Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning

2021 ◽  
Author(s):  
Salma Barkaoui ◽  
Philippe Lognonné ◽  
Taichi Kawamura ◽  
Éléonore Stutzmann ◽  
Léonard Seydoux ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Detection Efficiency ◽  
Noise Analysis ◽  
Space Mission ◽  
Continuous Data ◽  
Thermally Induced ◽  
Pressure Drops ◽  
Detection Techniques ◽  
Seismic Experiment ◽  
Unsupervised Deep Learning

ABSTRACT The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

Image Tamper Blind Detection Based on CFA

2014 ◽  
Vol 496-500 ◽  
pp. 2237-2240
Author(s):  
Dong Sheng Ji ◽  
Rui Yang Yang ◽  
Ting Cao ◽  
Yu Kai Yao ◽  
Xiao Yun Chen
Keyword(s):  
Detection Efficiency ◽  
Noise Analysis ◽  
Experimental Results ◽  
The Other ◽  
Blind Detection ◽  
Detection Techniques ◽  
Single Feature ◽  
Lower Detection ◽  
Pattern Number

Most of the methods need to build a sophisticated classifier to detect the image tamper which leads the lower detection efficiency. For this problem, we propose a method based on artifacts detection techniques in the process produces of CFA, which includes one based on CFA pattern number estimation and the other noise analysis based on CFA. The techniques are based on computing a single feature and a simple threshold based on classifier, determine whether the mosaic artifacts of the CFA was changed. The experimental results show that, the proposed approach has higher performance.

scholarly journals Understanding the relationship between clouds and surface downward radiation forecast errors with Unsupervised Deep Learning

2021 ◽  
Author(s):  
Matthias Zech ◽  
Lueder von Bremen
Keyword(s):  
Deep Learning ◽  
Unsupervised Learning ◽  
Prediction Models ◽  
Weather Prediction ◽  
Reanalysis Data ◽  
Cloud Formation ◽  
Trade Wind ◽  
Forecast Errors ◽  
Model Learning ◽  
Unsupervised Deep Learning

<p>Cloudiness is a difficult parameter to forecast and has improved relatively little over the last decade in numerical weather prediction models as the EMCWF IFS. However, surface downward solar radiation forecast (ssrd) errors are becoming more important with higher penetration of photovoltaics in Europe as forecasts errors induce power imbalances that might lead to high balancing costs. This study continues recent approaches to better understand clouds using satellite images with Deep Learning. Unlike other studies which focus on shallow trade wind cumulus clouds over the ocean, this study investigates the European land area. To better understand the clouds, we use the daily MODIS optical cloud thickness product which shows both water and ice phase of the cloud. This allows to consider both cloud structure and cloud formation during learning. It is also much easier to distinguish between snow and cloud in contrast to using visible bands. Methodologically, it uses the Unsupervised Learning approach <em>tile2vec</em> to derive a lower dimensional representation of the clouds. Three cloud regions with two similar neighboring tiles and one tile from a different time and location are sampled to learn lower-rank embeddings. In contrast to the initial <em>tile2vec</em> implementation, this study does not sample arbitrarily distant tiles but uses the fractal dimension of the clouds in a pseudo-random sampling fashion to improve model learning.</p><p>The usefulness of the cloud segments is shown by applying them in a case study to investigate statistical properties of ssrd forecast errors over Europe which are derived from hourly ECMWF IFS forecasts and ERA5 reanalysis data. This study shows how Unsupervised Learning has high potential despite its relatively low usage compared to Supervised Learning in academia. It further shows, how the generated land cloud product can be used to better characterize ssrd forecast errors over Europe.</p>

scholarly journals Large High-Efficiency Thermal Neutron Detectors Based on the Micromegas Technology

Universe ◽  
2018 ◽  
Vol 4 (12) ◽  
pp. 134 ◽  
Author(s):  
Georgios Tsiledakis ◽  
Alain Delbart ◽  
Daniel Desforge ◽  
Ioanis Giomataris ◽  
Thomas Papaevangelou ◽  
...  
Keyword(s):  
Thermal Neutron ◽  
Large Scale ◽  
High Efficiency ◽  
Detection Efficiency ◽  
Rate Capability ◽  
High Rate ◽  
Thermal Neutrons ◽  
Neutron Detectors ◽  
Electron Multiplier ◽  
Detection Techniques

