scholarly journals Finding Possible Precursors for the 2015 Cotopaxi Volcano Eruption Using Unsupervised Machine Learning Techniques

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Juan C. Anzieta ◽  
Hugo D. Ortiz ◽  
Gabriela L. Arias ◽  
Mario C. Ruiz
Keyword(s):  
Seismic Station ◽  
Low Frequency ◽  
Machine Learning Techniques ◽  
Time Warping ◽  
Learning Techniques ◽  
Cotopaxi Volcano ◽  
Density Vector ◽  
Stage Process ◽  
Dynamic Time ◽  
Increased Activity

Cotopaxi Volcano showed an increased activity since April 2015 and evolved into its eventual mild eruption in August 2015. In this work we use records from a broadband seismic station located at less than 4 km from the vent that encompass data from April to December of 2015, to detect and study low-frequency seismic events. We applied unsupervised learning schemes to group and identify possible premonitory low-frequency seismic families. To find these families we applied a two-stage process in which the events were first separated by their frequency content by applying the k-means algorithm to the spectral density vector of the signals and then were further separated by their waveform by applying Correntropy and Dynamic Time Warping. As a result, we found a particular family related to the volcano’s state of activity by exploring its time distribution and estimating its events’ locations.

scholarly journals Using Abductive Machine Learning for Online Vibration Monitoring of Turbo Molecular Pumps

1999 ◽  
Vol 6 (5-6) ◽  
pp. 253-265 ◽  
Author(s):  
R.E. Abdel-Aal ◽  
M. Raashid
Keyword(s):  
Machine Learning ◽  
Online Monitoring ◽  
Model Development ◽  
Low Frequency ◽  
Vibration Monitoring ◽  
Machine Learning Techniques ◽  
Vibration Signals ◽  
Output Performance ◽  
Learning Techniques ◽  
The Status

Turbo molecular vacuum pumps constitute a critical component in many accelerator installations, where failures can be costly in terms of both money and lost beam time. Catastrophic failures can be averted if prior warning is given through a continuous online monitoring scheme. This paper describes the use of modern machine learning techniques for online monitoring of the pump condition through the measurement and analysis of pump vibrations. Abductive machine learning is used for modeling the pump status as ‘good’ or ‘bad’ using both radial and axial vibration signals measured close to the pump bearing. Compared to other statistical methods and neural network techniques, this approach offers faster and highly automated model synthesis, requiring little or no user intervention. Normalized 50-channel spectra derived from the low frequency region (0–10 kHz) of the pump vibration spectra provided data inputs for model development. Models derived by training on only 10 observations predict the correct value of the logical pump status output with 100% accuracy for an evaluation population as large as 500 cases. Radial vibration signals lead to simpler models and smaller errors in the computed value of the status output. Performance is comparable with literature data on a similar diagnosis scheme for compressor valves using neural networks.

scholarly journals Fast estimation of earthquake arrival azimuth using a single seismological station and machine learning techniques

2019 ◽  
Vol 23 (2) ◽  
pp. 103-109 ◽  
Author(s):  
Luis Hernán Ochoa Gutierrez ◽  
Carlos Alberto Vargas Jiménez ◽  
Luis Fernando Niño Vásquez
Keyword(s):  
Machine Learning ◽  
Seismic Station ◽  
Machine Learning Techniques ◽  
Time Signal ◽  
Support Vector ◽  
Seismic Events ◽  
Frequency Noise ◽  
Learning Techniques ◽  
Vector Machines ◽  
Time Signals

The objective of this research is to apply a new approach to estimate arrival azimuth of seismic events using seismological records of the “El Rosal” station, near to the city of Bogota – Colombia, by applying support vector machines (SVMs). The algorithm was trained with time signal descriptors of 863 seismic events acquired from January 1998 to October 2008; considering only events with magnitude ≥ 2 ML.  The earthquake signals were filtered in order to remove diverse kind of low and high frequency noise not related to such events. During training stages of SVMs, several combinations of kernel function exponent and complexity factor were applied to time signals of 5, 10 and 15 seconds along with earthquake magnitudes of 2.0, 2.5, 3.0 and 3.5 ML. The best classification of SVMs was obtained using time signals of 5 seconds and earthquake magnitudes greater than 3.0 ML with kernel exponent of 10 and complexity factor of 2, showing accuracy of 45.4 degrees. This research is an improvement of previous works related to earthquake arrival azimuth determination from data of one single seismic station employing machine learning techniques. 

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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