2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )最新文献_第8页

A Study of Hyperspectral Classification Combined with Minimum Noise Fraction and Variational Mode Decomposition 结合最小噪声分数和变分模态分解的高光谱分类研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929638

Linlin Chen, Linzhao Hao, Fulong Liu, Quan Chen

引用次数: 0

An Improved Time Defocus Analysis Method for Measuring 3D 一种改进的三维测量时间离焦分析方法

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929976

Qing Long, Hongning Li, Ming Yang, Xin Yang

引用次数: 0

Research on the Construction of Multivariate-Induced ischemic stroke prediction model based on medical big data 基于医疗大数据的多变量缺血性脑卒中预测模型构建研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929988

Xuejing Li, Xinhong Yao, Yanzheng Liang, Lujia Tang

{"title":"Research on the Construction of Multivariate-Induced ischemic stroke prediction model based on medical big data","authors":"Xuejing Li, Xinhong Yao, Yanzheng Liang, Lujia Tang","doi":"10.1109/IAEAC54830.2022.9929988","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929988","url":null,"abstract":"The incidence, mortality and morbidity of ischemic stroke are high, and there is a trend of younger people in recent years. For patients and medical staff, building a reliable and accurate ischemic stroke medical early warning model is of great practical significance for disease screening and prevention. In the era of big data that year, traditional statistical and data analysis methods can no longer meet the needs of intelligent medical early warning. Based on the Hadoop platform and combined with the parallel database technology, this paper analyzes the medical information data that affects the cerebral blood flow velocity, and builds a prediction model for the incidence of ischemic stroke on this basis. The model applies the timely feedback of cause analysis and correlation analysis to the construction of the prediction model, which provides a technical reference for intelligent medical early warning. This model not only realizes the huge storage of medical information, but also can be used for clinical medical prediction and individual self-screening of patients for long-term observation and prevention of ischemic stroke.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113990541","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

Prediction and Analysis of Energy Saving Potential for Fuel Cell Vehicles 燃料电池汽车节能潜力预测与分析

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929504

Wei Wang, Fufan Ou, Wenbo Li

引用次数: 0

The Vulnerability Testing Method and Management for Software Source Code 软件源代码漏洞测试方法与管理

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929808

Li Min, Jing Sen, Dong Bin, Chen Wei

引用次数: 0

Research on ground-penetrating radar denoising algorithm based on CEEMD and Permutation Entropy 基于CEEMD和置换熵的探地雷达去噪算法研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929797

Li Guo, Linhui Cai, Dejun Chen

引用次数: 0

Research on Data Exchange Schema for Railway Infrastructure 铁路基础设施数据交换模式研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9930074

Shuang Gu, Beisheng Liu, Xiangru Lv, Hui Li, Rongbo Wang

引用次数: 0

Research on rice leaf disease identification based on ResNet 基于ResNet的水稻叶病鉴定研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929925

Song Liang, Xiangwu Deng

{"title":"Research on rice leaf disease identification based on ResNet","authors":"Song Liang, Xiangwu Deng","doi":"10.1109/IAEAC54830.2022.9929925","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929925","url":null,"abstract":"The traditional identification of rice leaf disease has a long period and low accuracy, which depends on artificial design features. In this paper, a rice leaf disease identification network based on depth residual network is proposed. The network is based on ResNet 101 network, and the nonlinear SVM algorithm based on kernel function is introduced to make the data samples linearly divisible. Secondly, the plant Village data set is migrated to the parameters trained in ResNet 101 network to complete the construction. After verification, the network can better balance the requirements of recognition accuracy and network lightweight and efficient, and the average recognition accuracy of the model is as high as 99.89%. By observing the evaluation criteria of the models such as accuracy rate, it can be seen that the network proposed in this paper has higher comprehensive average recognition rate, faster convergence speed, better robustness and generalization ability than the reference model in rice leaf disease identification, and has good application prospects, and initially meets the production requirements of rice disease identification.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131898534","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

Sparse Broad Learning System via a Novel Competitive Swarm Optimizer 基于新型竞争群优化器的稀疏广义学习系统

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929651

Jiamin Li, Xiangyu Wang, Guang-Fu Xue, Huaqing Zhang, Jian Wang

{"title":"Sparse Broad Learning System via a Novel Competitive Swarm Optimizer","authors":"Jiamin Li, Xiangyu Wang, Guang-Fu Xue, Huaqing Zhang, Jian Wang","doi":"10.1109/IAEAC54830.2022.9929651","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929651","url":null,"abstract":"Broad Learning System is recently proposed intended as an alternative to deep learning neural networks. It has attracted a lot of attention due to its excellent performance on classification and regression problems. However, the typical BLS is based on grid search to determine the hidden layer nodes, which undoubtedly imposes a heavy training burden. On the other hand, BLS chooses to use sparse autoencoder to fine-tune the weights of input data to feature mapping nodes as a way to reduce the uncertainty caused by random mapping and extract sparse features. In contrast, this paper proposes a BLS that determines the hidden layer nodes and sparse hidden layer weights by solving a multi-objective optimization problem. Firstly, we propose a novel competitive swarm optimizer(NCSO) with embedded sparse operators, called S-NCSO. Second, a bi-objective optimization problem with an error cost and the lo norm of the hidden layer weights is solved by using S-NCSO to determine both the hidden layer nodes and the sparse hidden layer weights. Finally, experiments on multiple regression datasets show that the proposed method not only yields sparser weights and compact structures, but also greatly reduces the training time while improving the prediction accuracy.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114671169","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

A Text Summarization Model with Enhanced Local Relevance 一种增强局部相关性的文本摘要模型

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929557

Ouyang Ning, Junyan Wang, Peng Jiang, Xiao-Sheng Cai

{"title":"A Text Summarization Model with Enhanced Local Relevance","authors":"Ouyang Ning, Junyan Wang, Peng Jiang, Xiao-Sheng Cai","doi":"10.1109/IAEAC54830.2022.9929557","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929557","url":null,"abstract":"When generating short text summaries., it is challenging to accurately learn the global semantic information of the original text and extract the correlation features between local semantic information., and at the same time lead to too much redundant information., making the generated summaries ineffective. In addition., the existing normalization algorithm will increase the computational complexity of the text summarization model., which affects the performance of the model. Aiming at the above problems., a text summarization generation model GMELC(Generation Model for Enhancing Local Correlation) is proposed to enhance local correlation in generated summaries. First., the residual concept used in other media feature extraction networks is introduced into the text summarization model. We add the word semantic feature as a residual block to the n-gram feature., which improves the dependencies of words in phrases and strengthens the correlation between phrases and words in sentences. Secondly., we propose a scaled I2 normalization method to normalize the data for reducing the amount of training parameters and removing the unnecessary computation caused by variance., so that the computational complexity of the model is reduced., thereby improving the computational efficiency and performance of the model. In order to verify the role of the model in enhancing the correlation between Chinese characters and words., experiments were conducted on the Chinese dataset LCSTS., the result shows that the summaries generated by GMELC have higher recall and better readability than other state-of-the-art models.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123734586","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