Due to the so-called 3He shortage crisis, many detection techniques for thermal neutrons are currently based on alternative converters. There are several possible ways of increasing the detection efficiency for thermal neutrons using the solid neutron-to-charge converters 10B or 10B4C. Here, we present an investigation of the Micromegas technology. The micro-pattern gaseous detector Micromegas was developed in the past years at Saclay and is now used in a wide variety of neutron experiments due to its combination of high accuracy, high rate capability, excellent timing properties, and robustness. A large high-efficiency Micromegas-based neutron detector is proposed for thermal neutron detection, containing several layers of 10B4C coatings that are mounted inside the gas volume. The principle and the fabrication of a single detector unit prototype with overall dimension of ~15 × 15 cm2 and its possibility to modify the number of 10B4C neutron converter layers are described. We also report results from measurements that are verified by simulations, demonstrating that typically five 10B4C layers of 1–2 μm thickness would lead to a detection efficiency of 20% for thermal neutrons and a spatial resolution of sub-mm. The high potential of this novel technique is given by the design being easily adapted to large sizes by constructing a mosaic of several such detector units, resulting in a large area coverage and high detection efficiencies. An alternative way of achieving this is to use a multi-layered Micromegas that is equipped with two-side 10B4C-coated gas electron multiplier (GEM)-type meshes, resulting in a robust and large surface detector. Another innovative and very promising concept for cost-effective, high-efficiency, large-scale neutron detectors is by stacking 10B4C-coated microbulk Micromegas. A prototype was designed and built, and the tests so far look very encouraging.

scholarly journals A European lightning density analysis using 5 years of ATDnet data

2014 ◽  
Vol 14 (4) ◽  
pp. 815-829 ◽  
Author(s):  
G. Anderson ◽  
D. Klugmann
Keyword(s):  
Detailed Analysis ◽  
Arrival Time ◽  
Detection Efficiency ◽  
Low Frequency ◽  
Continuous Data ◽  
Data Sets ◽  
Current Form ◽  
Very Low Frequency ◽  
Density Analysis ◽  
Using Data

Abstract. The Met Office has operated a very low frequency (VLF) lightning location network since 1987. The long-range capabilities of this network, referred to in its current form as ATDnet, allow for relatively continuous detection efficiency across Europe with only a limited number of sensors. The wide coverage and continuous data obtained by Arrival Time Differing NETwork (ATDnet) are here used to create data sets of lightning density across Europe. Results of annual and monthly detected lightning density using data from 2008–2012 are presented, along with more detailed analysis of statistics and features of interest. No adjustment has been made to the data for regional variations in detection efficiency.

scholarly journals Anomaly Detection Techniques in the Gaia Space Mission Data

2021 ◽  
Author(s):  
Marco Roberti ◽  
Alessandro Druetto ◽  
Deborah Busonero ◽  
Rossella Cancelliere ◽  
Davide Cavagnino ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Space Mission ◽  
Detection Techniques

scholarly journals Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6309
Author(s):  
Elena-Alexandra Budisteanu ◽  
Irina Georgiana Mocanu
Keyword(s):  
Unsupervised Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Data Augmentation ◽  
Learning Algorithms ◽  
Human Activity Recognition ◽  
Convolutional Networks ◽  
Supervised And Unsupervised Learning ◽  
Novel Approach ◽  
Unsupervised Deep Learning

Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned.

THE FOCAL SURFACE OF THE EUSO TELESCOPE

2005 ◽  
Vol 20 (29) ◽  
pp. 6890-6893 ◽  
Author(s):  
◽  
YOSHIYA KAWASAKI ◽  
M. BERTAINA ◽  
T. EBISUZAKI ◽  
F. KAJINO ◽  
...  
Keyword(s):  
Heat Dissipation ◽  
Detection Efficiency ◽  
Space Station ◽  
Wavelength Region ◽  
High Energy ◽  
Space Mission ◽  
Night Time ◽  
Focal Surface ◽  
Near Ultraviolet ◽  
Surface Detector

