scholarly journals A Deep Learning Method to Accelerate the Disaster Response Process

2020 ◽  
Vol 12 (3) ◽  
pp. 544 ◽  
Author(s):  
Vyron Antoniou ◽  
Chryssy Potsiou
Keyword(s):  
Deep Learning ◽  
Disaster Response ◽  
Human Factor ◽  
Landing Site ◽  
Response Process ◽  
Core Element ◽  
Site Analysis ◽  
Landing Sites ◽  
Helicopter Landing ◽  
Soccer Fields

This paper presents an end-to-end methodology that can be used in the disaster response process. The core element of the proposed method is a deep learning process which enables a helicopter landing site analysis through the identification of soccer fields. The method trains a deep learning autoencoder with the help of volunteered geographic information and satellite images. The process is mostly automated, it was developed to be applied in a time- and resource-constrained environment and keeps the human factor in the loop in order to control the final decisions. We show that through this process the cognitive load (CL) for an expert image analyst will be reduced by 70%, while the process will successfully identify 85.6% of the potential landing sites. We conclude that the suggested methodology can be used as part of a disaster response process.

scholarly journals Helicopter Landing Sites Identification depending on Slope, Landing Site Dimension and Shape

2021 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Eva Mertova ◽  
Martin Bures
Keyword(s):  
Complex Analysis ◽  
Landing Site ◽  
Landing Sites ◽  
Helicopter Landing

Abstract. The identification of the Helicopter Landing Sites (HLS) needs complex analysis of the terrain considering a lot of aspects. One of the unconditional aspects in this case is the slope of ground, therefore the HLS identification depending on slope, landing site dimension and shape was conducted. This paper describes the development of the tool for the HLS identification depending only on the relief, but no other objects on the earth’s surface. At the end of the paper, the possible improvements of the tools are stated.

scholarly journals Automated Discontinuity Detection and Reconstruction in Subsurface Environment of Mars Using Deep Learning: A Case Study of SHARAD Observation

2020 ◽  
Vol 10 (7) ◽  
pp. 2279
Author(s):  
Vanshika Gupta ◽  
Sharad Kumar Gupta ◽  
Jungrack Kim
Keyword(s):  
Deep Learning ◽  
Landing Site ◽  
Planetary Science ◽  
Detection Scheme ◽  
Shallow Subsurface ◽  
Discontinuity Detection ◽  
Database Construction ◽  
Subsurface Environment ◽  
Radar Echo ◽  
Subsurface Mapping

Machine learning (ML) algorithmic developments and improvements in Earth and planetary science are expected to bring enormous benefits for areas such as geospatial database construction, automated geological feature reconstruction, and surface dating. In this study, we aim to develop a deep learning (DL) approach to reconstruct the subsurface discontinuities in the subsurface environment of Mars employing the echoes of the Shallow Subsurface Radar (SHARAD), a sounding radar equipped on the Mars Reconnaissance Orbiter (MRO). Although SHARAD has produced highly valuable information about the Martian subsurface, the interpretation of the radar echo of SHARAD is a challenging task considering the vast stocks of datasets and the noisy signal. Therefore, we introduced a 3D subsurface mapping strategy consisting of radar echo pre-processors and a DL algorithm to automatically detect subsurface discontinuities. The developed components the of DL algorithm were synthesized into a subsurface mapping scheme and applied over a few target areas such as mid-latitude lobate debris aprons (LDAs), polar deposits and shallow icy bodies around the Phoenix landing site. The outcomes of the subsurface discontinuity detection scheme were rigorously validated by computing several quality metrics such as accuracy, recall, Jaccard index, etc. In the context of undergoing development and its output, we expect to automatically trace the shapes of Martian subsurface icy structures with further improvements in the DL algorithm.

scholarly journals Method for Remote State Identification Snow-Ice Cover by the Ratio of Fresnel Reflection Coefficients

2020 ◽  
Vol 23 (5) ◽  
pp. 46-56
Author(s):  
V. G. Mashkov
Keyword(s):  
Landing Site ◽  
Ice Cover ◽  
The State ◽  
Reflection Coefficients ◽  
Fresnel Reflection ◽  
State Identification ◽  
Key Factor ◽  
Helicopter Landing ◽  
Standard Values ◽  
Remote Identification

Introduction. Currently, the development of safe helicopter landing systems as the most complex and dangerous stage of a flight is one of the priority tasks. A significant number of companies in Russia and abroad are engaged in its solution. Landing on unprepared (unequipped) sites with snow-ice cover may be caused by the need to deliver units, cargo and ammunition in combat conditions, search and rescue operations, evacuations of victims, etc. A key factor for a landing decision is information about the height of snow and about the depth of ice cover. In the paper remote identification of the state of snow-ice cover, excluding the need to present any person (crew member or rescue worker) on a landing site is proposed.Aim. To develop a method for the remote identification of the state of snow-ice cover used to determine the possibility of a helicopter - type aircraft safe landing on a reservoir with snow-ice cover.Materials and methods. Numerical simulation of echo signals Fresnel reflection coefficients polarization ratio was realized in MatLab. Vertical and horizontal polarizations in the range from 25 to 45 degrees were simulated.Results. Intervals of polarization relations correspond to the interval density of snow-ice layers for fixed angles. For example, when θ = 34 for dry snow ρds = 100…500 kg/m3 (ε'ds = 1.162…1.984) – Prm = 5.6915...3.3266, dry firn ρdf = 500…700 kg/m3 (ε'df = 1.984…2.51) – Prm = 3.3266...2.8311, dry ice ρdi = 700…913 kg/m3 (ε'di = 2.51…3.179) – Prm = 2.8311...2.4753. A layer reconstruction inverse problem was solved by indirect determining of complex relative permittivity of each successive underlying layer with 10-2 real part resolution. The identity of the obtained characteristics of snow-ice layers with calculated (standard) values was established.Conclusion. Remote identification of components of a snow-ice cover structure allows one to automate the process of evaluating of landing possibility. Thereby it reduces a decision-making time and increases a level of safety. In contrast to the known methods of identification of the surface layer the identification of multilayer medium layers was carried out.

