scholarly journals Identification of Single Spectral Lines in Large Spectroscopic Surveys Using UMLAUT: an Unsupervised Machine-learning Algorithm Based on Unbiased Topology

2021 ◽  
Vol 257 (2) ◽  
pp. 67
Author(s):  
I. Baronchelli ◽  
C. M. Scarlata ◽  
L. Rodríguez-Muñoz ◽  
M. Bonato ◽  
L. Morselli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Spectral Lines ◽  
Emission Lines ◽  
Apparent Magnitude ◽  
Machine Learning Algorithm ◽  
Unsupervised Machine Learning ◽  
Near Ir ◽  
Ir Spectroscopic ◽  
Single Emission

Abstract The identification of an emission line is unambiguous when multiple spectral features are clearly visible in the same spectrum. However, in many cases, only one line is detected, making it difficult to correctly determine the redshift. We developed a freely available unsupervised machine-learning algorithm based on unbiased topology (UMLAUT) that can be used in a very wide variety of contexts, including the identification of single emission lines. To this purpose, the algorithm combines different sources of information, such as the apparent magnitude, size and color of the emitting source, and the equivalent width and wavelength of the detected line. In each specific case, the algorithm automatically identifies the most relevant ones (i.e., those able to minimize the dispersion associated with the output parameter). The outputs can be easily integrated into different algorithms, allowing us to combine supervised and unsupervised techniques and increasing the overall accuracy. We tested our software on WISP (WFC3 IR Spectroscopic Parallel) survey data. WISP represents one of the closest existing analogs to the near-IR spectroscopic surveys that are going to be performed by the future Euclid and Roman missions. These missions will investigate the large-scale structure of the universe by surveying a large portion of the extragalactic sky in near-IR slitless spectroscopy, detecting a relevant fraction of single emission lines. In our tests, UMLAUT correctly identifies real lines in 83.2% of the cases. The accuracy is slightly higher (84.4%) when combining our unsupervised approach with a supervised approach we previously developed.

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

scholarly journals Pet dog disease pre-diagnosis system for caregiver with possibilistic C-means clustering and disease database

2020 ◽  
Vol 20 (1) ◽  
pp. 300
Author(s):  
Kwang Baek Kim ◽  
Doo Heon Song
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Disease Symptom ◽  
Abnormal Behavior ◽  
Machine Learning Algorithm ◽  
Unsupervised Machine Learning ◽  
Diagnosis System ◽  
Pet Dogs ◽  
Pet Owners ◽  
Pet Owner

<p>While the population of pet dogs and veterinary clinics are increasing, there is no reliable and useful software for pet owners/caregivers who have limited knowledge on the pet diseases. In this paper, we propose a pre-diagnosis system working on the mobile platform that the pet owner can take a pre-diagnosis from his/her observation of pet dog’s abnormality. Technically, the system needs a reliable databases for disease-symptom association thus we provide it based on the textbook and encyclopedia. Then, we apply Possibilistic C-Means algorithm that is an unsupervised machine learning algorithm to form the connections between disease and symptoms from database. The system outputs five most probable diseases from the observed symptoms of pet dog. The utility of this system is to alert the owner’s attention on the pet dog’s abnormal behavior and try to find the diseases as soon as possible.</p>

Automatic microseismic event picking via unsupervised machine learning

2020 ◽  
Vol 222 (3) ◽  
pp. 1750-1764 ◽  
Author(s):  
Yangkang Chen
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
State Of The Art ◽  
The State ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Unsupervised Machine Learning ◽  
Earthquake Data

SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

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