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Approach to enhancement of spatial resolution of the rigid objects satellite imagery 提高刚体卫星图像空间分辨率的方法
Machine Learning and Data Analysis Pub Date : 2019-03-19 DOI: 10.21469/22233792.4.5.01
A. Murynin
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引用次数: 2
On the existence of soliton solutions for systems with a polynomial potential and their numerical realization 多项式势系统孤子解的存在性及其数值实现
Machine Learning and Data Analysis Pub Date : 2018-12-29 DOI: 10.21469/22233792.4.4.01
L. Beklaryan
{"title":"On the existence of soliton solutions for systems with a polynomial potential and their numerical realization","authors":"L. Beklaryan","doi":"10.21469/22233792.4.4.01","DOIUrl":"https://doi.org/10.21469/22233792.4.4.01","url":null,"abstract":"The problem of existence of soliton solutions (solutions of the traveling wave type) for the Korteweg-de Vries equation with a polynomial potential is considered on the basis of the approach within which the presence of a one-to-one correspondence of such solutions with solutions of the induced functional differential equation of pointwise type is demonstrated. On this path, conditions for the existence and uniqueness of solutions of the traveling wave type, with the growth restrictions both in time and in space, arise. It is very important that the conditions for the existence of a traveling wave solution are formed in terms of the right-hand side of the equation and the characteristics of the traveling wave, without using either the linearization and spectral properties of the corresponding equation in variations. Conditions for the existence of periodic soliton solutions are considered separately, and the possibility of transition from systems with a quasilinear potential to systems with a polynomial potential with conservation of corresponding existence theorems is demonstrated. Numerical implementation of such solutions is given.","PeriodicalId":361781,"journal":{"name":"Machine Learning and Data Analysis","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126879209","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
Features of the application of methods and algorithms for the reconstruction of the three-dimensional shape of rigid objects according to the panoramic survey 根据全景测量重建刚性物体三维形状的方法和算法的应用特点
Machine Learning and Data Analysis Pub Date : 2018-12-29 DOI: 10.21469/22233792.4.4.02
A. Murynin
{"title":"Features of the application of methods and algorithms for the reconstruction of the three-dimensional shape of rigid objects according to the panoramic survey","authors":"A. Murynin","doi":"10.21469/22233792.4.4.02","DOIUrl":"https://doi.org/10.21469/22233792.4.4.02","url":null,"abstract":"","PeriodicalId":361781,"journal":{"name":"Machine Learning and Data Analysis","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117153661","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: 3
Analysis of the neutron monitor data and allocation of the sporadic features on the basis of neural networks and wavelet transform 基于神经网络和小波变换的中子监测数据分析及零星特征分配
Machine Learning and Data Analysis Pub Date : 2018-12-29 DOI: 10.21469/22233792.4.4.03
O. Mandrikova
{"title":"Analysis of the neutron monitor data and allocation of the sporadic features on the basis of neural networks and wavelet transform","authors":"O. Mandrikova","doi":"10.21469/22233792.4.4.03","DOIUrl":"https://doi.org/10.21469/22233792.4.4.03","url":null,"abstract":"","PeriodicalId":361781,"journal":{"name":"Machine Learning and Data Analysis","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126754367","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
Automatic text summarization system using a stochastic model 采用随机模型的自动文本摘要系统
Machine Learning and Data Analysis Pub Date : 2018-12-29 DOI: 10.21469/22233792.4.4.04
T. Voznesenskaya
{"title":"Automatic text summarization system using a stochastic model","authors":"T. Voznesenskaya","doi":"10.21469/22233792.4.4.04","DOIUrl":"https://doi.org/10.21469/22233792.4.4.04","url":null,"abstract":"","PeriodicalId":361781,"journal":{"name":"Machine Learning and Data Analysis","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132126625","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
Application of machine learning methods for subject classification of the internet domains 机器学习方法在互联网领域主题分类中的应用
Machine Learning and Data Analysis Pub Date : 2018-12-21 DOI: 10.21469/22233792.4.3.05
A. Tleubaev
{"title":"Application of machine learning methods for subject classification of the internet domains","authors":"A. Tleubaev","doi":"10.21469/22233792.4.3.05","DOIUrl":"https://doi.org/10.21469/22233792.4.3.05","url":null,"abstract":"","PeriodicalId":361781,"journal":{"name":"Machine Learning and Data Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128422536","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
