Deep-Learning Based Automatic Spontaneous Speech Assessment in a Data-Driven Approach for the 2017 SLaTE CALL Shared Challenge

2017 ◽  
Author(s):  
Yoo Rhee Oh ◽  
Hyung-Bae Jeon ◽  
Hwa Jeon Song ◽  
Byung Ok Kang ◽  
Yun-Kyung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spontaneous Speech ◽  
Data Driven ◽  
Data Driven Approach ◽  
Speech Assessment

scholarly journals Risk Prediction for Ship Encounter Situation Awareness Using Long Short-Term Memory Based Deep Learning on Intership Behaviors

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jie Ma ◽  
Wenkai Li ◽  
Chengfeng Jia ◽  
Chunwei Zhang ◽  
Yu Zhang
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Short Term Memory ◽  
Data Driven ◽  
Yangtze River Estuary ◽  
Collision Risk ◽  
Short Term ◽  
Term Memory ◽  
Data Driven Approach ◽  
Long Short Term Memory

Encounter risk prediction is critical for safe ship navigation, especially in congested waters, where ships sail very near to each other during various encounter situations. Prior studies on the risk of ship collisions were unable to address the uncertainty of the encounter process when ignoring the complex motions constituting the dynamic ship encounter behavior, which may seriously affect the risk prediction performance. To fill this gap, a novel AIS data-driven approach is proposed for ship encounter risk prediction by modeling intership behavior patterns. In particular, multidimensional features of intership behaviors are extracted from the AIS trace data to capture spatial dependencies between encountering ships. Then, the challenging task of risk prediction is to discover the complex and uncertain relationship between intership behaviors and future collision risk. To address this issue, we propose a deep learning framework. To represent the temporal dynamics of the encounter process, we use the sliding window technique to generate the sequences of behavioral features. The collision risk level at a future time is taken as the class label of the sequence. Then, the long short-term memory network, which has a strong ability to model temporal dependency and complex patterns, is extended to establish the relationship. The benefit of our approach is that it transforms the complex problem for risk prediction into a time series classification task, which makes collision risk prediction reliable and easier to implement. Experiments were conducted on a set of naturalistic data from various encounter scenarios in the South Channel of the Yangtze River Estuary. The results show that the proposed data-driven approach can predict future collision risk with high accuracy and efficiency. The approach is expected to be applied for the early prediction of encountering ships and as decision support to improve navigation safety.

scholarly journals Contributions towards Data driven Deep Learning methods to predict Steady State Fluid Flow in mechanical Heart Valves

2021 ◽  
Vol 7 (2) ◽  
pp. 625-628
Author(s):  
Jan Oldenburg ◽  
Julian Renkewitz ◽  
Michael Stiehm ◽  
Klaus-Peter Schmitz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Steady State ◽  
Flow Field ◽  
Heart Valves ◽  
Data Driven ◽  
High Shear ◽  
Blood Flows ◽  
The Neural Network ◽  
Data Driven Approach

Abstract It is commonly accepted that hemodynamic situation is related with cardiovascular diseases as well as clinical post-procedural outcome. In particular, aortic valve stenosis and insufficiency are associated with high shear flow and increased pressure loss. Furthermore, regurgitation, high shear stress and regions of stagnant blood flow are presumed to have an impact on clinical result. Therefore, flow field assessment to characterize the hemodynamic situation is necessary for device evaluation and further design optimization. In-vitro as well as in-silico fluid mechanics methods can be used to investigate the flow through prostheses. In-silico solutions are based on mathematical equitation’s which need to be solved numerically (Computational Fluid Dynamics - CFD). Fundamentally, the flow is physically described by Navier-Stokes. CFD often requires high computational cost resulting in long computation time. Techniques based on deep-learning are under research to overcome this problem. In this study, we applied a deep-learning strategy to estimate fluid flows during peak systolic steady-state blood flows through mechanical aortic valves with varying opening angles in randomly generated aortic root geometries. We used a data driven approach by running 3,500 two dimensional simulations (CFD). The simulation data serves as training data in a supervised deep learning framework based on convolutional neural networks analogous to the U-net architecture. We were able to successfully train the neural network using the supervised data driven approach. The results showing that it is feasible to use a neural network to estimate physiological flow fields in the vicinity of prosthetic heart valves (Validation error below 0.06), by only giving geometry data (Image) into the Network. The neural network generates flow field prediction in real time, which is more than 2500 times faster compared to CFD simulation. Accordingly, there is tremendous potential in the use of AIbased approaches predicting blood flows through heart valves on the basis of geometry data, especially in applications where fast fluid mechanic predictions are desired.

