检索结果-内蒙古大学图书馆

2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Sun, Chengfu Ni, Yang Du, Rui Huaiyin Institute of Technology School of Computer and Software Engineering Huaian China

ISBN: (纸本)9798400718199

targeting issues where the fault features in the chemical production process are hard to discern and the fault diagnosis method's accuracy is low. This paper proposes an improved CNN-BiGRU (Convolutional Neural Networks and Bidirectional Gated Recurrent Unit) model, which is different from the usual serial structure, and adopts a parallel structure to combine the improved CNN and Bidirectional Gated Recurrent Unit. First, CNN is used to extract local features adaptively, and a batch normalization (BN) layer is used in CNN to avoid internal covariate shift and accelerate network training. In order to prevent overfitting and minimize network calculations, the global pooling layer is introduced. Second, BiGRU is used to extract global features from the data, and by incorporating a linear layer into the conventional BiGRU network, the model's accuracy is increased. Eventually, the fusion layer fuses the features that were extracted by the two networks. The experiment's outcomes demonstrate how well the model can raise fault detection accuracy. Taking the Eastman Process of Tennessee process (TE) as the research object, experiments show that the proposed method has excellent performance. © 2024 ACM.

关键词： Failure analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-angle face expression recognition based on integration of lightweight deep network and key point feature positioning

Multi-angle face expression recognition based on integration...

引用

International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Gao, Han Mokhtarzadeh, Amir Ali Li, Shaofan Fei, Hongyan Geng, Junzuo Wang, Deye College of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China

In the current advancement of science and technology, robot and machine vision technology have significant application value. Due to their mimetic structure, quadruped robots are better equipped to adapt to the requirements of walking over complex terrain. This paper introduce lightweight deep network and combining key point feature positioning for multi-angle face expression recognition. Using robot dog to recognize facial expressions will be affected by distance and angle. To solve this problem, this paper proposes a method for facial expression recognition at different distances and angles,which solved the larger distance and deflection angle of face expression recognition accuracy and real-time issues. The dataset used in this study was collected by adjusting the camera mounted in an AI robotic dog in a laboratory scenario. The result shows a common and fast and intuitive visual representation of human beings to convey emotions or mental states, facial expression is an effective way for robots to obtain information and interact with people through visual images. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Pose-aware Multi-position Feature Network for Driver Distraction Recognition

Pose-aware Multi-position Feature Network for Driver Distrac...

引用

International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Li, Shaofan Mokhtarzadeh, Amir Ali Gao, Han Zhang, Yingying College of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China

This paper introduce pose-aware multi-position feature network for driver distraction recognition, taking into account the high association between the visual and geometric features of neighboring human key points. This method first uses the object detector to detect the driver's body, and then uses the pose estimation to detect the key points of the human body. Finally, the key points of the human body are deconstructed to obtain key visual features and spatial *** order to create the visual representation that corresponds to each key point, all the chosen visual representations are sent into a convolutional neural network. The geometric locations of key points on the upper body are used to build the spatial reasoning module, which is then transmitted to the neural network to get spatial features. The visual appearance features are fused with geometric features to predict corresponding driver action through linear *** experimental result shows that our methods achieve comparative performance on our own HY Large Vehicle Driver Dataset and the public AUC Driver Distracted Dataset. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

YOLO-RLC:An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5

引用

computers, Materials & Continua 2024年第9期80卷 4973-4995页

作者： Yuanyuan Wang Jialong Huang Md Sharid Kayes Dipu Hu Zhao Shangbing Gao Haiyan Zhang Pinrong Lv School of Computer and Software Engineering Huaiyin Institute of TechnologyHuai’an223003China Huaiyin Institute of Technology Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu ProvinceHuai’an223001China Jiangsu Kesheng Xuanyi Technology Co. Ltd.Huai’an223300China

Printed circuit boards(PCBs)provide stable connections between electronic ***,defective printed circuit boards may cause the entire equipment system to malfunction,resulting in incalculable ***,it is crucial to detect defective printed circuit boards during the generation *** detection methods have low accuracy in detecting subtle defects in complex background *** 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 Only Look Once-Residual Large Kernel).Build a deep large kernel backbone to expand the effective field of view,capture global informationmore efficiently,and use 1×1 convolutions to balance the depth of the model,improving feature extraction efficiency through reparameterization *** neck network introduces a bidirectional weighted feature fusion network,combined with a brand-new noise filter and feature enhancement extractor,to eliminate noise information generated by information fusion and recalibrate information from different channels to improve the quality of deep *** the aspect ratio of the bounding box to alleviate the issue of specificity *** training and testing on the PCB defect dataset,our method achieved an average accuracy of 97.3%(mAP50)after multiple experiments,which is 4.1%higher than YOLOv5-S,with an average accuracy of 97.6%and an Frames Per Second of *** comparative analysis also proves the superior performance and feasibility of YOLO-RLC in PCB defect detection.

