale images are compared with traditional diagnosis methods. The results show that compared with common gray image and global denoising gray image, the method proposed in this paper improves the accuracy of gear box fault diagnosis by 4 and 1.8 percentage points respectively, and the convergence speed is significantly faster. Compared with BP neural network and ELM diagnosis method, the method proposed in this paper significantly improves the accuracy of gear box fault diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The input and feature combination of the current commonsense reasoning model based on multi- knowledge base fusion is too simple, resulting in the loss of some important information related to questions and answers, which limits the effect of the commonsense reasoning model integrating external knowledge. In addition, during the commonsense question and answer task, the problem of vector anisotropy in the output of the pre- training language model and the answer representation has not been solved. These problems are the factors that lead to the poor reasoning performance of commonsense question answering. To solve the above problems, this paper proposes a multi-knowledge base common-sense question answering model based on local feature fusion, which improves the fusion of external knowledge bases and question-answer texts. The model integrates the local question and answer features into the global features of the pre-trained language model to enrich the feature information of the model, and combines the features of multiple dimensions in the prediction layer for prediction. The model for the questions and answers to be matched. Sentence representations are whitened and then the matching task is performed. Through the whitening operation, the model enhances the isotropy of the sentence representation and improves the representation ability of the sentence vector. This paper also explores the effect of different pre-trained encoders (such as, ALBERT, ELECTRA) on the model to strengthen knowledge. The feature extraction ability of text is strengtened, and the stability of the model is proved. The experimental results show that under the same BERT-base encoder experiment, the accuracy of the model reaches 78.6%, which is 3.5 percentage points higher than the baseline model. In the experiment of ELECTRA-base encoder, the accuracy reaches 80.1%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Remote sensing scene classification is a hot research topic in the field of computer vision, and it is of great significance to semantic understanding of remote sensing images. At present, remote sensing scene classification methods based on deep learning occupy a dominant position in this field. However, it suffers from the lack of samples and poor model generalization ability in actual application scenarios. Therefore, this paper proposes a few-shot remote scene classification method based on attention mechanism, and designs a structure of dual- branches similarity measurement. This method is based on the meta-learning training strategy to divide the dataset into tasks. At the meantime, the input images are divided into blocks in order to preserve the feature distribution in the remote sensing image. Then the lightweight attention module is introduced into the feature extraction network to reduce the risk of overfitting and ensure the acquisition of discriminative features. Finally, based on earth mover's distance (EMD), a dual-branches similarity measurement module is added to improve the discriminative ability of the classifier. The results show that compared with the classic smallsample learning method, the few-shot remote scene classification method proposed in this paper can significantly improve the classification performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure. of feature maps, one-dimensional CNN（1D-CNN） and two-dimensional CNN（2D-CNN） were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multidimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility. Results: The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results. Conclusions: The multidimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention [ABSTRACT FROM AUTHOR]

To satisfy the requirement of low power consumption vehicle computing platform for lane detection, this paper proposes a low computing power dependent real-time lane recognition method. Considering the variation of illumination during vehicle driving, a color separation method based on adaptive illumination to extract lane characteristics is proposed. The effective edge point form is defined and the lane lines are determined by edge point voting based on the classical edge detection and Hough transform algorithm. The lane lines are used to filter and supplement the edge points and the lane curve equation is obtained by using the random sample consensus algorithm. The results show that the proposed method achieves a recognition accuracy of over 98% and computation speed of 38 frames per second on a low power processor. Furthermore, the method has proven to be stable and robust in a variety of scenarios. [ABSTRACT FROM AUTHOR]

To eliminate the defects of incomplete detection and high false detection rate caused by insignificant pedestrian target features, dense small targets and complex background in infrared images, this paper proposes an infrared pedestrian target detection algorithm based on improved YOLOv7. Firstly, the original Spatial Pyramid Pooling (SPP) module is replaced by the Channel Attention based Spatial Pyramid Pooling (CASPP) module based on the YOLOv7-tiny model, so that the model could pay more attention to the extraction of pedestrian features; then, the convolution module CBM based on the Meta-ACON activation function is introduced, which further suppressed the background noise and preserved the details of the pedestrians; finally, an alpha fusion data enhancement method is proposed to enrich the diversity of samples and improve the stability of the model in complex environments. The validation based on the FLIR dataset shows that the proposed method improves the accuracy by 3% and reduces the computation by 38% compared with the YOLOv7-tiny algorithm, which is more suitable for infrared pedestrian target detection scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A way to quickly and accurately extract building information from visible light images acquired by drones is a hot issue in remote sensing image processing research. In response to the above problems, this paper combined feature extraction, random forest regression, and improved morphological alternating filtering algorithms on the basis of in-depth research on the morphological building index and enhanced morphological building index. A targeted improvement was made to the building extraction method. The main improvements in this paper are as follows. (1) A method for building enhancement of UAV visible light images based on random forest regression is proposed. By fusing the original visible light band, visible light remote sensing index features, texture features, etc., the feature space is constructed, and no artificial feature selection is required while enhancing the building information in the case of the situation. (2) Aiming at the problem of a lot of noise in the homogeneous area inside the enhanced image of the building, the image is morphologically filtered through EASFs. Finally, in order to verify the effectiveness of the method in the paper, the representative city center building area and the city surrounding building area are selected for extraction experiments. The main building types of the two areas are quite different. In the above two areas, the F1 value of the proposed method is increased by 10.40% and 10.08% compared with the MBI algorithm, and the F1 of the first proposed an improved enhanced morphological building index value is increased by 5.23% and 7.87% compared with the EMBI algorithm. The results show that this method can better extract the building information from the UAV's visible light image. [ABSTRACT FROM AUTHOR]

