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2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)最新文献

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Optimization of stator material and design of slot inclination angle of ultrasonic motors based on response characteristics of stator tooth surface 基于定子齿面响应特性的超声电机定子材料优化及槽倾角设计
2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00032
Fang Hu, Shouqing Huang, W. Dai, Taichun Qin, Fangyong Li, Chen Yu
{"title":"Optimization of stator material and design of slot inclination angle of ultrasonic motors based on response characteristics of stator tooth surface","authors":"Fang Hu, Shouqing Huang, W. Dai, Taichun Qin, Fangyong Li, Chen Yu","doi":"10.1109/CTISC52352.2021.00032","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00032","url":null,"abstract":"For traveling wave ultrasonic motor, the contact transmission between stator and rotor is a very important power transmission process, which determines the final output characteristics of the motor. Based on the idea that the uniform dynamic response at each position of the stator tooth surface helps to ensure the close contact between the stator friction plate and the rotor and the efficient operation, this paper proposes that the amplitude ratio of the outer ring and inner ring of the stator tooth surface is taken as the optimization design objective to optimize the stator material and optimize the design of the tooth slot inclination angle. In the finite element model, a refined geometric model including rotor assembly is established, and the influence of pre-pressure on the vibration characteristics of ultrasonic motor and the contact model between stator friction plate material and rotor are considered. The simulation results based on workbench software show that TC11 material has better uniformity of dynamic response of tooth surface among the three candidate materials, and the uniformity of dynamic response on tooth surface is the best when the inclination angle of tooth slot is about 4°.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125874987","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
Unsupervised Domain Adaption based on metric learning for Person Re-Identification 基于度量学习的无监督域自适应人物再识别
2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-04-01 DOI: 10.1109/CTISC52352.2021.00081
Roolmich Pierre, Meibin Qi
{"title":"Unsupervised Domain Adaption based on metric learning for Person Re-Identification","authors":"Roolmich Pierre, Meibin Qi","doi":"10.1109/CTISC52352.2021.00081","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00081","url":null,"abstract":"Person re-identification(ReID) with deep convolutional neural networks(CNNs) has attracted increasing interest in computer vision due to its wide potential applications in visual surveillance and has achieved high performance in recent years using a lot of techniques to overcome the challenges such as variations in view angle, lighting, image occlusion. Another main challenge in person re-identification(ReID) is the cross domain adaptation. Due to different domains, a person re-identification model trained on one dataset with good performance often fails to achieve same or better performance on other datasets. We propose a method which is about both the source and target datasets. We fine-tune the deep CNN model on the labeled source dataset in a supervised manner by using distance metric learning and the unlabeled target dataset in an unsupervised manner simultaneously.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115171947","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: 1
Generalized Image Reconstruction over T-Algebra 基于t代数的广义图像重构
2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2021-01-17 DOI: 10.1109/CTISC52352.2021.00076
L. Liao, Xue-Wu Zhang, Xinqiang Wang, Sen Lin, Xin Liu
{"title":"Generalized Image Reconstruction over T-Algebra","authors":"L. Liao, Xue-Wu Zhang, Xinqiang Wang, Sen Lin, Xin Liu","doi":"10.1109/CTISC52352.2021.00076","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00076","url":null,"abstract":"Principal Component Analysis (PCA) is well known for its capability of dimension reduction and data compression. However, when using PCA for compressing/reconstructing images, images need to be recast to vectors. The vectorization of images makes some correlation constraints of neighboring pixels and spatial information lost. To deal with the drawbacks of the vectorizations adopted by PCA, we used small neighborhoods of each pixel to form compoun pixels and use a tensorial version of PCA, called TPCA (Tensorial Principal Component Analysis), to compress and reconstruct a compound image of compound pixels. Our experiments on public data show that TPCA compares favorably with PCA in compressing and reconstructing images. We also show in our experiments that the performance of TPCA increases when the order of compound pixels increases.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124219894","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: 2
An improved helmet detection method for YOLOv3 on an unbalanced dataset 基于非平衡数据集的改进YOLOv3头盔检测方法
2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) Pub Date : 2020-11-09 DOI: 10.1109/CTISC52352.2021.00066
Rui Geng, Yixuan Ma, Wanhong Huang
{"title":"An improved helmet detection method for YOLOv3 on an unbalanced dataset","authors":"Rui Geng, Yixuan Ma, Wanhong Huang","doi":"10.1109/CTISC52352.2021.00066","DOIUrl":"https://doi.org/10.1109/CTISC52352.2021.00066","url":null,"abstract":"The YOLOv3 target detection algorithm is widely used in industry due to its high speed and high accuracy, but it has some limitations, such as the accuracy degradation of unbalanced datasets. The YOLOv3 target detection algorithm is based on a Gaussian fuzzy data augmentation approach to pre-process the data set and improve the YOLOv3 target detection algorithm. Through the efficient pre-processing, the confidence level of YOLOv3 is generally improved by 0.01-0.02 without changing the recognition speed of YOLOv3, and the processed images also perform better in image localization due to effective feature fusion, which is more in line with the requirement of recognition speed and accuracy in production.","PeriodicalId":268378,"journal":{"name":"2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114880955","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: 5
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