The Extreme Universe Space Observatory (EUSO) is a space mission to study extremely high-energy cosmic rays. The EUSO instrument is a wide-angle refractive telescope in near-ultraviolet wavelength region to observe time-resolved atmospheric fluorescence images of the extensive air showers from the International Space Station. The Focal surface is an aspherical curved surface, and its area amounts to about 4.5 m2. The focal surface detector is designed as a mosaic of multianode photomultipliers (MAPMT) for the single photoelectron counting capability. The strongest requirement for the focal surface detector is the maximization of the photon detection efficiency together with the uniformity over the focal surface. We have developed a new type of MAPMT. It is modified from the ordinary one and has a grid between the photocathode and the first dynode to electrostatically demagnify the photoelectron image on the dynode. We are also developing the HV supply system for a great number of MAPMTs. EUSO experiments the day-time and night-time every 90 minutes. The heat flow must be considered to stabilize the PMT characteristics, in parallel with the heat dissipation of the electronics attached on the focal surface supporting structure.

scholarly journals Automated SEM Image Analysis of the Sphere Diameter, Sphere-Sphere Separation, and Opening Size Distributions of Nanosphere Lithography Masks

2021 ◽  
pp. 1-11
Author(s):  
Thomas Riedl ◽  
Jörg K.N. Lindner
Keyword(s):  
Detection Efficiency ◽  
Size Variation ◽  
Cost Effective ◽  
Automated Analysis ◽  
Sphere Diameter ◽  
Size Distributions ◽  
Sem Images ◽  
Detection Techniques ◽  
Sem Image ◽  
Sphere Size

Abstract Colloidal nanosphere monolayers—used as a lithography mask for site-controlled material deposition or removal—offer the possibility of cost-effective patterning of large surface areas. In the present study, an automated analysis of scanning electron microscopy (SEM) images is described, which enables the recognition of the individual nanospheres in densely packed monolayers in order to perform a statistical quantification of the sphere size, mask opening size, and sphere-sphere separation distributions. Search algorithms based on Fourier transformation, cross-correlation, multiple-angle intensity profiling, and sphere edge point detection techniques allow for a sphere detection efficiency of at least 99.8%, even in the case of considerable sphere size variations. While the sphere positions and diameters are determined by fitting circles to the spheres edge points, the openings between sphere triples are detected by intensity thresholding. For the analyzed polystyrene sphere monolayers with sphere sizes between 220 and 600 nm and a diameter spread of around 3% coefficients of variation of 6.8–8.1% for the opening size are found. By correlating the mentioned size distributions, it is shown that, in this case, the dominant contribution to the opening size variation stems from nanometer-scale positional variations of the spheres.

scholarly journals Current and Future of Microlensing Exoplanet Search

2012 ◽  
Vol 8 (S293) ◽  
pp. 10-19 ◽  
Author(s):  
Takahiro Sumi
Keyword(s):  
Mass Function ◽  
Space Mission ◽  
Wide Field ◽  
Large Space ◽  
Detection Techniques ◽  
Snow Line ◽  
Continuous Survey ◽  
The Galaxy ◽  
High Cadence

AbstractGravitational microlensing has a unique sensitivity to exoplanets at outside of the snow-line with masses down to the Earth-mass. Because of the rarity and short timescale of the planetary signal, the survey groups, MOA-II in New Zealand and OGLE-IV in Chile carry out the wide field survey observation towards the galactic bulge to issue alerts in real time. Then telescopes of the follow-up groups conduct high cadence follow-up observation to get dense sampling of the short planetary signal. Recent high cadence survey observations by MOA-II and OGLE-IV have started to find exoplanets without follow-up observation systematically. This is a transition to the next generation 24-hour high cadence survey network which can reveal the mass function of exoplanets down to Earth-mass outside of the snow-line. The Wide Field Infrared Survey Telescope (WFIRST) is the highest ranked recommendation for a large space mission in the recent New Worlds, New Horizons (NWNH) in Astronomy and Astrophysics 2010 Decadal Survey. Exoplanet microlensing program is one of the primary science of WFIRST. WFIRST will find about 2,000 bound planets and 1,000 unbound planets by the high precision continuous survey with 15 min. cadence. WFIRST can complete the statistical census of planetary systems in the Galaxy, from the outer habitable zone to gravitationally unbound planets – a discovery space inaccessible to other exoplanet detection techniques.

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