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time-and-motion-studies
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On control of spacecraft relative motion in the case of an elliptic keplerian chief
, Article Advances in the Astronautical Sciences ; Vol. 150, issue , August , 2014 , p. 1413-1432 ; 2195268X ; Butcher, E. A ; Mesbahi, A ; Sharif University of Technology
Abstract
In this study, control strategies based on time-varying LQR, Lyapunov-Floquet transformation (LFT), backstepping, feedback linearization, and constant gain feedback control are implemented for the linearized time periodic equations of spacecraft relative motion when the reference orbit is elliptic. Also, natural and nonnatural leader-follower two-spacecraft formations are studied. The stability of the closed-loop response, the control effort required, and the settling time are investigated and compared for all control strategies. Furthermore, using constant gain feedback, the estimated region of attraction of the closed-loop system is obtained analytically
Abnormal event detection and localisation in traffic videos based on group sparse topical coding
, Article IET Image Processing ; Volume 10, Issue 3 , 2016 , Pages 235-246 ; 17519659 (ISSN) ; Tabandeh, M ; Gholampour, I ; Sharif University of Technology
Institution of Engineering and Technology
2016
Abstract
In visual surveillance, detecting and localising abnormal events are of great interest. In this study, an unsupervised method is proposed to automatically discover abnormal events occurring in traffic videos. For learning typical motion patterns occurring in such videos, a group sparse topical coding (GSTC) framework and an improved version of it are applied to optical flow features extracted from video clips. Then a very simple and efficient algorithm is proposed for GSTC. It is shown that discovered motion patterns can be employed directly in detecting abnormal events. A variety of abnormality metrics based on the resulting sparse codes for detection of abnormality are investigated....
A new two-stage topic model based framework for modeling traffic motion patterns
, Article 10th Iranian Conference on Machine Vision and Image Processing, MVIP 2017 ; Volume 2017-November , April , 2018 , Pages 276-280 ; 21666776 (ISSN) ; 9781538644041 (ISBN) ; Gholampour, I ; Tabandeh, M ; Sharif University of technology
IEEE Computer Society
2018
Abstract
Analyzing motion patterns in traffic videos can be exploited directly to generate high-level descriptions of the video content. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this paper, a new two-stage framework is proposed for traffic motion pattern extraction based on topic models. This framework forces the topic model to learn known meaningful motion patterns in traffic scenes. Latent Dirichlet Allocation (LDA) is employed as the topic model. Experimental results show that our proposed framework finds the motion patterns more efficiently and gives a meaningful representation for the video. © 2017 IEEE
Sequential topic modeling for efficient analysis of traffic scenes
, Article 9th International Symposium on Telecommunication, IST 2018, 17 December 2018 through 19 December 2018 ; 2019 , Pages 559-564 ; 9781538682746 (ISBN) ; Pir Moradian, E ; Gholampour, I ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
A two-level Sparse Topical Coding (STC) topic model is proposed in this paper for analyzing video sequences of traffic surveillance containing hierarchical patterns accompanied by complicated motions and co-occurrences. In order to automatically cluster optical flow features into motion patterns, a first level STC model is used. Next, the second level STC model is applied for clustering motion patterns into traffic phases. The effectiveness of the suggested method is proved by experiments on a traffic dataset in the real world. Our simulations show that the proposed two-level STC is able to extract the motion patterns and traffic phases accurately, leading to realistic describing the traffic...
Employing topical relations in semantic analysis of traffic videos
, Article IEEE Intelligent Systems ; Volume 34, Issue 1 , 2019 , Pages 3-13 ; 15411672 (ISSN) ; Gholampour, I ; Tabandeh, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Motion patterns in traffic video can be directly exploited to generate high-level descriptions of video content, which can be used for rule mining and abnormal event detection. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this paper, a topic related sparse topical coding framework is proposed for more effectively discovering motion patterns in traffic videos. © 2001-2011 IEEE
Metabolic load comparison between the quarters of a game in elite male basketball players using sport metabolomics
, Article European Journal of Sport Science ; Volume 21, Issue 7 , 2021 , Pages 1022-1034 ; 17461391 (ISSN) ; Gaeini, A. A ; Shirzad, E ; Gilany, K ; Chashniam, S ; Sandbakk, Ø ; Sharif University of Technology
Taylor and Francis Ltd
2021
Abstract
Purpose: A basketball match is characterized by intermittent high-intensity activities, thereby relying extensively on both aerobic and anaerobic metabolic pathways. Here, we aimed to compare the metabolic fluctuations between the four 10-min quarters of high-level basketball games using metabolomics analyses. Methods: 70 male basketball players with at least 3 years of experience in the Iran national top-league participated. Before and after each quarter, saliva samples were taken for subsequent untargeted metabolomics analyses, where Principal component analysis (PCA) and Partial least squares-discriminant analysis (PLS-DA) were employed for statistical analysis. Results: Quarters 1 and 3...
Beyond bag-of-words: An improved Sparse Topical Coding for learning motion patterns in traffic scenes
, Article 9th Iranian Conference on Machine Vision and Image Processing, 18 November 2015 through 19 November 2015 ; Volume 2016-February , 2015 , Pages 1-4 ; 21666776 (ISSN) ; 9781467385398 (ISBN) ; Tabandeh, M ; Gholampour, I ; Sharif University of Technology
IEEE Computer Society
Abstract
Analyzing motion patterns in traffic videos can directly generate some high-level descriptions of the video content which can be further employed in rule mining and abnormal event detection. The most recent and successful unsupervised approaches for complex traffic scene analysis are based on topic models. However, most existing topic models share some key characteristics which could limit their utility. In this paper, based on extracted optical flow features from video clips, we employ Sparse Topical Coding (STC) framework to automatically discover typical motion patterns in traffic scenes. For this purpose, we improve the STC to overcome one of the drawbacks of topic models with the aim of...
Introducing a response-based duration metric and its correlation with structural damages
, Article Bulletin of Earthquake Engineering ; Volume 17, Issue 11 , 2019 , Pages 5987-6008 ; 1570761X (ISSN) ; Harati, M ; Ashoori Barmchi, M ; Estekanchi, H. E ; Sharif University of Technology
Springer Netherlands
2019
Abstract
This study proposes a response-based parameter for strong motion duration which is computed for structures and is the total time they are nonlinear during an earthquake. Correlation between structural response and duration for structures, subjected to a set of spectrum matched ground motions, is employed to examine the efficiency of the proposed method. The spectral matching procedure ensures that the influence of amplitude and frequency content of motions on structural response variability is significantly removed. Four concrete building type systems are studied and correlation coefficients of structural response with the proposed duration definition are examined. Comparison of the proposed...