关键词： Deep learning PCB defect detection large kernel noise filtering weighted fusion YOLO

来源：评论

学校读者我要写书评

暂无评论

Cleaning of Multi-Source Uncertain Time Series Data Based on PageRank

引用

Journal of Donghua University(English Edition) 2023年第6期40卷 695-700页

作者：高嘉伟孙纪舟 Faculty of Computer and Software Engineering Huaiyin Institute of TechnologyHuai’an 223003China

There are errors in multi-source uncertain time series *** discovery methods for time series data are effective in finding more accurate values,but some have limitations in their *** tackle this challenge,we propose a new and convenient truth discovery method to handle time series data.A more accurate sample is closer to the truth and,consequently,to other accurate *** the mutual-confirm relationship between sensors is very similar to the mutual-quote relationship between web pages,we evaluate sensor reliability based on PageRank and then estimate the truth by sensor ***,this method does not rely on smoothness assumptions or prior knowledge of the ***,we validate the effectiveness and efficiency of the proposed method on real-world and synthetic data sets,respectively.

关键词： big data data cleaning time series truth discovery PageRank

来源：评论

学校读者我要写书评

暂无评论

Reflective clothing detection based on YOLOv5s fused with SE attention mechanism

引用

International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 9-16页

作者： Wang, Yuanyuan Gu, Dingyun Zhu, Yemeng Zhang, Zhihan Cao, Jiahui Zhang, Haiyan School of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Engineering Laboratory of Mobile Interconnection Technology for Internet of Things Jiangsu Province Huai’an Huaian City223001 China

This paper proposes a YOLOv5s deep learning algorithm incorporating the SE attention mechanism to address the issue of workers failing to wear reflective clothing on duty, which has resulted in casualties from time to time. The YOLOv5s model is used to train the reflective clothing dataset obtained by collecting images of construction sites, and the SE attention mechanism module is added to the network structure of YOLOv5 for improved performance. The reflective clothing detection algorithm is then deployed to the web for real-time detection. The experimental results show that the algorithm achieves mAP value of 96.70% with the SE attention module, resulting in an accuracy improvement of 1.09% compared to the model without the SE module. This enables practical applications to reduce the incidence of construction accidents at construction sites. Coрyright © 2025 Inderscience Enterрrises Ltd.

关键词： attention mechanism flask reflective clothing detection vue YOLOv5

来源：评论

学校读者我要写书评

暂无评论

Lightweight medical image segmentation network based on ghost convolution and attention mechanism 2

Lightweight medical image segmentation network based on ghos...

引用

2nd International Symposium on computer Applications and Information Systems, ISCAIS 2023

作者： Wang, Haoran Yu, Kun Li, Qiangqiang Guan, Qianjun Gao, Shihong College of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huai’an223003 China

ISBN: (纸本)9781510666665

Ghostnet is a lightweight image classification network proposed by Kai Han and other scholars in 2019 which was applied in terms of object detection. Ghostnet has the advantages of few parameters and high accuracy. In this paper, through the idea of transfer learning, ghost net is used as the network of feature extraction and CBAM attention mechanism is added. CBAM is not only a lightweight attention mechanism, but also a good combination of space and channels. CBAM eliminate irrelevant noise intelligences and concern to key intelligences. At the same time, the ghost convolution block is improved, and point-by point convolution is added to the ghost convolution block to obtain more image feature information. Add the squeeze and enumeration module to improve the convolution receptive field, increase the ability of the network to extract multi-scale spatial information, and introduce the residual idea to tackle the problem of information loss and gradient descent disappearance. GAUnet, designed according to the above idea, achieves better intersection and merger ratio than Unet, Unet++, DeepLabV3+and other neural networks on the multi- organ segmented chaos dataset with fewer parameters. © 2023 SPIE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

An enterprise portrait tag extraction method based on context embedding and knowledge distillation

引用

Soft Computing 2024年第20期28卷 11823-11834页

作者： Li, Xiang Ding, Xingshuo Zhu, Quanyin Ma, Jialin Zhou, Hong Sun, Jizhou Faculty of Computer and Software Huaiyin Institute of Technology Jiangsu Huaian223003 China Qingdao Ocean Shipping Mariners College Shandong Qingdao266400 China

