Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 1Three-Dimensional Imaging in Degraded Visual Field(IOP Publishing Ltd, 2016-04) Oran, A.; Ozharar, S.; Ozdur, I.Imaging at degraded visual environments is one of the biggest challenges in today's imaging technologies. Especially military and commercial rotary wing aviation is suffering from impaired visual field in sandy, dusty, marine and snowy environments. For example during landing the rotor churns up the particles and creates dense clouds of highly scattering medium, which limits the vision of the pilot and may result in an uncontrolled landing. The vision in such environments is limited because of the high ratio of scattered photons over the ballistic photons which have the image information. We propose to use optical spatial filtering (OSF) method in order to eliminate the scattered photons and only collect the ballistic photons at the receiver. OSF is widely used in microscopy, to the best of our knowledge this will be the first application of OSF for macroscopic imaging. Our experimental results show that most of the scattered photons are eliminated using the spatial filtering in a highly scattering impaired visual field. The results are compared with a standard broad area photo detector which shows the effectiveness of spatial filtering.Conference Object Citation - WoS: 1Estimation of Cohesion for Intact Rock Materials Using Regression and Soft Computing Analyses(IOP Publishing Ltd, 2024-01-01) Koken, E.; Strzalkowski, P.; Kazmierczak, U.; Strzałkowski, P.Shear strength parameters such as cohesion (c) and internal friction angle (phi) are among the most critical rock properties used in the geotechnical design of most engineering projects. However, the determination of these properties is laboring and requires special equipment. Therefore, this study introduces several predictive models based on regression and artificial intelligence methods to estimate the c of different rock types. For this purpose, a comprehensive literature survey is carried out to collect quantitative data on the shear strength properties of different rock types. Then, regression and soft computing analyses are performed to establish several predictive models based on the collected data. As a result of these analyses, five different predictive models (M1-M5) were established. Based on the performance of the established predictive models, the artificial neural network-based predictive model (model 5, M5) was the most suitable choice for evaluating the c for different rock types. In addition, mathematical expressions behind the M5 model are also presented in this study to allow users to implement it more efficiently. In this regard, the present study can be declared a case study showing the applicability of regression and soft computing analyses to evaluate the c of different rock types. However, the number of datasets used in this study should be increased to get more comprehensive predictive models in future studies.