Optical Satellite Multiobjective Genetic Algorithms for Investigation of MH370 Debris

2020 ◽  
pp. 166-207
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cloud Cover ◽  
Optical Remote Sensing ◽  
New Approach ◽  
Precise Method ◽  
Spectral Signatures ◽  
Remote Sensing Technique ◽  
Multiobjective Genetic Algorithms ◽  
Strength Constraints

At present, there is no precise method that can inform where the lost flight MH370 is. This chapter proposes a new approach to search for the missing flight MH370. To this end, multiobjective genetic algorithms are implemented. In this regard, a genetic algorithm is taken into consideration to optimize the MH370 debris that is notably based on the geometrical shapes and spectral signatures. Currently, there may be three limitations to optical remote sensing technique: (1) strength constraints of the spacecraft permit about two hours of scanning consistently within the day, (2) cloud cover prevents unique observations, and (3) moderate information from close to the ocean surface is sensed through the scanner. Needless to say that the objects that are spotted by different satellite data do not scientifically belong to the MH370 debris and could be just man-made without accurate identifications.

scholarly journals A new approach to characterize emission contributions from area sources during optical remote sensing technique testing

2012 ◽  
Vol 62 (12) ◽  
pp. 1403-1410 ◽  
Author(s):  
Tarek Abichou ◽  
Jeremy Clark ◽  
Jeffery Chanton ◽  
Gary Hater ◽  
Roger Green ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
New Approach ◽  
Remote Sensing Technique

Research of Genetic Algorithm for Parabolic Equation Inverse Problem

2011 ◽  
Vol 50-51 ◽  
pp. 428-431
Author(s):  
Ya Mian Peng ◽  
Guan Chen Zhou ◽  
Hui Juan Zhao
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Inverse Problem ◽  
Genetic Algorithms ◽  
Parabolic Equation ◽  
Optimization Problem ◽  
High Accuracy ◽  
Practical Application ◽  
New Approach ◽  
Ill Posed

It is difficult to solve the inverse problem because it always ill-posed. This paper introduced a new approach based on Genetic Algorithms (GA) for solve the parabolic equation inverse problem. The GA transforms the inverse problem into an optimization problem. The results of numerical simulation show that the method has high accuracy and quick convergent speed. And it is easy to program and calculate. It is worth of practical application.

scholarly journals Optimizing Ontology Alignments by Using Genetic Algorithms

2018 ◽  
Author(s):  
Jorge Martinez-Gil ◽  
José Francisco Aldana Montes ◽  
Enrique Alba ◽  
J. F. Aldana-Montes
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Key Words ◽  
Similarity Measures ◽  
False Positives ◽  
Ontology Alignment ◽  
New Approach ◽  
Edge Similarity ◽  
F Measure ◽  
Func Tion

In this work we present GOAL (Genetics for Ontology Align- ments) a new approach to compute the optimal ontology alignment func- tion for a given ontology input set. Although this problem could be solved by an exhaustive search when the number of similarity measures is low, our method,is expected to scale better for a high number,of measures. Our approach is a genetic algorithm which is able to work with several goals: maximizing the alignment precision, maximizing the alignment re- call, maximizing the f-measure or reducing the number of false positives. Moreover, we test it here by combining some cutting-edge similarity mea- sures over a standard benchmark, and the results obtained show several advantages in relation to other techniques. Key words: ontology alignment; genetic algorithms; semantic integra-

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

New approach to constrained shape optimization using genetic algorithms

AIAA Journal ◽  
1998 ◽  
Vol 36 ◽  
pp. 51-61 ◽  
Author(s):  
M. C. Sharatchandra ◽  
Mihir Sen ◽  
Mohamed Gad-el-Hak
Keyword(s):  
Genetic Algorithms ◽  
Shape Optimization ◽  
New Approach

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hirofumi Hashimoto ◽  
Weile Wang ◽  
Jennifer L. Dungan ◽  
Shuang Li ◽  
Andrew R. Michaelis ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Seasonal Patterns ◽  
Optical Remote Sensing ◽  
Amazon Forest ◽  
Evergreen Forests ◽  
Geostationary Satellites ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
New Generation

AbstractAssessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

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