Proceedings of the 2022 6th International Conference on Deep Learning Technologies最新文献_第3页

A novel evolutionary algorithm for solving large-scale dynamic economic dispatch problem integrated with wind power 一种求解大规模风电动态经济调度问题的进化算法

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556699

Qun Niu, Likun Wang, Litao Yu

引用次数: 0

An Improved dynamic functional connectivity and deep neural network model for Autism Spectrum Disorder Classification 一种改进的动态功能连接和深度神经网络模型用于自闭症谱系障碍分类

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556694

Ming Li, Shanshan Tu, S. Rehman, Yong Jie Yang

{"title":"An Improved dynamic functional connectivity and deep neural network model for Autism Spectrum Disorder Classification","authors":"Ming Li, Shanshan Tu, S. Rehman, Yong Jie Yang","doi":"10.1145/3556677.3556694","DOIUrl":"https://doi.org/10.1145/3556677.3556694","url":null,"abstract":"Brain disorders such as autism spectrum disorder (ASD) is still difficult to diagnose. In the recent years, different novel deep learning algorithms have been applied to detect ASD. Most studies use the functional connectivity (FC) pattern to represent the brain activities. However, it has been investigated that dynamic functional connectivity (dFC) which represent more features than FC can characterize the intrinsic brain organization changes over time. The goal of this paper is to determine that dFC features are more successful than FC features in the classification of ASD using deep learning. In this paper, we propose a classification model using dFC and deep neural network. Firstly, we used windowed k-means (WKM) approach to compute the sub-state of the brain and extract the main features of the functional magnetic resonance imaging(fMRI). Then, two stacked denoising autoencoders were applied to extract the features and reduce the dimension. At last, the MLP was utilized to complete the classification task and do fine-tuning based on the autoencoder encoders weights. The experiments were carried out on the Autism Brain Imaging Data Exchange (ABIDE) datasets. Result shows that we acquired a mean accuracy of 68.51%. Overall, our proposed classification is effective and provide evidence that dFC contains more brain states features.","PeriodicalId":350340,"journal":{"name":"Proceedings of the 2022 6th International Conference on Deep Learning Technologies","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126535834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ultrasonic scanning image defect detection of plastic packaging components based on FCOS 基于FCOS的塑料包装部件超声扫描图像缺陷检测

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 2022-07-26 DOI: 10.1145/3556677.3556686

Yiwen Long, Mengyan Xiao, Xiaoqiang Wang, Bin Wang, Jun Luo, Shuo Diao

{"title":"Ultrasonic scanning image defect detection of plastic packaging components based on FCOS","authors":"Yiwen Long, Mengyan Xiao, Xiaoqiang Wang, Bin Wang, Jun Luo, Shuo Diao","doi":"10.1145/3556677.3556686","DOIUrl":"https://doi.org/10.1145/3556677.3556686","url":null,"abstract":"Defect detection of ultrasonic scanning images of plastic packaging components is mainly rely on manpower and not suitable for traditional feature extraction methods, to solve this problem, this paper put forward an optimized FCOS deep learning network to identify its delaminated defects. We redesign the backbone IResNeSt that consists of new bottleneck and data transmission path as the feature extraction module to enhance the information expression ability, furthermore, we introduce a feature pyramid network TF-FPN to improve the feature utilization. Finally, the complete proposed structure FCOS-ITN realizes the identification of various defects and retains more feature details. The experimental results show that compared with the typical object detection method, our FCOS-ITN applied on ultrasonic scan data set locates the delaminated region more accurately. As a matter of fact, the average accuracy (mAP) achieved 90.27% on all defect types, which is 6.58% higher than that of the original frame, indicating that our approach is feasible for non-destructive defect detection.","PeriodicalId":350340,"journal":{"name":"Proceedings of the 2022 6th International Conference on Deep Learning Technologies","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120911697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proceedings of the 2022 6th International Conference on Deep Learning Technologies 2022年第六届深度学习技术国际会议论文集

Proceedings of the 2022 6th International Conference on Deep Learning Technologies Pub Date : 1900-01-01 DOI: 10.1145/3556677

引用次数: 0