Dynamic Data Assimilation - Beating the Uncertainties最新文献

Estimation for Motion in Tracking and Detection Objects with Kalman Filter 基于卡尔曼滤波的目标跟踪与检测运动估计

Dynamic Data Assimilation - Beating the Uncertainties Pub Date : 2020-10-08 DOI: 10.5772/intechopen.92863

Afef Salhi, F. Ghozzi, A. Fakhfakh

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引用次数: 4

Adaptive Filter as Efficient Tool for Data Assimilation under Uncertainties

Dynamic Data Assimilation - Beating the Uncertainties Pub Date : 2020-07-10 DOI: 10.5772/intechopen.92194

H. S. Hoang, R. Baraille

引用次数: 0

Kalman Filtering Applied to Induction Motor State Estimation 卡尔曼滤波在感应电机状态估计中的应用

Dynamic Data Assimilation - Beating the Uncertainties Pub Date : 2020-06-27 DOI: 10.5772/intechopen.92871

Yassine Zahraoui, M. Akherraz

引用次数: 5

Convolutional Neural Network Demystified for a Comprehensive Learning with Industrial Application 为工业应用的全面学习揭秘卷积神经网络

Dynamic Data Assimilation - Beating the Uncertainties Pub Date : 2020-06-13 DOI: 10.5772/intechopen.92091

Anandharaju Durai Raju, S. Thirunavukkarasu

{"title":"Convolutional Neural Network Demystified for a Comprehensive Learning with Industrial Application","authors":"Anandharaju Durai Raju, S. Thirunavukkarasu","doi":"10.5772/intechopen.92091","DOIUrl":"https://doi.org/10.5772/intechopen.92091","url":null,"abstract":"In the recent past of time, numerous investigators have driven on and subsidized novelties to image classification methods. In this chapter, an introduction to image classification scheme and their types is offered. Image classification discovers its application in a variety of fields, to name a few, judgment of diseases, finding and identification of faults, classification of nutrition goods based on superiority, valuation of usual capitals and conservation pollution, education of land use and land cover from remote sensing satellite images, character identification and detection in optical character reader, face recognition, object detection, and so on. Automatic image classification schemes found on actual algorithms deliver high accuracy and exactness in recognizing object/features. Convolution neural network is a superior genre of neural network that requires minimal preprocessing. The ability of the convolutional neural network (CNN) to understand the visual content of the input image makes its suitable for recognizing minute variation between the classes. This power of the CNN makes it a good choice to address image classification problems with multi-classes. So, in this chapter, the entire flow of CNN’s architecture with different industrial applications will be discussed.","PeriodicalId":250840,"journal":{"name":"Dynamic Data Assimilation - Beating the Uncertainties","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116892201","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

Data Processing Using Artificial Neural Networks 使用人工神经网络进行数据处理

Dynamic Data Assimilation - Beating the Uncertainties Pub Date : 2020-05-26 DOI: 10.5772/intechopen.91935

W. Alaloul, A. H. Qureshi

引用次数: 18