WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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Publisher: search.filters.publisher.Gazi Univ, Fac Engineering ArchitectureSubject: search.filters.subject.Bilgisayar Bilimleri, Yazılım Mühendisliği

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Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 12
    Citation - Scopus: 19
    Artificial Intelligence Applications in Earthquake Resistant Architectural Design: Determination of Irregular Structural Systems With Deep Learning and Imageai Method
    (Gazi Univ, Fac Engineering Architecture, 2020) Bingol, Kaan; Akan, Asli Er; Ormecioglu, Hilal Tugba; Er, Arzu
    Although the architectural design process is carried out with the collaboration of experts who are experienced in many different areas from the main preferences to the detailing stage, the major decisions such as plan organization, mass design etc. are taken by the architect. Computer Aided Design (CAD) programs are generally effective after the major decisions of the design are taken. For this reason, it is common for the main decisions, taken during the design process, to be changed during the analysis of the structural system. In order to prevent this, in the early stages of architectural design, earthquake system awareness and structural system design should be included as an design input; as, the failure of the structural system which did not considered well in the architectural design phase leads to unexpected revisions in the implementation project phase and thus leads to serious losses in both time and cost. The aim of this study is to create an Irregularity Control Assistant (IC Assitant) that can provide architects general information about the appropriateness of structural system decisions to earthquake regulations in the early stages of design process by using the deep learning and image processing methods. In this way, correct decisions will be made in the early stages of the design and unexpected revisions that may occur during the implementation project phase will be prevented.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 2
    Classification of Low Probability of Intercept Radar Waveforms Using Gabor Wavelets
    (Gazi Univ, Fac Engineering Architecture, 2021) Ergezer, Halit
    Low Probability of Intercept (LPI Radar) is a class of radar with specific technical characteristics that make it very difficult to intercept with electronic support systems and radar warning receivers. Because of their properties as low power, variable frequency, wide bandwidth, LPI radar waveforms are difficult to intercept by ESM systems. In recent years, studies on the classification of waveforms used by these types of radar have been accelerated. In this study, Time-Frequency Images (TFI) has been obtained from the LPI radars waveforms by using Choi-Williams Distribution method. From these images, feature vectors have been generated using Gabor Wavelet transform. In contrast to many methods in the literature, waveform classification has been performed by directly comparing the feature vectors obtained without using any machine learning method. With the method we propose, classification accuracies were obtained at intervals of 2 dB between -20 dB and 10 dB and performed at reasonable classification accuracy rates up to -8 dB SNR value. Better results than the best reported in the literature were obtained for some signal types. The results obtained for all waveform types are given in comparison with the results of the existing methods in the literature.