Enterprise portraits are reliant on the meticulous process of tag extraction, with the accuracy and efficiency of this foundational task being pivotal for the construction of high-quality portraits and their subsequent applicability in varied real-world scenarios. However, current methodologies, chiefly semantic clustering and deep neural networks, are constrained by significant shortcomings: the former struggles with intricate text feature discernment, while the latter, although high-performing, are marked by their extensive computational requirements. In response to these challenges, we present EPCEKD, a novel approach leveraging context embedding and knowledge distillation, designed to refine the tag extraction process. EPCEKD utilizes structured enterprise context data to facilitate feature expansion and impose tag constraints, enabling the efficient vectorization and integration of enterprise context data and text. Further, EPCEKD amalgamates a large-scale BERT network and a streamlined GRU network, embedding the branch network into the backbone network to forge a unified tag extraction model. To validate the efficacy of EPCEKD, comprehensive experiments were conducted on the Ente-pku dataset and benchmarked against eight prevailing models, with EPCEKD demonstrating superior performance, achieving 95.17% in precision, recall, and F1-score, significantly surpassing models like BiLSTM-CNN (94.25%) and Transformer (91.46%). Importantly, EPCEKD not only excels in precision and F1-score but also enhances the computing speed of large-scale neural networks by 4-11.4 times, showcasing its substantial potential for practical enterprise portrait applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

An Efficient Heterogeneous Ring Signcryption Scheme for Wireless Body Area Networks

引用

computer Systems Science & engineering 2023年第11期47卷 2061-2078页

作者： Qingqing Ning Chunhua Jin Zhiwei Chen Yongliang Xu Huaqi Lu Faculty of Computer&Software Engineering Huaiyin Institute of TechnologyHuai’an233003China

Wireless body area networks(WBANs)are an emerging technology for the real-time monitoring of physiological *** provide a mechanism for collecting,storing,and transmitting physiological data to healthcare ***,the open wireless channel and limited resources of sensors bring security *** ensure physiological data security,this paper provides an efficient Certificateless Public Key Infrastructure Heterogeneous Ring Signcryption(CP-HRSC)scheme,in which sensors are in a certificateless cryptosystem(CLC)environment,and the server is in a public key infrastructure(PKI)*** could solve the limitations of key escrow in identity-based cryptography(IBC)and certificate management for public keys in *** PKI is suited for the server because it is widely used on the ***,this paper designs a ring signcryption method that allows the controller to anonymously encrypt physiological data on behalf of a set of sensors,but the server does not exactly know who the sensor *** construction of this paper can achieve anonymity,confidentiality,authentication,non-repudiation,and integrity in a logically single *** the computational Diffie-Hellman(CDH)problem,the formal security proof is provided in the random oracle model(ROM).This paper demonstrates that this scheme has indistinguishability against adaptive chosen ciphertext attacks(IND-CCA2)and existential unforgeability against adaptive chosen message attacks(EUF-CMA).In terms of computational cost and energy usage,a comprehensive performance analysis demonstrates that the proposed scheme is the most *** to the three existing schemes,the computational cost of this paper’s scheme is reduced by about 49.5%,4.1%,and 8.4%,and the energy usage of our scheme is reduced by about 49.4%,3.7%,and 14.2%,respectively.

关键词： Wireless body area networks certificateless cryptosystem public key infrastructure security ring singncryption

来源：评论

学校读者我要写书评

暂无评论

A Semi-Anchoring Guided High-Resolution Capsule Network for Vehicle Logo Recognition

A Semi-Anchoring Guided High-Resolution Capsule Network for ...

引用

2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Wang, Lanfang Yu, Zhenyang Yu, Yongtao Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

As a unique identity reflecting the manufacturer of the vehicle, the vehicle logo information plays an important role in many transportation-related applications. However, due to the challenges of size variations, shape and form diversities, deformations, occlusions, and complex scenarios, it is still not an easy task to realize highly accurate vehicle logo recognition from images. This paper proposes a novel semi-anchoring guided high-resolution capsule network (SAGHR-CapsNet) for vehicle logo recognition. First, constructed with a multibranch high-resolution capsule network architecture functioned with repeated multiresolution feature fusion for feature extraction, the SAGHR-CapsNet can extract semantically strong and spatially accurate feature representations at each scale. Second, designed with a capsule-based efficient self-attention mechanism for feature semantic promotion, the SAGHRCapsNet functions excellently to attend to channel-wise informative features and target-oriented spatial features. Finally, adopted with a semi-anchoring guided strategy for vehicle logo recognition, the SAGHR-CapsNet performs promisingly to simultaneously improve the processing efficiency and guarantee the recognition accuracy. Intensive quantitative evaluations and comparative analyses on two large-scale data sets demonstrated the applicability and superiority of the SAGHR-CapsNet in vehicle logo recognition tasks. © 2023 IEEE.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：