Computation of Sectional Loads from Surface Triangulation and Flow Data

2011 ◽  
Author(s):  
Shishir Pandya ◽  
William Chan
Keyword(s):  
Flow Data ◽  
Surface Triangulation

Pressure-Flow Measurements for Selected Oral and Nasal Sound Segments Produced by Normal Adults

1992 ◽  
Vol 29 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Meri L. Andreassen ◽  
Bonnie E. Smith ◽  
Thomas W. Guyette
Keyword(s):  
Limited Information ◽  
Flow Data ◽  
Female Adult ◽  
Flow Measurements ◽  
Pressure Flow ◽  
Orifice Area ◽  
Normative Pressure ◽  
Categorization Scheme ◽  
Velopharyngeal Function ◽  
Normal Adults

Pressure-flow data are often used to provide information about the adequacy of velopharyngeal valving for speech. However, there is limited information available concerning simultaneous pressure-flow measurements for oral and nasal sound segments produced by normal speakers. This study provides normative pressure, flow, and velopharyngeal orifice area measurements for selected oral and nasal sound segments produced by 10 male and 10 female adult speakers. An aerodynamic categorization scheme of velopharyngeal function, including one typical category and three atypical categories (open, closed, and mixed) is proposed.

Evaluation of the statistical convergence of turbulent flow data

2017 ◽  
Author(s):  
Ramon Martins ◽  
Roney Thompson ◽  
Aristeu Silveira Neto ◽  
Gilmar MOMPEAN ◽  
João Rodrigo Andrade
Keyword(s):  
Turbulent Flow ◽  
Statistical Convergence ◽  
Flow Data

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

A Graphical Method for Storage Coefficient Determination from Quasi-Steady State Flow Data

1996 ◽  
Vol 27 (4) ◽  
pp. 247-254 ◽  
Author(s):  
Zekâi Şen
Keyword(s):  
Steady State ◽  
Graphical Method ◽  
Pumping Test ◽  
Quasi Steady State ◽  
Flow Data ◽  
Steady State Flow ◽  
Storage Coefficient ◽  
State Flow ◽  
Aquifer Test

A simple, approximate but practical graphical method is proposed for estimating the storage coefficient independently from the transmissivity value, provided that quasi-steady state flow data are available from a pumping test. In the past, quasi-steady state flow distance-drawdown data have been used for the determination of transmissivity only. The method is applicable to confined and leaky aquifers. The application of the method has been performed for various aquifer test data available in the groundwater literature. The results are within the practical limits of approximation compared with the unsteady state flow solutions.

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