GLCM, kNN and Meanshift for neuron detection on Nissl-stained brain slice images GLCM、kNN和Meanshift对nissl染色的脑切片图像进行神经元检测
Machine Learning and Data Analysis Pub Date : 2018-12-21 DOI: 10.21469/22233792.4.3.04
S. Nosova
{"title":"GLCM, kNN and Meanshift for neuron detection on Nissl-stained brain slice images","authors":"S. Nosova","doi":"10.21469/22233792.4.3.04","DOIUrl":"https://doi.org/10.21469/22233792.4.3.04","url":null,"abstract":"The method for neuron detection on Nissl-stained brain slice images is proposed. The method uses textural features of neurons extracted from 4 GLC-matrices. The method includes the following steps: image preprocessing, kNN classification by the textural features and Meanshift clustering of neuron pixels. Preprocessing includes the following steps: grayscale conversion, histogram equalization, histogram quantization. Gray conversion by blue component gives the best result. It is shown that using 2-,4bin histogram gives close detection quality with 8-bin histogram (F1 = 0, 83..0, 85). For pixel classification kNN algorithm was used. The results demonstrate that kNN is better choice for current task in comparing with NBC. The reached detection quality for given approach is precision = 0, 82, recall = 0, 92, F1 = 0, 86. Is is shown that our results are near the same or some better in recall characteristic in comparing with other neuron detection method. In our future work we’ll prolong this investigation for great volume of dataset and special dataset for important diseases.","PeriodicalId":361781,"journal":{"name":"Machine Learning and Data Analysis","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131073422","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
On Information Theoretical Model for Data Classification 数据分类的信息理论模型研究
Machine Learning and Data Analysis Pub Date : 2018-12-19 DOI: 10.21469/22233792.4.3.03
Michael Lange
{"title":"On Information Theoretical Model for Data Classification","authors":"Michael Lange","doi":"10.21469/22233792.4.3.03","DOIUrl":"https://doi.org/10.21469/22233792.4.3.03","url":null,"abstract":"","PeriodicalId":361781,"journal":{"name":"Machine Learning and Data Analysis","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131436932","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
Analysis and prediction of hydrological series based on generalized precedents 基于广义先例的水文序列分析与预测
Machine Learning and Data Analysis Pub Date : 2018-12-03 DOI: 10.21469/22233792.4.3.02
V. Naumov
{"title":"Analysis and prediction of hydrological series based on generalized precedents","authors":"V. Naumov","doi":"10.21469/22233792.4.3.02","DOIUrl":"https://doi.org/10.21469/22233792.4.3.02","url":null,"abstract":"The paper presents a new approach to the use of the apparatus of generalized precedents in the problems of analysis and prediction of hydrological series. Generalized precedents are computational tools that yield to use various a priori, directly observed or preferred for some or another reason local regularities in the data on a unified basis. The main stages of the scheme for applying generalized precedents are presented, and a close relationship is shown with the Hough transform scheme. The possibilities of comparison and joint analysis of meteorological data and actual data on the volume of river flow are investigated. In this case, the generalized precedents are typical nonlinear relationships between certain hydrological parameters. The goal is to identify the differentiation of the regions of the river basin by their accumulating capabilities. We show how this can be done on the basis of an analysis of timelimited contemporary statistics. Obtained flow characteristics in the regions can be further used for short-term forecasting of river level variations and other hydrological processes and phenomena, including flood and drought situations. These characteristics can also serve as an important factor in the study of ecosystems, geology of the region and other similar purposes.","PeriodicalId":361781,"journal":{"name":"Machine Learning and Data Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127965222","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: 3
Studies of the relationship between non-stationary time series using the production functions 用生产函数研究非平稳时间序列之间的关系
Machine Learning and Data Analysis Pub Date : 2018-12-03 DOI: 10.21469/22233792.4.3.01
I. Kirilyuk
{"title":"Studies of the relationship between non-stationary time series using the production functions","authors":"I. Kirilyuk","doi":"10.21469/22233792.4.3.01","DOIUrl":"https://doi.org/10.21469/22233792.4.3.01","url":null,"abstract":"","PeriodicalId":361781,"journal":{"name":"Machine Learning and Data Analysis","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116451378","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
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