Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Automated building energy modeling for existing buildings using computer vision
Download
index.pdf
Date
2019-09-20
Author
Gürsel Dino, Ipek
Sarı, Alp Eren
Kalfaoğlu, Esat
Akın, Şahin
Işeri, Orçun Koral
Kalkan, Sinan
Alatan, Abdullah Aydin
Erdoğan, Bilge
Metadata
Show full item record
Item Usage Stats
136
views
89
downloads
Cite This
URI
https://hdl.handle.net/11511/85094
Conference Name
CIB W78: Conference: Advances in ICT in Design, Construction and Management in Architecture, Engineering, Construction and Operations (AECO), (18 - 20 Eylül 2019)
Collections
Department of Architecture, Conference / Seminar
Suggestions
OpenMETU
Core
Automated biological data acquisition and integration using machine learning techniques
Çarkacıoğlu, Levent; Atalay, Mehmet Volkan; Department of Computer Engineering (2009)
Since the initial genome sequencing projects along with the recent advances on technology, molecular biology and large scale transcriptome analysis result in data accumulation at a large scale. These data have been provided in different platforms and come from different laboratories therefore, there is a need for compilation and comprehensive analysis. In this thesis, we addressed the automatization of biological data acquisition and integration from these non-uniform data using machine learning techniques....
Automated crowd behavior analysis for video surveillance applications
Güler, Püren; Temizel, Alptekin; Taşkaya Temizel, Tuğba; Department of Information Systems (2012)
Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groups; people counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behavi...
Automated Game Mechanics and Aesthetics Generation Using Neural Style Transfer in 2D Video Games
Şen, Deniz; Küçükkaykı, Hasan Tahsin; Sürer, Elif (2021-07-01)
Automated Sizing of Truss Structures Using a Computationally Improved SOPT Algorithm
Hasançebi, Oğuzhan (2013-06-01)
The present study attempts to apply an efficient yet simple optimization (SOPT) algorithm to optimum design of truss structures under stress and displacement constraints. The computational efficiency of the technique is improved through avoiding unnecessary analyses during the course of optimization using the so-called upper bound strategy (UBS). The efficiency of the UBS integrated SOPT algorithm is evaluated through benchmark sizing optimization problems of truss structures and the numerical results are r...
Automated design analysis of anti-roll bars
Çalışkan, Kemal; Ünlüsoy, Yavuz Samim; Dağ, Serkan; Department of Mechanical Engineering (2003)
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
I. Gürsel Dino et al., “Automated building energy modeling for existing buildings using computer vision,” presented at the CIB W78: Conference: Advances in ICT in Design, Construction and Management in Architecture, Engineering, Construction and Operations (AECO), (18 - 20 Eylül 2019), Newcastle-Upon-Tyne, İngiltere, 2019, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/85094.