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- MakaleYalnız künye2019Scopus SJRQ1Deep long short-term memory networks-based automatic recognition of six different digital modulation types under varying noise conditions
Daldal, Nihat; Yıldırım, Özal; Polat, Kemal
In this paper, a new method based on deep learning has been proposed in order to recognize noise-digital modulation signals at varying noise levels automatically. The 8-bit data from six different modulations have been obtained by adding noise levels from 5 to 25 dB. The used digital modulation types are Amplitude Shift Keying, Frequency Shift Keying, Phase Shift Keying, Quadrature Amplitude Shift Keying, Quadrature Frequency Shift Keying, and Quadrature Phase Shift Keying. To recognize the noise-digital modulation signals automatically, a new deep long short-term memory networks (LSTMs) model has been proposed and then applied to these signals successfully. A significant advantage of the proposed system is that deep learning method has been trained and tested with raw digital modulation signals without applying any feature extraction from the signals. In this study, the noise modulation signals of 5–25 dB have been classified and compared with each other. The innovative aspect of the study is to classify the modulation with the LSTM method without dealing with the extraction of signal characteristics. Without noise, added digital modulation signals had been classified as the success rate of 97.22%, while with all noise-added signals have been classified as the success rate of 94.72% with deep LSTM model. The experimental results show that the proposed deep LSTM model has been achieved remarkable results in recognition of noised six different modulation signals with a fully end-to-end structure.
AtıfScopus35WoS26OpenAlex35
İncele - MakaleYalnız künye2019Scopus SJRQ2The effect of hygrothermal aging and UV radiation on the low-velocity impact behavior of the glass fiber-reinforced epoxy composites
Dogan, Akar; Arman, Yusuf
The present work examines the effects of hygrothermal and UV radiation aging on the reinforced epoxy composites. The role of temperature in the hygrothermal aging process on tensile properties, impact behavior of composites and compression-after impact was investigated. Among four different chosen aging conditions, three of them were hygrothermal and last one was composed of both hygrothermal and UV radiation. Unidirectional e-glass fiber fabrics with an areal density 300 g/m2 as reinforcement and epoxy matrix were used. Composites were manufactured using vacuum-assisted resin infusion molding. The goal of this study was to investigate the effect of UV radiation and aging temperature with respect to the glass transition temperature. In this context, aging procedures were carried out in two different ways for 70 °C temperature with and without UV radiation. As a result of the test, the tensile strength, tensile modulus, absorbed energy, interlaminar shear strength (ILSS), and compression-after impact (CAI) strength of the glass reinforced composites decreased for all conditions. Despite being under the same ambient temperature and humidity, the samples which were exposed to UV radiation had lower mechanical properties. The reduction of tensile strength of specimens exposed to 70 °C and 70% humidity with and without UV radiation were 24% and 10%, respectively. The temperature of hygrothermal conditions significantly affected the perforation threshold. In low impact energies, delamination was the primary damage mode, while fiber breakage becomes more important in the composite at high impact energy levels.
AtıfScopus48WoS43OpenAlex48İncele