Hepatit hastalığının teşhisi için çok katmanlı sinir ağı (MLNN) ve sigmoid aktivasyon fonksiyonu uygulanmıştır. Yöntemler: Yapay sinir ağları (YSA) tıbbi tanı için halen yaygın olarak kullanılan etkili araçlardır. Donanım tabanlı mimarilerde aktivasyon fonksiyonları YSA davranışında önemli rol oynamaktadır. Sigmoid fonksiyonu yumuşak tepkisi nedeniyle en sık kullanılan aktivasyon fonksiyonudur. Bu nedenle, sigmoid fonksiyonu ve yaklaşımları aktivasyon fonksiyonu olarak uygulanmıştır. Veri kümesi UCI makine öğrenme veri tabanından alınmıştır. Bulgular: Hepatit hastalığının tanısı için, MLNN yapısı hayata geçirilmiş ve Levenberg Morquardt (LM) algoritması öğrenme için kullanılmıştır. Hepatit hastalığını sınıflandıran yöntemimiz 10-kat çapraz doğrulama yoluyla 91.9%'den 93.8%'e doğruluklar sağlamıştır. Sonuç: Yapay sinir ağları ve aynı veri setini kullanarak hepatit hastalığını teşhis eden önceki çalışma ile karşılaştırıldığında, bizim sonuçlarımız sinir ağı tabanlı donanımın boyutunu ve maliyetini azaltması bakımından umut vericidir. Böylece, donanım tabanlı tanı sistemleri sigmoid fonksiyonu yaklaşımları kullanılarak etkili bir şekilde geliştirilebilir. Anahtar kelimeler: Hepatit hastalığı tanısı, çok katmanlı sinir ağı, 10-kat çapraz doğrulama, sigmoid aktivasyon fonksiyonu yaklaşımları ABSTRACT Objective: Implementation of multilayer neural network (MLNN) with sigmoid activation function for the diagnosis of hepatitis disease. Methods: Artificial neural networks (ANNs) are efficient tools currently in common use for medical diagnosis. In hardware based architectures activation functions play an important role in ANN behavior. Sigmoid function is the most frequently used activation function because of its smooth response. Thus, sigmoid function and its close approximations were implemented as activation function. The dataset is taken from the UCI machine learning database. Results: For the diagnosis of hepatitis disease, MLNN structure was implemented and Levenberg Morquardt (LM) algorithm was used for learning. Our method of classifying hepatitis disease produced an accuracy of 91.9% to 93.8% via 10 fold cross validation. Conclusion: When compared to previous work that diagnosed hepatitis disease using artificial neural networks and the identical data set, our results are promising in order to reduce the size and cost of neural network based hardware. Thus, hardware based diagnosis systems can be developed effectively by using approximations of sigmoid function.
No abstract
Geostationary satellites are objects, which revolve around the Earth where orbits are nearly circular and located on the equator plane with a period exactly equal to the rotation of the Earth. Most of such satellites are used for civil and military communication, television broadcasting and weather forecasting. Gravitational forces of the Sun, Moon and non-uniform mass distribution of the Earth perturb the geostationary orbits. Including these gravitational anomalies pressure of Solar winds is another source of perturbation. Because of these perturbations, orbits of geostationary satellites disturbed and some correction maneuvers must be performed. ITU radio regulation requires geostationary satellites have capability of maintaining their positions within ±0.1° of the longitude of their nominal positions [2]. Multiple numbers of colocated geostationary satellites can be operated within ±0.1° box with careful orbit determination and maneuver strategies. Most orbit determinations of the geostationary satellites are performed by tone ranging; measuring phase difference of RF signals sent to satellite and received from the satellite. Angular parameters of the satellites are obtained by azimuth and elevation of the control station antennas, which are following beacon signal of the satellite. Because of all geostationary satellites must be located on geostationary orbit, collision risk always exists. Telescope observation of geostationary satellites provides us to complimentary information to tone ranging systems, which can be used for correlation and calibration purposes.In this study, inter-satellite distances of co-located Turksat-2A and Turksat-3A satellites measured by telescope observations. This optical observation performed in 2011 at the Ankara University Observatory (AUO) using 20 cm (8-inch) optical telescope and with a CCD type detector. The inter-satellite distances are calculated by using the observed angular measurements between Turksat-2A and Turksat-3A and the radial distance measured with tone ranging. Results are compared with tone ranging orbit measurements performed by Turksat Satellite Control Center.
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