Modal Decomposition of Flow Data via Gradient-Based Transport Optimization

2021 ◽  
pp. 203-224
Author(s):  
Felix Black ◽  
Philipp Schulze ◽  
Benjamin Unger
Keyword(s):  
Flow Data ◽  
Modal Decomposition ◽  
Gradient Based

Gradient based system-level diagnosis

2007 ◽  
Vol 51 (1-2) ◽  
pp. 43
Author(s):  
Balázs Polgár ◽  
Endre Selényi
Keyword(s):  
System Level ◽  
Gradient Based

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.

An Efficient Multiple Description Coding for Multi-View Video Based on the Correlation of Spatial Polyphase Transformed Subsequences

2019 ◽  
Vol 63 (5) ◽  
pp. 50401-1-50401-7 ◽  
Author(s):  
Jing Chen ◽  
Jie Liao ◽  
Huanqiang Zeng ◽  
Canhui Cai ◽  
Kai-Kuang Ma
Keyword(s):  
Video Transmission ◽  
Video Sequence ◽  
Three Dimensional ◽  
The Other ◽  
Multiple Description Coding ◽  
Multiple Description ◽  
Prediction Mode ◽  
Gradient Based ◽  
Directional Interpolation ◽  
Description Coding

Abstract For a robust three-dimensional video transmission through error prone channels, an efficient multiple description coding for multi-view video based on the correlation of spatial polyphase transformed subsequences (CSPT_MDC_MVC) is proposed in this article. The input multi-view video sequence is first separated into four subsequences by spatial polyphase transform and then grouped into two descriptions. With the correlation of macroblocks in corresponding subsequence positions, these subsequences should not be coded in completely the same way. In each description, one subsequence is directly coded by the Joint Multi-view Video Coding (JMVC) encoder and the other subsequence is classified into four sets. According to the classification, the indirectly coding subsequence selectively employed the prediction mode and the prediction vector of the counter directly coding subsequence, which reduces the bitrate consumption and the coding complexity of multiple description coding for multi-view video. On the decoder side, the gradient-based directional interpolation is employed to improve the side reconstructed quality. The effectiveness and robustness of the proposed algorithm is verified by experiments in the JMVC coding platform.

Stable and Supported Semantics in Continuous Vector Spaces

2020 ◽  
Author(s):  
Yaniv Aspis ◽  
Krysia Broda ◽  
Alessandra Russo ◽  
Jorge Lobo
Keyword(s):  
Logic Program ◽  
Matrix Multiplication ◽  
Vector Spaces ◽  
Continuous Space ◽  
Continuous Vector ◽  
Novel Approach ◽  
Gradient Based ◽  
Normal Logic ◽  
Parameter Values ◽  
Normal Logic Program

We introduce a novel approach for the computation of stable and supported models of normal logic programs in continuous vector spaces by a gradient-based search method. Specifically, the application of the immediate consequence operator of a program reduct can be computed in a vector space. To do this, Herbrand interpretations of a propositional program are embedded as 0-1 vectors in $\mathbb{R}^N$ and program reducts are represented as matrices in $\mathbb{R}^{N \times N}$. Using these representations we prove that the underlying semantics of a normal logic program is captured through matrix multiplication and a differentiable operation. As supported and stable models of a normal logic program can now be seen as fixed points in a continuous space, non-monotonic deduction can be performed using an optimisation process such as Newton's method. We report the results of several experiments using synthetically generated programs that demonstrate the feasibility of the approach and highlight how different parameter values can affect the behaviour of the system.

Exploration of Modal Decomposition Techniques for Wind Turbines

2014 ◽  
Author(s):  
Anshul Mittal ◽  
Kidambi Sreenivas ◽  
Lafayette K. Taylor
Keyword(s):  
Wind Turbines ◽  
Decomposition Techniques ◽  
Modal Decomposition

Modal Decomposition Analysis of Hovering Rotor Wake Breakdown

2021 ◽  
Author(s):  
Forrest Mobley ◽  
Jared Carnes ◽  
James G. Coder
Keyword(s):  
Decomposition Analysis ◽  
Modal Decomposition ◽  
Rotor Wake

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

Basic heat-flow data from the United States

1974 ◽  
Author(s):  
J.H. Sass ◽  
Robert J. Munroe
Keyword(s):  
United States ◽  
Heat Flow ◽  
The United States ◽  
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%

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