Aerospace Applications Of Soft Computing And Interval Computations (with An Emphasis On Multi-Spectral Satellite Imaging) (original) (raw)

Soft Computing: Frontiers? A Case Study of Hyper-Spectral Satellite Imaging

Soft computing methods such as fuzzy control, neural networks, etc., often require lots of computations even for small amounts of data. It is, therefore, sometimes believed that for larger amounts of data, the required amount of computations will be so large that we will reach the frontiers of soft computing. In this paper, we show, on the example of hyperspectral satellite imaging, that this belief is often too pessimistic. We should not be afraid to use (or at least to try to use) soft computing methods even for large amounts of data.

Computational intelligence and soft computing for space applications

IEEE Aerospace and Electronic Systems Magazine, 1996

Systems using computational intelligence and soft computing have been successfully developed for many industrial and space applications. These systems seek to emulate the type of reasoning that humans perform when solving complex tasks. the inventor of fuzzy logicencompasses fuzzy logic as well as other methodologies such as neural networks, genetic algorithms, and uncertainty management. It is expected that soft-computing techniques will eventually become as common and prevalent as traditional methods of computer science. This paper presents an overview of applications of fuzzy logic and soft computing to space projects. The role of fuzzy systems that can learn from experience to improve their performance is discussed. We present a report on applications of these adaptive systems to NASA space projects such as the orbital operations of the Space Shuttle, which include attitude control and The field of soft computing, as defined by Zadeh

A soft computing approach for obtaining transition regions in satellite images

… Computing Theories and …, 2010

Most of the current satellite image classification methods consider rough boundaries among homogeneous regions. However; real images contain transition regions where pixels belong, at different degrees, to different classes. With this motivation, in this paper we propose a satellite image classification method that allows the identification of transition regions among homogeneous regions. Our solution is based on Soft Computing because of its ability to handle the uncertainties present in nature. We present our method as well as preliminary results that show how our method is able to solve real world problems.

Applied Soft Computing

Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types. Applied Soft Computing, 109, 107531.

A Soft Computing Approach to Aerial Image Restoration

An aerial image can be corrupted because of various reasons. Once the image has been acquired, the image can't be discarded because of various reasons even though the image is corrupted due to noise, camera misalignment etc. Various algorithms in the field of conventional restoration do exist but the efficiency with which they restore the image is less. A soft computing approach to this aerial image restoration is presented in this paper with stress on neural networks and fuzzy logic to improve the restoration algorithm and comparisons with the conventional algorithms.

Interval Computations: Introduction, Uses, and Resources

1996

Interval analysis is a broad Þeld in which rigorous mathematics is associated with with scientiÞc computing. A number,of researchers worldwide have produced a voluminous literature on the subject. This article introduces interval arithmetic and its interaction with established mathematical theory. The article provides pointers to traditional literature collections, as well as electronic resources. Some successful scientiÞc and engineering applications

Interval-Related Talks at the North American Fuzzy Information Processing Society Annual Conference NAFIPS’07

Reliable Computing, 2007

Latest scientific and engineering advances have started to recognize the need of defining multiple types of uncertainty. Probabilistic modeling cannot handle situations with incomplete or little information on which to evaluate a probability, or when that information is nonspecific, ambiguous, or conflicting [46, 11, 43]. Many interval-based models of uncertainty have been developed to treat such situations. This paper presents an interval approach for the treatment of parameter uncertainty for linear static problems of mechanics. Uncertain parameters are introduced in the form of unknown but bounded quantities (intervals). Interval analysis is applied to Finite Element Method to analyze the system response due to uncertain stiffness and loading. To avoid overestimation, the formulation is based on an element-by-element (EBE) technique. Element matrices are formulated, based on the physics of materials, and the Lagrange multiplier method is applied to impose the necessary constraints for compatibility and equilibrium. Earlier EBE formulation provided sharp bounds only on displacements [29]. Based on the developed formulation, the bounds on the system's displacement and forces are obtained simultaneously and have the same level of accuracy. Very sharp enclosures for the exact system responses are obtained. A number of numerical examples are introduced and scalability is illustrated.

Soft computing in multidisciplinary aerospace design—new directions for research

Progress in Aerospace Sciences, 2002

This paper is part of the following report: TITLE: Aerodynamic Design and Optimisation of Flight Vehicles in a Concurrent MultiDisciplinary Environment [la Conception et l'optimisation aerodynamiques des vehicules eriens dans un environnement pluridisciplinaire et simultane] To order the complete compilation report, use: ADA388284 The component part is provided here to allow users access to individually authored sections f proceedings, annals, symposia, ect. However, the component should be considered within he context of the overall compilation report and not as a stand-alone technical report.