Voltage Sag Data Sampling Method for Papermaking Process Based on Compressed Sensing Technology

2019 ◽  
Vol 51 (5) ◽  
pp. 61-72
Author(s):  
Yan Liu ◽  
Wei Tang ◽  
Wen-juan Shan
Keyword(s):  
Compressed Sensing ◽  
Sampling Method ◽  
Voltage Sag ◽  
Data Sampling ◽  
Sensing Technology

Constructing the Seismograms of Future Earthquakes in Yunnan, China, Using Compressed Sensing

2020 ◽  
Vol 92 (1) ◽  
pp. 261-274
Author(s):  
Jie Zhang ◽  
Huiyu Zhu ◽  
Siwei Yu ◽  
Jianwei Ma
Keyword(s):  
Compressed Sensing ◽  
Active Fault ◽  
Regional Scale ◽  
Focal Mechanism Solution ◽  
Total Output ◽  
Detailed Knowledge ◽  
Large Dataset ◽  
Sensing Technology ◽  
Data Driven Approach ◽  
Using Data

Abstract The ability to calculate the seismogram of an earthquake at a local or regional scale is critical but challenging for many seismological studies because detailed knowledge about the 3D heterogeneities in the Earth’s subsurface, although essential, is often insufficient. Here, we present an application of compressed sensing technology that can help predict the seismograms of earthquakes at any position using data from past events randomly distributed in the same area in Jinggu County, Yunnan, China. This first data-driven approach for calculating seismograms generates a large dataset in 3D with a volume encompassing an active fault zone. The input number of earthquakes comprises only 1.27% of the total output events. We use the output data to create a database intended to find the best-matching waveform of a new event by applying an earthquake search engine, which instantly reveals the hypocenter and focal-mechanism solution.

scholarly journals Online Recognition Method for Voltage Sags Based on a Deep Belief Network

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 43 ◽  
Author(s):  
Fei Mei ◽  
Yong Ren ◽  
Qingliang Wu ◽  
Chenyu Zhang ◽  
Yi Pan ◽  
...  
Keyword(s):  
Large Scale ◽  
Deep Belief Network ◽  
Support Vector ◽  
Voltage Sag ◽  
Voltage Sags ◽  
Data Sampling ◽  
Recognition Method ◽  
Belief Network ◽  
Online Recognition ◽  
Residential Electricity

Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.

scholarly journals Comparison of testing load cell sensor data sampling method based on the variation of time delay

2021 ◽  
Vol 700 (1) ◽  
pp. 012018
Author(s):  
A F Hastawan ◽  
S Haryono ◽  
A B Utomo ◽  
A Hangga ◽  
A Setiyawan ◽  
...  
Keyword(s):  
Time Delay ◽  
Sampling Method ◽  
Load Cell ◽  
Sensor Data ◽  
Data Sampling

Reconstruction of Slice Image from Solid Rocket Motor Skiagrams Based on Compressed Sensing Algorithm

2013 ◽  
Vol 321-324 ◽  
pp. 1035-1040
Author(s):  
Zhi Gao Xu ◽  
Chao Ning ◽  
Jing Ma ◽  
Xiang Bin Li
Keyword(s):  
Compressed Sensing ◽  
Rocket Motor ◽  
Reconstructed Image ◽  
Solid Rocket Motor ◽  
Sensing Technology ◽  
Radiographic Interpretation ◽  
Solid Rocket ◽  
Slice Image ◽  
Algebraic Reconstruction

A reconstruction program of slice image based on SolidRocket Motor (SRM) skiagrams is put forward to overcome the deficiency of artificial radiographic interpretation. The algebraic reconstruction algorithmbased on compressed sensing technology is designed. The influence of radiographic interval angle and skiagram sizes on reconstructed slice image is studied. Radiographic interval angle has a great impact on the quality of the reconstructed image. Slender defects are not sensitive to changes in the length of the skiagram, but circular defects are sensitive to changes in the length of the skiagram. The reconstruction tests of model SRM skiagrams show that the sizes and locations of the debonded defects can be easily ascertained and the efficiency of radiographic interpretation can be greatly improved.

scholarly journals SISTEM IJON PADA USAHATANI CENGKEH DI DESA RAANAN BARU KECAMATAN MOTOLING BARAT KABUPATEN MINAHASA SELATAN

