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.
This article proposes a technique for using fuzzy interval logic during multi-stage processing of state spaces of complex biophysical objects in the medical system HEALTH. The proposed method of selecting the best diagnoses in the context of fuzzy background information was used to solve the practical problems of computer medical diagnostics. The theoretical and practical results formulated are the basis for the construction of systems and tools for computational intelligence. The diagnostic system is implemented on the basis of modern information technologies and computers. The presented computerized medical diagnostics system can be used in medical practice for the diagnosis of cardiovascular diseases, planning of health events and conducting of social and hygienic monitoring in medical institutions. Laboratory operation of fuzzy intellectual system HEALTH confirmed the high probability of making objective decisions in the medical subject area.
On Soft Computing Techniques in Various Areas
Computer Science & Information Technology ( CS & IT ), 2013
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition, recognition, understanding, learning, and others into the fields of computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Lecture Notes in Electrical Engineering, 2018
The satellite image is an assortment of the massive quantity of information for agriculture, environmental assessment and monitoring, mapping, military, and future planning of maintaining the natural resources and disasters. So it contains more useful and necessary information for analysis and processing. High resolution, low-cost, and easy availability of satellite images are the reasons for the fast growth in the usage of satellite images to extract the necessary information. For this aspect, various approaches have been proposed. Both soft and non-soft computing methods have been applied on satellite images to obtain meaningful clusters. Even though many kinds of literature are available for non-soft computing methods, only a limited number of authors have proposed soft computing based segmentation of satellite images. This work proposed a novel technique for the segmentation of RGB and HSI color space transformed satellite images using soft computing techniques.
Soft computing applications in aircraft sensor management and flight control law reconfiguration
IEEE Transactions on Systems, Man, and Cybernetics, 2002
A sensor management system based on soft computing techniques has been developed and implemented in the flight control system of a small commercial aircraft. Unlike in the conventional sensor management system, the signals from sensors are assigned weights based on fuzzy membership functions and the consolidated signal is computed as a weighted average. This approach improves the quality of the consolidated signal and reduces transients due to sensor failures. This soft voting is extended to soft flight control law reconfiguration. In addition, a virtual sensor has been introduced as an arbitrator which enables the isolation of the failed sensor in the duplex operation and the detection of a sensor failure in the simplex operation. The effectiveness of the proposed methods is demonstrated by using an extensive simulation model of a small commercial aircraft, developed by airframe and control system manufacturers on the basis of an existing business jet. Furthermore, the system has been successfully evaluated and compared to standard techniques by means of pilot-in-the-loop simulations on the Research Flight Simulator of the National Aerospace Laboratory in The Netherlands. This application, developed within a Brite/EuRam research project, is characterized by the effective combination of novel soft computing techniques with standard, well proven methods of the aircraft industry. The properties of the conventional sensor management system have been retained, with the additional advantage that the quality of the consolidated signal is improved, the failure-induced transients are reduced, and the consolidated signal remains available up to the last valid sensor.
Soft computing based on interval valued fuzzy ANP-A novel methodology
Journal of Intelligent Manufacturing, 2012
Analytic Network Process (ANP) is the multicriteria decision making (MCDM) tool which takes into account such a complex relationship among parameters. In this paper, we develop the interval-valued fuzzy ANP (IVF-ANP) to solve MCDM problems since it allows interdependent influences specified in the model and generalizes on the supermatrix approach. Furthermore, performance rating values as well as the weights of criteria are linguistics terms which can be expressed in IVF numbers (IVFN). Moreover, we present a novel methodology proposed for solving MCDM problems. In proposed methodology by applying IVF-ANP method determined weights of criteria. Then, we appraise the performance of alternatives against criteria via linguistic variables which are expressed as triangular interval-valued fuzzy numbers. Afterward, by utilizing IVFweights which are obtained from IVF-ANP and applying IVF-TOPSIS and IVF-VIKOR methods achieve final rank for alternatives. Additionally, to demonstrate the procedural implementation of the proposed model and its effectiveness, we apply it on a case study regarding to assessment the performance of property responsibility insurance companies. Keywords Analytic Network Process (ANP) • Multicriteria decision making (MCDM) • Interval-valued fuzzy set (IVFS) • IVF-ANP • IVF-TOPSIS • IVF-VIKOR