In order to improve the spatial resolution of brain magnetic resonance imaging (MRI) images, the super resolution (SR) reconstruction method based on deep convolutional neural network (CNN) has achieved remarkable results. Increasing network depth or width usually can expand the receptive field of the network to improve the quality of reconstruction, but it is difficult to train the network due to the large parameter quantity and huge computing requirements. Meanwhile, the network does not pay enough attention to the medium and high frequency features of the image, which affects the quality of reconstruction. To solve the above problems, this paper proposes a cascaded channels-space attention and encode-decode network for 3D-MRI image SR reconstruction. Firstly, feature extraction is carried out in the low resolution space, and the image features are extracted by the symmetric connected encoder-decoder with dilated convolution to alleviate the checkerboard artifacts. Secondly, a serial connected channel-space attention module is constructed to capture the interdependence between the feature channels by channel attention, and the spatial attention is increased to assign weight to information of different positions, effectively enhancing the network' s learning of medium and high frequency information. Finally, subpixel convolution is used to sample the feature map to the target image size and meanwhile reduce memory consumption. Experimental results on public Kirby21 and BraTS datasets show that the proposed method is superior in both quantitative peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and subjective visual quality when compared with traditional SR algorithms and mainstream CNN-based SR algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper designed an intelligent extraction algorithm for inscription-brocades pattern elements was designed. First of all, the bilateral filtering algorithm was used to smooth the fabric texture. Then, the image was processed by ACE to enhance the contrast definition of the image. Next, the Grab-Cut algorithm was designed. The amount of super-pixel segmentation quantity was determined, and the pattern elements were extracted pertinently. Finally, the contour of inscription-brocades pattern elements was extracted by Canny operator, and CDR software was used to vectorize it. The experimental results of intelligent extraction of "Chang Le Ming Guang" brocade show that the pixel segmentation accuracy PA of six image factors is greater than 95%. It shows that the combined algorithm in this paper can accurately and completely extract inscription-brocades pattern elements in Han Dynasty. This study can provide reference for the reuse of inscriptions in China. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of the research on saliency target detection is to pursue performance, while ignoring efficiency, resulting in poor practicability. To this end, this paper proposes an efficient and lightweight network model. Firstly, a feature extraction sub-network (LFRM) is constructed using the idea of feature reuse to fully extract and aggregate the deep feature information of the lightweight feature extraction network, and generate the initial rough saliency prediction map that is used for positioning target guidance of subsequent low- level features. Secondly, according to the differences between feature layers at each stage, a cross-layer interactive aggregation module (CIAM) is constructed to effectively aggregate spatial information and semantic information and reduce redundant information. Finally, an edge refinement module (ERM) is constructed to fully obtain and utilize edge contour information, while adopting a progressive self-guided loss to enhance the dependence of edge information on each other. The final network has only 3.48 x 106 of parameters, and for a 352 x 352 image, it can reach a running speed of 108 FPS on a single GTX1080Ti graphics card. The test results on five benchmark public datasets show that the model proposed in this paper has comparable or even better performance than the current state-of-the-art SOD methods, with smaller parameters and faster speed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Gaze estimation is a technique for predicting the gaze position or gaze direction of the human eye and plays an important role in human-computer interaction and computer vision applications. The recent development of deep learning has revolutionized many computer vision tasks, and using deep learning for appearance-based gaze estimation has also become a hot topic. Focusing on the training process of the deep learning model, this paper analyzes state-of-the-art gaze estimation methods from four perspectives: gaze data preprocessing, gaze feature extraction, gaze learning strategies, and deep gaze model structures. In addition, the mainstream public datasets are summarized, and the performance evaluation and analysis of 2D and 3D gaze estimation methods are carried out on several popular datasets. Finally, the challenges faced by the existing gaze estimation methods are discussed, and the future development directions are prospected. [ABSTRACT FROM AUTHOR]

