2017 Winter Simulation Conference (WSC) 2017
DOI: 10.1109/wsc.2017.8247971
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Coupling risk attitude and motion data mining in a preemtive construction safety framework

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Cited by 9 publications
(7 citation statements)
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“…Numerous data augmentation techniques have been tried and tested in computer vision [24][25][26][27][28] and speech recognition domain [29][30][31]. Charalambous and Bharath (2016) [24] introduced a simulation-based methodology which can be used for generating synthetic video data and sequence for machine/deep learning gait recognition algorithms.…”
Section: Data Augmentation For Classificationmentioning
confidence: 99%
See 1 more Smart Citation
“…Numerous data augmentation techniques have been tried and tested in computer vision [24][25][26][27][28] and speech recognition domain [29][30][31]. Charalambous and Bharath (2016) [24] introduced a simulation-based methodology which can be used for generating synthetic video data and sequence for machine/deep learning gait recognition algorithms.…”
Section: Data Augmentation For Classificationmentioning
confidence: 99%
“…in order to generate synthetic data for training and testing machine/deep learning algorithms [25,26,30]. Moreover, in the speech recognition domain, studies have applied techniques such as vocal tract length normalization [27][28], speech rate, and frequency-axis random distortion [27], label-preserving audio transformation [29] to improve the performance of learning algorithms.…”
Section: Data Augmentation For Classificationmentioning
confidence: 99%
“…For example, recent research has focused on developing various hazard proximity warning systems using localization systems [9][10][11][12] that alert workers when they get dangerously close to safety hazards that can cause injury. These efforts have demonstrated that workers are able to adopt appropriate hazard avoidance measures after being notified of their proximity to safety hazards such as mobile equipment [13,14].…”
Section: Introductionmentioning
confidence: 99%
“…Automated activity identification of construction equipment enables real-time applications including, but not limited to, real-time productivity monitoring (Ahn et al, 2015;Louis and Dunston, 2016b;Kim H. et al, 2018), real-time safety analysis (Carbonari et al, 2011;Cheng and Teizer, 2013;Rashid et al, 2017;Rashid and Behzadan, 2018), real-time routing of resources (Louis and Dunston, 2016a), and realtime AR/VR visualization (Ku et al, 2011;Dong and Kamat, 2013;Park et al, 2014;You et al, 2018). Offline applications enabled by automated activity identification include preparing dynamic simulation input (Akhavian and Behzadan, 2013;Vahdatikhaki and Hammad, 2014), automated cycle time analysis (Vahdatikhaki and Hammad, 2014;Mathur et al, 2015;Kim H. et al, 2018), operation analysis (Vahdatikhaki and Hammad, 2014;, and fuel use analysis (Lewis et al, 2011) etc.…”
Section: Introductionmentioning
confidence: 99%