2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)最新文献_第6页

Research on Mask Wearing Detection Based on Faster RCNN 基于快速RCNN的口罩佩戴检测研究

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00128

Yahui Ding, Chang Liu, Hongjuan Wang, Zhengjian Chang

引用次数: 1

Research on Rebar Counting Based on YOLO-Rebar Model 基于YOLO-Rebar模型的钢筋计数研究

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00078

Haijun Wang, Minjian Long, Jianchun Wang, Congcong Guan

引用次数: 0

Helmet wear detection based on EfficientNet-Y 基于EfficientNet-Y的头盔磨损检测

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00141

Jie Liu, Lizhi Liu

{"title":"Helmet wear detection based on EfficientNet-Y","authors":"Jie Liu, Lizhi Liu","doi":"10.1109/AEMCSE55572.2022.00141","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00141","url":null,"abstract":"In response to government policies, \"Internet +\" has been integrated into the construction site to build a \"smart site\" ecosystem, and construction departments have begun to carry out visual management of the project. To address the problems of low recognition rate, slow detection speed, high hardware cost and complex construction site background for helmet wearing detection at construction sites, a lightweight model EfficientNet-Y is proposed in order to improve the detection accuracy, enhance the detection speed. The model uses EfficientNet backbone feature extraction network to replace the original. The experimental results demonstrate that the number of parameters of EfficientNet-Y model is reduced by 80% compared with the YOLOv3 model, and the mAP is increased by 1.5% compared with that of EfficientDet. The FPS is improved by 55% compared with YOLOv3 and doubled compared with EfficientDet, while the size of the model is only 1/4 of the volume size of YOLOv3 model. The newly constructed dataset resulted in a significant improvement in mAP for each model, with EfficientNet-Y improving by 7.95%, EfficientDet by 9.58%, and YOLOv3 by 5.01%.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115284237","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

Drug Purchase Prediction Model Algorithm 药品采购预测模型算法

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00084

Jin-rong Liu, Xing-yu Wu, Yong-xiang Feng

引用次数: 0

Research of Heart Disease Prediction Based on Machine Learning 基于机器学习的心脏病预测研究

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00071

Shuge Ouyang

引用次数: 3

Deep methods based on GAN for face-spoofing 基于GAN的人脸欺骗深度方法

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/aemcse55572.2022.00092

Lianghong Chen, Wenkai Li, Leyi Zhang

{"title":"Deep methods based on GAN for face-spoofing","authors":"Lianghong Chen, Wenkai Li, Leyi Zhang","doi":"10.1109/aemcse55572.2022.00092","DOIUrl":"https://doi.org/10.1109/aemcse55572.2022.00092","url":null,"abstract":"To prevent illegal access to users’ privacy by using face-spoofing, many researchers attempt to develop CNN models to identify it. However, only by working with high-quality face images, the CNN model can precisely report illegal accesses but is unreliable when the images are taken in bad conditions. To make up for the defect, this paper compares the performance of two kinds of more advanced neuron network models under the self-attention mechanism dealing with face-spoof issues. The first method is the Self-Attention GAN (SAGAN) model. Under the GAN framework, the SAGAN model introduces a self-attention mechanism to enable generator and discriminator to model the relationship between widely separated spatial regions. Based on this feature, SAGAN can be used to operate distant pixel points and then generate clear face images. The second research method is to apply the ViTGAN model to generate clear face images. Compared with developing the SAGAN model, general the ViTGAN model is a new approach, which can elegantly deal with the face images taken in the dark light conditions. This helps to CNN model cannot report face-spoofing issues with the input face images in the dark environment. To sum up, it is better to use the ViTGAN model to help to solve the face-spoofing issue.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121757477","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

