Vaughn College of Aeronautics and Technology

About: Vaughn College of Aeronautics and Technology is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Gravitational microlensing & Planetary system. The organization has 727 authors who have published 708 publications receiving 14082 citations. The organization is also known as: College of Aeronautics.

Conduction of Heat within a Structure subjected to Kinetic Heating

Abstract: MANY hold the view that the attainment of very high speeds of flight will be prohibited by the excessive skin temperatures involved, particularly in the vicinity of the wing leading edge, at least until that time when unforeseen advances in metallurgy, or in the application of ceramics, enable the extraordinary problems involved to be overcome As an opinion it may for all one knows be justified by the event, but it seems (at least to the author) to exaggerate the facts, because it surely ignores the important role played by the conduction of heat along the skin in limiting the temperature It is the intention of the present article to convert others to this way of thinking

Assessment of the eN Method as a Transition Prediction Tool for Zero Pressure Gradient Flows with and without Boundary Layer Suction

Abstract: This paper compares theoretical predictions for the onset of transition to turbulence with experimental results observed in a 2-D zero pressure gradient boundary layer (typical unit Reynolds number 2 million) subjected to suction through a perforated strip extending 20cm in the streamwise direction, for suction coefficients in the range 0.001 to 0.01. In the zero suction case experimental observations agreed well with theoretical predictions obtained by using the eN method with a transition criterion of N=9. However, with suction applied this criterion failed to predict transition onset in the post-suction region. The discrepancy is attributed to disturbance effects not accounted for in linear stability theory, made apparent, either directly or indirectly, by the application of boundary layer suction.

Multi-label Classification of Abnormalities in 12-Lead ECG Using Deep Learning

Abstract: Identifying arrhythmias from electrocardiogram(ECG) signals remains an intractable challenge. This study aims to develop an effective and non-invasive approach to realize the recognition of arrhythmias based on 12-lead ECG for the PhysioNet/Computing in Cardiology Challenge2020. To this end, we propose a deep learning-based diagnosis approach, called EASTNet which captures the characteristics of cardiac abnormalities and correlation between heartbeats sampled randomly from 12-lead ECG records by a 34-layer 1D-deep squeeze-and-excitation network. Experimenting in the multi-label arrhythmia classification task, our team, EASTBLUE, was unable to rank and score in the hidden validation and test sets, but achieved diagnostic performance with 0.7030 ± 0.0090 metric score using 5-fold cross-validation on the training set. We also investigate the effect of beat sampling on diagnostic performance, and find that the beat sampling plays a role in data augmentation that effectively alleviates network overfitting. These results demonstrate that our approach has good potential application prospects in clinical practice, especially in the auxiliary diagnosis of abnormalities.

Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability.

Abstract: Emotional text-to-speech synthesis (ETTS) has seen much progress in recent years. However, the generated voice is often not perceptually identifiable by its intended emotion category. To address this problem, we propose a new interactive training paradigm for ETTS, denoted as i-ETTS, which seeks to directly improve the emotion discriminability by interacting with a speech emotion recognition (SER) model. Moreover, we formulate an iterative training strategy with reinforcement learning to ensure the quality of i-ETTS optimization. Experimental results demonstrate that the proposed i-ETTS outperforms the state-of-the-art baselines by rendering speech with more accurate emotion style. To our best knowledge, this is the first study of reinforcement learning in emotional text-to-speech synthesis.

Failure Prediction of Integrated Avionic System

Abstract: The avionic system is developing towards integration,which brings a new challenge to failure prediction technology.Integration features of the avionic system were analyzed and structures for failure prediction of avionic products were presented,including model-based structure,data driven structure and integrated prediction structure.Common features of these structures and their typical application in the field of aviation were discussed.On the basis of which,and according to requirement of integration features and the applicability of structures for failure prediction of avionic products,some problems facing the failure prediction of integrated avionic system were analyzed,and preliminary solutions were proposed.