scholarly journals Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Nilani Algiriyage ◽  
Raj Prasanna ◽  
Kristin Stock ◽  
Emma E. H. Doyle ◽  
David Johnston
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Disaster Response ◽  
Multimodal Data

scholarly journals Unveiling the molecular mechanism of SARS-CoV-2 main protease inhibition from 137 crystal structures using algebraic topology and deep learning

2020 ◽  
Vol 11 (44) ◽  
pp. 12036-12046
Author(s):  
Duc Duy Nguyen ◽  
Kaifu Gao ◽  
Jiahui Chen ◽  
Rui Wang ◽  
Guo-Wei Wei
Keyword(s):  
Deep Learning ◽  
Binding Site ◽  
Crystal Structures ◽  
Protease Inhibitors ◽  
Molecular Mechanism ◽  
Algebraic Topology ◽  
Binding Affinities ◽  
Protease Inhibition ◽  
Site Analysis ◽  
Main Protease

By integrating algebraic topology and deep learning, we provide a reliable ranking of binding affinities, binding site analysis, and fragment decomposition for 137 SARS-CoV-2 main protease inhibitors.

scholarly journals Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens

2020 ◽  
Vol 10 (17) ◽  
pp. 6116
Author(s):  
KyungHyun Han ◽  
Seong Oun Hwang
Keyword(s):  
Detection Method ◽  
Detection System ◽  
Landing Site ◽  
Rapid Identification ◽  
Inverse Document Frequency ◽  
Abstract Syntax Tree ◽  
Detection Systems ◽  
Document Frequency ◽  
Proposed Model ◽  
Landing Sites

Attackers use a variety of techniques to insert redirection JavaScript that leads a user to a malicious webpage, where a drive-by-download attack is executed. In particular, the redirection JavaScript in the landing site is obfuscated to avoid detection systems. In this paper, we propose a lightweight detection system based on static analysis to classify the obfuscation type and to promptly detect the obfuscated redirection JavaScript. The proposed model detects the obfuscated redirection JavaScript by converting the JavaScript into an abstract syntax tree (AST). Then, the structure and token information are extracted. Specifically, we propose a lightweight AST to identify the obfuscation type and the revised term frequency-inverse document frequency to efficiently detect the malicious redirection JavaScript. This approach enables rapid identification of the obfuscated redirection JavaScript and proactive blocking of the webpages that are used in drive-by-download attacks.

Helicopter Landing Ship: Undercarriage Strength and the Role of the Human Factor

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
E. Suhir
Keyword(s):  
Human Factor ◽  
Operation Time ◽  
Extreme Value Distribution ◽  
Mean Value ◽  
Helicopter Landing ◽  
The Times ◽  
The Moment ◽  
Normal Law ◽  
Time Required

We address, using probabilistic modeling and the extreme-value-distribution technique, the helicopter undercarriage strength in a helicopter-landing-ship situation. Our analysis contains an attempt to quantify, on the probabilistic basis, the role of the human factor in the situation in question. This factor is important from the standpoint of the operation time that affects the likelihood of safe landing during the lull period in the sea condition. The operation time includes (1) the time required for the officer-on-ship-board and the helicopter pilot to make their go-ahead decisions and (2) the time of actual landing. It is assumed, for the sake of simplicity, that both these times could be approximated by Rayleigh’s law, while the lull duration follows the normal law with a high enough ratio of the mean value to the standard deviation. Safe landing could be expected if the probability that it occurs during the lull time is sufficiently high. The probability that the helicopter undercarriage strength is not compromised can be evaluated as a product of the probability that landing indeed occurs during the lull time and the probability that the relative velocity of the helicopter undercarriage with respect to the ship’s deck at the moment of encounter does not exceed the allowable level. This level is supposed to be determined for the helicopter-landing-ground situation. The developed model can be used when developing specifications for the undercarriage strength, as well as guidelines for personnel training. Particularly, the model can be of help when establishing the times to be met by the two humans involved to make their go-ahead decisions for landing and to actually land the helicopter. Plenty of additional risk analyses (associated with the need to quantify various underlying uncertainties) and human psychology related efforts will be needed, of course, to make such guidelines practical.

Venus Landing Site Analysis

2016 ◽  
Author(s):  
Jason Rabinovitch ◽  
Kathryn Stack ◽  
Richard Otero ◽  
Gary M. Ortiz ◽  
Mark A. Bullock
Keyword(s):  
Landing Site ◽  
Site Analysis
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