Vocabulary enhancement in Chinese-named entity recognition 中文实体识别中的词汇增强

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00119

Lichen Xu, Xue-feng Fu, Yuehua Wu, Qian-Hui Gu

{"title":"Vocabulary enhancement in Chinese-named entity recognition","authors":"Lichen Xu, Xue-feng Fu, Yuehua Wu, Qian-Hui Gu","doi":"10.1109/AEMCSE55572.2022.00119","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00119","url":null,"abstract":"In the traditional Chinese-named entity recognition system, the word-based sequence labeling model is affected by the effect of word segmentation, which is easy to cause entity boundary detection errors. Although the character-based sequence labeling model avoids the error propagation of the word segmentation system, it loses a lot of lexical information because its model can only learn the original language signals at the character level. This leads to the blurred boundary of the entity and the poor effect of entity recognition. In order to solve the problem that it is difficult to demarcate the boundaries of Chinese-named entities, a vocabulary enhancement model is proposed. First of all, the model starts from the character-based sequence labeling model to avoid the error propagation of Chinese word segmentation. Then, it is integrated into the external lexicon to increase the lexical information and improve the entity boundary. Finally, the ERNIE pre-trained language model is introduced to supplement the hidden vocabulary features and improve the contextual information capture ability of words. Therefore, the model has a strong semantic awareness, which significantly improves the effect of Chinese-named entity recognition in each classical data set.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122048064","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

Classification efficiency of LassoNet model in image recognition lasonet模型在图像识别中的分类效率

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00083

Xingkai Wen, Zhiji Yang

{"title":"Classification efficiency of LassoNet model in image recognition","authors":"Xingkai Wen, Zhiji Yang","doi":"10.1109/AEMCSE55572.2022.00083","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00083","url":null,"abstract":"LassoNet is a neural network framework proposed by Robert Tibshirani et al. and published in the \"Journal of Machine Learning Research\" in 2021. The model generalizes the existing Lasso regression and its feature sparsity to a feedforward neural network, and performs feature selection and parameter learning at the same time under the premise of unknown optimal number of selected features. In order to verify whether the classification efficiency of LassoNet is efficient, LassoNet is first compared with four shallow learning methods (logistic regression, Fisher linear discriminant, random forest and support vector machine) and three deep learning methods (CNN, Inception and Residual Module), respectively. For the classification of high-dimensional and large-sample datasets in five different fields, the experimental results show that LassoNet has a significant classification effect, which is significantly better than the general shallow learning method, and is comparable to the deep learning method. It can be seen that LassoNet has strong versatility and It is easy to use, but it takes a lot of time to run. In the follow-up work, the feedforward neural network can be optimized or replaced to further improve the classification efficiency.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126442439","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

Design of temperature monitoring system for Space Environment Simulation and Research Infrastructure based on EPICS 基于EPICS的空间环境模拟与研究基础设施温度监测系统设计

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00007

Weiming Tong, Ze Xu, Xu Chu, Zhongwei Li

{"title":"Design of temperature monitoring system for Space Environment Simulation and Research Infrastructure based on EPICS","authors":"Weiming Tong, Ze Xu, Xu Chu, Zhongwei Li","doi":"10.1109/AEMCSE55572.2022.00007","DOIUrl":"https://doi.org/10.1109/AEMCSE55572.2022.00007","url":null,"abstract":"Space Environment Simulation and Research Infrastructure (SESRI) is one of the important technologies to promote the development of aerospace industry, and the ion accelerator system is one of the more important subsystems in SESRI. Aiming at the problem that the temperature of the ion accelerator is too high and the stability of the electric field is affected by the long-term operation of the ion accelerator, this paper designs an ion accelerator temperature monitoring system based on EPICS. In this paper, the temperature control principle of the ion accelerator is firstly analyzed, and according to its characteristics, the robust control of the temperature of the ion accelerator is realized by using the PID control algorithm. Secondly, based on EPICS, the overall scheme of the control system is designed, the EPICS operating environment is built, the communication function between the IOC and the underlying equipment is designed, and the OPI human-computer interaction is realized. Finally, the control effect and operation of the system are simulated. The simulation results show that the system has good stability and can control the temperature of the ion accelerator to meet the project control requirements.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130449410","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

Fuzzy PI Control of Permanent Magnet Synchronous Motor Based on Improved Differential Evolution Algorithm 基于改进差分进化算法的永磁同步电机模糊PI控制

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2022-04-01 DOI: 10.1109/AEMCSE55572.2022.00022

Xuancheng Zhang, Kailiang Long, Ningzhou Li, Lun Hai

引用次数: 0