In recent years, the classification and segmentation research of point cloud mostly adopts the method of extracting the features of point cloud with multi-level architecture, and obtains relatively stable high-level semantic features. However, the extraction of global features and neighborhood features are insufficient, and the feature fusion of context information is lacking. Therefore, a new LAM-EdgeCNN network is proposed in this paper, which adopts the cascade of edge convolution and attention mechanism to extract multi-level feature information from point clouds and obtain high-level feature information. Firstly, in order to enhance the capture of specific channel features and key spatial points, a lightweight LAM attention mechanism is proposed, which uses CAM feature channel attention to acquire the correlation of each channel and locate the capture of key channel features, so that the network pays more attention to specific channel features to reduce the information dispersion and feature redundancy. Secondly, SAM spatial attention mechanism is introduced to obtain the attention weight of the location information of the point space and increase the granularity of the shallow information. Finally, a combination of attention mechanism and edge convolution EdgeConv is used to enhance context awareness, fully extract and fuse the local features and context features of point cloud, and obtain the downstream task-oriented point cloud features. The model is applied to the public data set, and the experiment shows that the model has good effect and robustness in the tasks of point cloud classification, component segmentation and semantic segmentation. [ABSTRACT FROM AUTHOR]

The point cloud of the real scene not only has the spatial geometric information of the point cloud, but also has the color information of the 3D object. The existing network cannot effectively use the local features and spatial geometric feature information of the real scene. Therefore, a dual-channel feature fusion of real scene for point cloud semantic segmentation DCFNet can be used for indoor and outdoor scene semantic segmentation in different scenarios. More specifically, in order to solve the problem that the color information of the point cloud in the real scene cannot be fully extracted, the method uses two input channels, and the channel adopts the same feature extraction network structure. The input of the upper channel is the complete RGB color and point cloud coordinate information, and the channel mainly focuses on the scene features of complex objects, while the lower channel only inputs the point cloud coordinate information. This channel mainly focuses on the spatial geometric characteristics of the point cloud. In each channel, in order to better extract local and global information and improve network performance, the inter-layer fusion module and the Transformer channel feature expansion module are introduced. At the same time, the existing 3D point cloud semantic segmentation methods lack of attention to the relationship between local features and global features, which leads to poor segmentation results for complex scenes. In this paper, the features extracted from the upper and lower channels are fused by the DCFFS (dual-channel feature fusion segmentation) module, and the semantic segmentation of the real scene is performed. The experimental results show that the mean intersection over union (MIOU) of the proposed DCFNet segmentation method on the S3DIS Area5 indoor scene dataset and the STPLS3D outdoor scene dataset reaches 71.18% and 48.87% respectively. The mean average precision (MACC) and overall accuracy (OACC) reach 77.01% and 86.91% respectively, achieving high-precision point cloud semantic segmentation in real scenes. [ABSTRACT FROM AUTHOR]

The intelligent classroom is a popular application scenario in the field of artificial intelligence. This paper proposes a face detection algorithm based on improved YOLOv5, named YOLOv5-SASA, to address the issues of missed or false detection caused by small or occluded faces in images captured by cameras located far away or at an angle. The algorithm consists of three parts. Firstly, the CSPDarknet53 network is utilized in the backbone layer, and the BasicRFB module is used in the final spatial pooling layer to enhance the network's feature extraction ability. Secondly, the NWD loss function is employed to improve the model's robustness in detecting small targets. Thirdly, the independent self- attention mechanism module SASA is introduced in the head layer to address the issue of face occlusion and reduce the model's parameter count. Finally, the improved YOLOv5 network is optimized by reducing the number of neurons in the middle layer channels and adjusting the learning rate to avoid overfitting. Experimental results demonstrate that the proposed method outperforms the original network in the easy, medium, and hard levels of the WiderFace validation set, achieving accuracies of 97.5%, 96.3%, and 86.5%, respectively, which effectively improves the accuracy of face detection in classroom scenarios. [ABSTRACT FROM AUTHOR]

In order to counter the limitations of the original YOLOv5 human fall detection task, a highly accurate fall detection algorithm, called C2D-YOLO, is proposed in this paper. The original task struggles to effectively handle complex detail capture, deformation handling, target adaptation to different scales, and occlusion detection. To overcome these challenges, several improvements are made to the YOLOv5 model. Firstly, a new feature extraction module called C2D is introduced, which improves feature characterisation, captures complex details, and handles deformations by combining deformable convolution, standard convolution, and channel-space hybrid attention mechanisms. Secondly, in the neck network, Swin Transformer block replaces the bottleneck layer of the C3 module to retain more feature information, thereby improving target detection accuracy at different scales and enhancing performance under occlusion. Finally, the head module of YOLOv5 is enhanced based on the decoupled structure of YOLOX borrowed from YOLOv5 to optimise classification and regression performance. Experimental results show that this method achieves a 3.2 percentage points improvement in mAP0.5 and a 6.5 percentage points improvement in mAP0.5: 0.95 compared to existing YOLOv5s. These improvements significantly increase detection accuracy and reduce false alarm rates. [ABSTRACT FROM AUTHOR]

