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automatic-identification
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Automatic Identification of Hazard Factors in Project Planning Phase Using Building Information Modeling (BIM)
, M.Sc. Thesis Sharif University of Technology ; Alvanchi, Amin (Supervisor) ; Karimi, Hossein (Supervisor)
Abstract
The construction is classified as a high-risk industry. The number of casualties and fatalities in this industry is disproportionately high. The ability to recognize hazards in the construction process and the design stage is still in its early stages of development. The most important way to control and eliminate hazards and accidents is to analyze these hazards before they occur and plan to deal with them before starting project activities. Identifying safety risks is the first step in evaluating and analyzing these risks. Building Information Modeling (BIM) technology, due to the consideration of the relationship between the elements in the model and because it contains an object-oriented...
Monitoring Risks of Tower Crane Operations Using Computer Vision and Deep Learning Techniques
, M.Sc. Thesis Sharif University of Technology ; Alvanchi, Amin (Supervisor)
Abstract
The utilization of tower cranes at construction sites presents numerous inherent risks. These cranes are commonly employed for lifting heavy loads, which carry the potential hazard of accidental falls. Simultaneously, workers may inadvertently overlook overhead dangers while focusing on their tasks. To mitigate these risks, laws in many countries explicitly prohibit individuals from occupying the vicinity directly beneath suspended loads, known as the fall zone. Such measures are vital to safeguard against the peril of heavy loads plummeting onto people. However, existing studies have not offered a comprehensive and efficient approach to identify crane load fall zone. To address this gap,...
Machine-Vision Based Visual Inspection of Masonry Structures Using Optical Measurements on Still Images
, M.Sc. Thesis Sharif University of Technology ; Rahimzadeh Rofooei, Fayyaz (Supervisor) ; Mahdavi, Hossein (Co-Supervisor)
Abstract
Cracks are the earliest signs of damage in structures which are needed to get detected through structural inspection before causing failure. Regular inspection and post-disaster inspection of structures are usually conducted manually by engineers. These kinds of visual inspections could be expensive, subjective, not reliable, and most importantly inefficient. Computer vision developments help automate this process by analyzing digital images captured from the surface of structures. Despite the fact that masonry structures have more complex surfaces than other kinds of structures, limited studies have focused on presenting machine-vision-based techniques for crack detection in those...
Automatic identification of overlapping/touching chromosomes in microscopic images using morphological operators
, Article 2011 7th Iranian Conference on Machine Vision and Image Processing, MVIP 2011 - Proceedings, 16 November 2011 through 17 November 2011 ; November , 2011 , Page(s): 1 - 4 ; 9781457715358 (ISBN) ; Setarehdan, S. K ; Fatemizadeh, E ; Sharif University of Technology
2011
Abstract
Karyotyping, is the process of classification of human chromosomes within the microscopic images. This is a common task for diagnosing many genetic disorders and abnormalities. Automatic Karyotyping algorithms usually suffer the poor quality of the images due to the non rigid nature of the chromosomes which makes them to have unpredictable shapes and sizes in various images. One of the main problems that usually need operator's interaction is the identification and separation of the overlapping/touching chromosomes. This paper presents an effective algorithm for identification of any cluster of the overlapping/touching chromosomes together with the number of chromosomes in the cluster, which...
Fast and scalable system for automatic artist identification
, Article IEEE Transactions on Consumer Electronics ; Volume 55, Issue 3 , 2009 , Pages 1731-1737 ; 00983063 (ISSN) ; Abolhassani, H ; Shirali Shahreza, M. H ; Sharif University of Technology
2009
Abstract
Digital music technologies enable users to create and use large collections of music. One of the desirable features for users is the ability to automatically organize the collection and search in it. One of the operations that they need is automatic identification of tracks' artists. This operation can be used to automatically classify new added tracks to a collection. Additionally, the user can use this operation to identify the artist of an unknown track. The artist name of a track can help the user find similar music. In this paper, we introduce a fast and scalable system that can automatically identify the artist of music tracks. This system is creating a signature for each track that is...
Feature selection and intrusion detection in cloud environment based on machine learning algorithms
, Article Proceedings - 15th IEEE International Symposium on Parallel and Distributed Processing with Applications and 16th IEEE International Conference on Ubiquitous Computing and Communications, ISPA/IUCC 2017 ; 25 May , 2018 , Pages 1417-1421 ; 9781538637906 (ISBN) ; Kazemi Abharian, S ; Wang, G ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
Characteristics and way of behavior of attacks and infiltrators on computer networks are usually very difficult and need an expert. In addition; the advancement of computer networks, the number of attacks and infiltrations is also increasing. In fact, the knowledge coming from expert will lose its value over time and must be updated and made available to the system and this makes the need for expert person always felt. In machine learning techniques, knowledge is extracted from the data itself which has diminished the role of the expert. Various methods used to detect intrusions, such as statistical models, safe system approach, neural networks, etc., all weaken the fact that it uses all the...