WoS İndeksli Yayınlar Koleksiyonu

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

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Language: search.filters.language.trSubject: search.filters.subject.Deep Learning

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  • 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.
  • Conference Object
    Citation - WoS: 9
    Citation - Scopus: 16
    Perlin Random Erasing for Data Augmentation
    (Ieee, 2021) Saran, Ayse Nurdan; Saran, Murat; Nar, Fatih
    In the last decade, Deep Learning is applied in a wide range of problems with tremendous success. Large data, increased computational resources, and theoretical improvements are main reasons for this success. As the dataset grows, the real-world is better represented, allows developing a model that can generalize. However, creating a labeled dataset is expensive, time-consuming, or sometimes even challenging. Therefore, researchers proposed data augmentation methods to increase dataset size by creating variations of the existing data. This study proposes an extension to Random Erasing data augmentation method by introducing smoothness. The proposed method provides better performance compared to Random Erasing data augmentation method, which is shown using a transfer learning scenario on the UC Merced Land-use image dataset.
  • Conference Object
    An Analysis on the Effect of Skip Connections in Fully Convolutional Networks for License Plate Localization
    (Institute of Electrical and Electronics Engineers Inc., 2019) Uzun, E.; Akagunduz, E.
    In this study, the effect of the skip connections, which are seen in fully convolutional networks, on object localization is analyzed. For this purpose, a local data set for plate detection is created. Experiments are carried out using this data set. Due to the small size of the image set, data augmentation method is used to overcome the danger of over-fitting. The learning rates of the first layers are frozen for analysis and finetuning is applied to only the last layer and deconvolution layers. The results obtained are compared with the results of other image sets. The results indicate the importance of the information provided by the skip connections on object localization. © 2019 IEEE.