Data Extraction Method from Printed Images with Different Formats

2017 ◽  
Vol E100.A (11) ◽  
pp. 2355-2357
Author(s):  
Mitsuji MUNEYASU ◽  
Nayuta JINDA ◽  
Yuuya MORITANI ◽  
Soh YOSHIDA
Keyword(s):  
Extraction Method ◽  
Data Extraction

scholarly journals Klasterisasi Kerusakan Bearing Motor Induksi 3 Fasa Menggunakan Metode Transformasi Wavelet Diskrit dan K-Medoids

ELKHA ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 54
Author(s):  
Eska Rizqi Naufal ◽  
Gigih Priyandoko ◽  
Fachrudin Hunaini
Keyword(s):  
Induction Motor ◽  
Extraction Method ◽  
Data Extraction ◽  
Working Environment ◽  
Discrete Wavelet ◽  
Clustering Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Group Data ◽  
Low Pass

The 3 phase induction motor is a reliable and strong motor also has cheap price. However induction motor are also vulnerable, from the result of survey conducted by Electric Power Research Institute (EPRI), there are 41% cases of damage occur in the bearing caused by working environment condition, bearing age, and several other factors. Bearing fault is not easily to identified, with applying the data extraction method using the Discrete Wavelet Transform (DWT) and the K-Medoids clustering method will facilitate the identification process. The extraction method will pass the data in the form of current signals into the digital filter (Low Pass Filter and High Pass Filter) to be mapped into the region of frequency and time simultaneously, and clustering method will group data based on certain characteristics. Based on the clustering tests that have been done on the 3 phase induction motor current signal data with 3 bearing conditions, the Discrete Wavelet Transformation with mother wavelet bior1.1 decomposition level 2 and K-Medoids produce an accuracy rate of 86.8%.

PERANCANGAN BRAND CAMPAIGN SEBAGAI MEDIA PROMOSI THE PETITE PROJECT

Jurnal VICIDI ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 1-21
Author(s):  
Jesslyn Jovita
Keyword(s):  
Social Media ◽  
Product Development ◽  
Growth And Development ◽  
Extraction Method ◽  
Online Survey ◽  
Data Extraction ◽  
Secondary Data ◽  
Primary Data ◽  
Final Project ◽  
Spare Time

Creativity is very important to be honed, especially during the golden age of children, which is an important moment in the growth of every child which is very dear to miss and is a period that cannot be repeated. However, most parents still do not know the importance of honing children's creativity where they are still in the stage of growth and development, where their creativity needs to be honed as well as filling their children's spare time while at home. The design of this final project is carried out in order to educate the market about the importance of developing or honing children's creativity, so The Petite Project comes with a product, namely the DIY pom-pom kit which already contains tools and materials to make key chains that make it a more practical product without having to. buy tools and materials separately again. Because today many people use social media, the appropriate promotional media is social media to introduce and promote The Petite Project products in the form of a brand campaign. In collecting data for the purposes of writing a final project, the primary data and secondary data extraction method is used, where primary data is obtained through interviews with expert users and extreme users, while secondary data is obtained through literature sources from various books and journals. And the last one is done by distributing an online survey in the form of a Google Form with the aim of testing the design results that have been made to the target audience which will later be useful as criticism and suggestions for brand and product development from The Petite Project. The design results are in the form of a brand campaign to promote products as well as educate the market about the importance of honing children's creativity.   Keywords: Craft, Creativity, Product for kids, Promotion media, Social media.

Improved Latency of CAN Vehicle Data Extraction Method

2018 ◽  
pp. 14-26 ◽  
Author(s):  
Kavian Khosravinia ◽  
Mohd Khair Hassan ◽  
Ribhan Zafira Abdul Rahman ◽  
Syed Abdul Rahman Al-Haddad
Keyword(s):  
Extraction Method ◽  
Data Extraction ◽  
Vehicle Data

scholarly journals Locality-Based Visual Outlier Detection Algorithm for Time Series

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Zhihua Li ◽  
Ziyuan Li ◽  
Ning Yu ◽  
Steven Wen
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Extraction Method ◽  
Time Series Data ◽  
Data Extraction ◽  
Detection Algorithm ◽  
Series Data ◽  
Practical Application ◽  
Detection Model ◽  
Efficient Data

Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.

Relevant Data Node Extraction:A Web Data Extraction Method for Non Contagious Data

2020 ◽  
Author(s):  
Vaibhav Rupapara ◽  
Manideep Narra ◽  
Naresh Kumar Gonda ◽  
Kaushika Thipparthy
Keyword(s):  
Extraction Method ◽  
Data Extraction ◽  
Web Data ◽  
Web Data Extraction
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