2018 ◽  
Vol 14 (1) ◽  
pp. 175
Author(s):  
Sandro ., Pangemanan ◽  
Rine ., Kaunang ◽  
Jean F. J. Timban
Keyword(s):  
Sampling Method ◽  
Descriptive Analysis ◽  
Secondary Data ◽  
Research Data ◽  
Primary Data ◽  
Data Sampling ◽  
Analysis Method ◽  
Written Form ◽  
Preparation Phase ◽  
The Village

This study aims to describe the prevailing sijon system in clove farming in Raanan Baru Village, West Motoling Subdistrict. This research was conducted for 3 (three) months starting from the preparation phase until the preparation of the report that is since December 2017 s / d February 2018. The data used in this study is primary data which is research data obtained directly from the farmers as respondents and data secondary data which is a research data obtained indirectly through agency information or documents relating to this research and village data. Sampling method in this study using purposive sampling method, meaning that researchers determine their own samples taken because there are certain considerations. Data Analysis Method in this research is processed by using descriptive analysis and then Analysis This data is processed / presented in written form and table. The process of bond transfers in Raanan Baru Village has been going on for generations and has even taken root in the village community itself. From the results of case studies encountered in the field stated that the transaction system of ijon that occurred in Raanan Baru Village, West Motoling Subdistrict of South Minahasa Regency is done on the basis of both parties have agreed and have good intention to do the transaction of debt by only having mutual trust with each other.

scholarly journals PENGARUH MANAJEMEN ASET TERHADAP PENDAPATAN ASLI DAERAH PEMERINTAH KABUPATEN DAN KOTA DI JAWA BARAT

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Ony Widilestariningtyas
Keyword(s):  
Local Government ◽  
Asset Management ◽  
Research Method ◽  
Sampling Method ◽  
Secondary Data ◽  
Data Sampling ◽  
West Java ◽  
Verification Method ◽  
District Government ◽  
Take All

Conducted in the local cities and district government in West Java  this research was tookon 2014 period. The phenomenon encountered in the field is the embodiment of autonomy in theform of asset management that gave a less improvement to the local own-source revenue. The objective of this study is to determine how much influence of the asset managementon  local own-source revenue Cities and District Local Government in West Java. Research method that used in this research were descriptive and verification method withstatistical testing using Simple Linier Regression with secondary data. Sampling method used isto take all of population called census. The results showed that asset management has a significant effect on the local ownsourcerevenuewithapositivedirection,thatmeanthehigherassetmanagementpoint,thehigherlocalown-sourcerevenuewillbe.ThenThestrengthoftherelationshipwasindicatedashighcorrelation.

scholarly journals An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm

2021 ◽  
Vol 18 (6) ◽  
pp. 9076-9093
Author(s):  
Qun Song ◽  
◽  
Tengyue Li ◽  
Simon Fong ◽  
Feng Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Optimization Algorithm ◽  
Sampling Method ◽  
Adaptive Design ◽  
System Optimization ◽  
Data Series ◽  
Data Sampling ◽  
Metaheuristic Optimization ◽  
Metaheuristic Optimization Algorithm ◽  
Ecg Data

<abstract><p>With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity of a simple home-use IoT system is often limited. In order to provide a responsive and reasonably high-resolution analysis over the data, the ECG recorder sampling rate must be tuned to an acceptable level such as 50Hz (compared to between 100Hz and 500Hz in lab), a huge amount of time series are to be gathered and dealt with. Therefore, a suitable sampling method that helps shorten the ECG data transformation time and uploading time is very important for cost saving.. In this paper, how to down sample the ECG data is investigated; instead of traditional data sampling methods, the use of a novel Brick-up Metaheuristic Optimization Algorithm (BMOA) that automatically optimizes the sampling of ECG data is proposed. By its adaptive design in choosing the most appropriate components, BMOA can build in real-time a best metaheuristic optimization algorithm for each device user assuming no two ECG data series are exactly identical. This dynamic pre-processing approach ensures each time the most optimal part of the ECG data series is harvested for health analysis from the raw data, in different scenarios from different users. In this study various application scenarios using real ECG datasets are simulated. The experimentation is tested with one of the most commonly used ECG classification methods, Long Short-Term Memory Network. The result shows the ECG data sampling by BMOA is indeed adaptive, the classification efficiency is improved, and the data storage requirement is reduced.</p></abstract>

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