Current digital office documents involve huge amount of table data. Accordingly, increasing demand on intelligent table recognition emerges. Nonetheless, the table structure is complex and closely linked, leading to the ultrahigh difficulty in table structure detection. To address this problem, a new table row-column cell structure detection method was proposed based on YOLOv8, in which the ICDAR19-cTDaR table cell structure and the TabStructDB table row-column structure were taken as object. Firstly, in order to enhance the extraction of table cells and row and column features, this paper introduced deformable convolution network (DCN). Secondly, the introduction of spatial and channel reconstruction convolution (SCConv) not only had a strong feature extraction capability, but also reduced redundant features to reduce the complexity and computational cost. Based on the above introduced convolution, a new module DSC module was designed to replace the Bottlenck module in C2f and named as C2fDSC module. Additionally, in order to further enhance the corner local feature extraction of the table structure, a explicit visual center feature adjustment (EVC) module was added to the backbone network of YOLOv8. Finally, the loss function of the original model was replaced by MPDIoU. When the problem of dense objective regression accuracy is being solved, the MPDIoU loss function bounding box regression is more accurate and efficient compared to the original model loss function. Experimental results show that the table structure detection algorithm in the dataset ICDAR19-cTDaR achieves the best detection results so far. The cell checking rate, checking rate and F1 value are 91.7%, 82.3% and 86.7%, respectively. Moreover, the proposed algorithm also performed well in the data-set TabStructDB table row and column detection. [ABSTRACT FROM AUTHOR]

To solve the large proportion of small targets and complex background in UAV (unmanned aerial vehicle) aerial image, the current object detection model has the problems of low accuracy and missed detection of small targets. Based on the YOLOv8s model, this paper proposes a 3D multi-layer feature collaboration UAV remote sensing object detection algorithm. Firstly, based on the coordinate attention, this paper proposes 3D multi-branch coordinate attention (MBCA), which improves the global feature extraction ability of the model and reduces the computation of spatial dimension by increasing the information interaction of channel dimension and the splitting and fusion of extended branches. Secondly, SPD-Conv is used to replace part of the standard convolution, which effectively retains more feature information and speeds up inference during downsampling. Then, a more efficient FastDBB_Bottleneck module is used in the C2f module, combining PConv and DBB structure reparameterization superposition to further reduce the calculation of the model. Finally, PG-Detect detection head is introduced to significantly reduce the calculation and effectively reduce the missed detection rate of small targets. Experimental results on the VisDrone2019 dataset show that the mAP50 value of the proposed method reaches 44.5%, which is 5.7 percentage points higher than that of the YOLOv8s baseline model. Simultaneously, the crack detection verification experiment is carried out on the self-built dam crack dataset, and the mAP50 value of the improved method is 3.3 percentage points higher than that of YOLOv8s, the FPS reaches 289 frames. Experimental results show that the proposed method improves the accuracy and real- time performance of the detection model in complex scene object detection, and has good adaptability and robustness. [ABSTRACT FROM AUTHOR]

Bearing faults can seriously reduce industrial production efficiency and even endanger the safety of people's lives and property. Monitoring the operating conditions of bearings conducting fault diagnosis are of great significance for ensuring the safe operation of the production process. This paper proposes a Multi-Scale and Attentive Convolutional Neural Network (MACNN) based on attention mechanism for bearing faults classification. Firstly, one-dimensional bearing signals are input into a convolution layer, followed by a maximum pooling layer to suppress noise and reduce information redundancy. Then, four MACNN modules are taken as input, each of which adopts a parallel network structure of ordinary convolution and void convolution. Under the premise without increasing model parameters, the model's receptive field is expanded to extract more fault features for improving the accuracy. In addition, the attention mechanism module is connected at the end of each MACNN model and the feature information is further extracted by using the ability of automatic extraction of important features. The average pooling layer is used in the network structure to prevent the overfitting and the full connection layer from the output of experimental classification results. Furthermore, the Boundary Equilibrium Generative Adversarial Networks (BEGAN) model is adopted to enhance fault data, change the proportion of unbalanced data sets, increase the number of dataset samples, reduce overfitting of MACNN model, and improve diagnostic accuracy. Finally, the experimental results on the Paderborn University Dataset show that MACNN can achieve better performance in feature extraction and fault classification, outperforming the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)