scholarly journals Comparison of Spatio-Spectral Properties of Zen-Meditation and Resting EEG Based on Unsupervised Learning

2021 ◽  
Vol 11 (02) ◽  
pp. 58-72
Author(s):  
Pei-Chen Lo ◽  
Nasir Hussain
Keyword(s):  
Unsupervised Learning ◽  
Spectral Properties

Model of multicomponent narrow-band periodical non-stationary random signal

2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Hilbert Transform ◽  
Spectral Properties ◽  
Narrow Band ◽  
Analytic Signal ◽  
Random Signal ◽  
Hilbert Transformation ◽  
The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

Structure and Spectral Properties of New Composites Based on Metal Alkanoates with Gold Nanoparticles

2015 ◽  
Vol 60 (04) ◽  
pp. 356-361 ◽  
Author(s):  
A. Tolochko ◽  
◽  
P. Teselko ◽  
A. Lyashchova ◽  
D. Fedorenko ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Spectral Properties

PHYSICOCHEMICAL POINTS OF SYNTHESIS OF LONG PERSISTENCE PHOSPHORS ON THE BASIS OF YTTRIUM OXOSULFIDE. REPORT 3. STUDY OF Er3+ IONS INFLUENCE ON SPECTRAL PROPERTIES OF Y2O2S: Ti, Mg

2007 ◽  
Vol 3 (2) ◽  
pp. 20-26
Author(s):  
A.A. Bogatureva ◽  
◽  
V.M. Ishenko ◽  
O.Y. Manashirov ◽  
A.N. Georgobiani ◽  
...  
Keyword(s):  
Spectral Properties

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

An Unsupervised Learning Algorithm to Compute Fluid Volumes From NMR T1-T2 Logs in Unconventional Reservoirs

2018 ◽  
Vol 59 ◽  
pp. 617-632 ◽  
Author(s):  
Lalitha Venkataramanan ◽  
◽  
Noyan Evirgen ◽  
David F. Allen ◽  
Albina Mutina ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Learning Algorithm ◽  
Unconventional Reservoirs

Klasifikasi pada Tempat Tinggal Menurut Provinsi dan Jenis Kepemilikan Berdasarkan Algoritma K-Means

2020 ◽  
Vol 4 (3) ◽  
pp. 247
Author(s):  
Dwi Swasono Rachmad
Keyword(s):  
Data Mining ◽  
Unsupervised Learning ◽  
Residential Buildings ◽  
Government Agency ◽  
Role Of Government ◽  
The Republic ◽  
Household Processing ◽  
Central Statistics

<p><em>H</em><em>ousing is derived from the word house</em><em> which means</em><em> a place that has a place to live which will stay or stop in a certain time. Housing is a residence that has been grouped into a place that has facilities and infrastructure. The problem in this study focuses on the type of residential ownership in the form of SHM ART, SHM Non ART, NON SHM and others. </em><em>T</em><em>hese four types</em><em> can be used</em><em> to know the percentage of ownership in all provinces in Indonesia. Due to the fact that there is still a lot of information about the type of certificate ownership, there is still not much ownership. Therefore, the use of the k-Means algorithm as a data mining concept in the form of clusters, where the data already has parameters or values that fall into the category of unsupervised learning. That data produced the best. The data was obtained from published sources of the Republic of Indonesia government agency, namely the Central Statistics Agency data with the category of household processing with self-owned residential buildings purchased from developers or non-developers by province and type of ownership in 2016 throughout Indonesia. In conducting the dataset, researchers used the RapidMiner application as a clustering process application. This research </em><em>shows that</em><em> there are more types of ownership in the SHM ART, but for other values it is still smaller than the value in other types of ownership which is the second largest value. So</em><em>,</em><em> in this case, the role of government in providing assistance in the process of ownership in order to become SHM ART</em><em> is very important</em><em>.</em></p>

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