Evaluation of Semi-rigid Base Performance Through Numerical Simulation and Data Mining of Pavement Deflection Basin

2018 ◽  
pp. 364-371 ◽  
Author(s):  
Xuqiu Cui ◽  
Qiao Dong ◽  
Fujian Ni ◽  
Xiaolong Liang
Keyword(s):  
Numerical Simulation ◽  
Data Mining ◽  
Rigid Base

Seismic Hazard Visualization from Big Simulation Data: Construction of a Parallel Distributed Processing System for Ground Motion Simulation Data

2016 ◽  
Vol 11 (2) ◽  
pp. 265-271 ◽  
Author(s):  
Takahiro Maeda ◽  
◽  
Hiroyuki Fujiwara
Keyword(s):  
Numerical Simulation ◽  
Data Mining ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Large Scale ◽  
Distributed Processing ◽  
Processing System ◽  
Great Earthquake ◽  
Simulation Data ◽  
Parallel Distributed Processing

We have developed a data mining system of parallel distributed processing system which is applicable to the large-scale and high-resolution numerical simulation of ground motion by transforming into ground motion indices and their statistical values, and then visualize their values for the seismic hazard information. In this system, seismic waveforms at many locations calculated for many possible earthquake scenarios can be used as input data. The system utilizes Hadoop and it calculates the ground motion indices, such as PGV, and statistical values, such as maximum, minimum, average, and standard deviation of PGV, by parallel distributed processing with MapReduce. The computation results are being an output as GIS (Geographic Information System) data file for visualization. And this GIS data is made available via the Web Map Service (WMS). In this study, we perform two benchmark tests by applying three-component synthetic waveforms at about 80,000 locations for 10 possible scenarios of a great earthquake in Nankai Trough to our system. One is the test for PGV calculation processing. Another one is the test for PGV data mining processing. A maximum of 10 parallel processing are tested for both cases. We find that our system can hold the performance even when the total tasks is larger than 10. This system can enable us to effectively study and widely distribute to the communities for disaster mitigation since it is built with data mining and visualization for hazard information by handling a large number of data from a large-scale numerical simulation.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Water uptake by substituted amylose tablets: experimentation and numerical simulation

2009 ◽  
Vol 00 (00) ◽  
pp. 090904073309027-8
Author(s):  
H.W. Wang ◽  
S. Kyriacos ◽  
L. Cartilier
Keyword(s):  
Numerical Simulation ◽  
Water Uptake

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

Post, Mine, and Be Disturbed: Social Media Data Mining

PsycCRITIQUES ◽  
2016 ◽  
Vol 61 (51) ◽  
Author(s):  
Daniel Keyes
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Media Data ◽  
Media Data
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