scholarly journals Finding epic moments in live content through deep learning on collective decisions

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hyeonho Song ◽  
Kunwoo Park ◽  
Meeyoung Cha
Keyword(s):  
Deep Learning ◽  
User Study ◽  
Data Driven ◽  
Live Streaming ◽  
Video Content ◽  
Live Video ◽  
Collective Decisions ◽  
Data Driven Approach ◽  
Streaming Services ◽  
Deep Learning Model

AbstractLive streaming services enable the audience to interact with one another and the streamer over live content. The surging popularity of live streaming platforms has created a competitive environment. To retain existing viewers and attract newcomers, streamers and fans often create a well-condensed summary of the streamed content. However, this process is manual and costly due to the length of online live streaming events. The current study identifies enjoyable moments in user-generated live video content by examining the audiences’ collective evaluation of its epicness. We characterize what features “epic” moments and present a deep learning model to extract them based on analyzing two million user-recommended clips and the associated chat conversations. The evaluation shows that our data-driven approach can identify epic moments from user-generated streamed content that cover various contexts (e.g., victory, funny, awkward, embarrassing). Our user study further demonstrates that the proposed automatic model performs comparably to expert suggestions. We discuss implications of the collective decision-driven extraction in identifying diverse epic moments in a scalable way.

scholarly journals Stochastic Detection of Interior Design Styles Using a Deep-Learning Model for Reference Images

2020 ◽  
Vol 10 (20) ◽  
pp. 7299 ◽  
Author(s):  
Jinsung Kim ◽  
Jin-Kook Lee
Keyword(s):  
Deep Learning ◽  
Interior Design ◽  
Learning Model ◽  
Data Driven ◽  
Data Preparation ◽  
Reference Images ◽  
Data Driven Approach ◽  
Image Set ◽  
Design Style ◽  
Deep Learning Model

This paper describes an approach for identifying and appending interior design style information stochastically with reference images and a deep-learning model. In the field of interior design, design style is a useful concept and has played an important role in helping people understand and communicate interior design. Previous studies have focused on how the interior design style categories can be defined. On the other hand, this paper focuses on how stochastically recognizing the design style of given interior design reference images using a deep learning-based data-driven approach. The proposed method can be summarized as follows: (1) data preparation based on a general design style definition, (2) implementing an interior design style recognition model using a pre-trained VGG16 model, (3) training and evaluating the trained model, and (4) demonstration of stochastic detection of interior design styles for reference images. The result shows that the trained model automatically recognizes the design styles of given interior images with probability values. The recognition results, model, and trained image set contribute to facilitating the management and utilization of an interior design references database.

scholarly journals A data-driven approach for lightning nowcasting with deep learning

2021 ◽  
Author(s):  
Amirhossein Mostajabi ◽  
Ehsan Mansouri ◽  
Pedram Pad ◽  
Marcos Rubinstein ◽  
Andrea Dunbar ◽  
...  
Keyword(s):  
Deep Learning ◽  
South America ◽  
Large Scale ◽  
Prediction Method ◽  
Data Driven ◽  
System Failures ◽  
Preventive Actions ◽  
Data Driven Approach ◽  
Microphysical Processes ◽  
Using Data

<p>Lightning is  responsible directly or indirectly, for significant human casualties and property damage worldwide. <sup>1,2</sup>  It can cause injury and death in humans and animals, ignite fires, affect and destroy electronic devices, and cause electrical surges and system failures in airplanes and rockets.<sup>3–5</sup> These severe and costly outcomes can be averted by predicting the lightning occurrence in advance and taking preventive actions accordingly. Therefore, a practical and fast lightning prediction method is of considerable value.</p><p>Lightning is formed in the atmosphere through the combination of complex dynamic and microphysical processes, making it difficult to predict its occurrence using analytical or probabilistic approaches. In this work, we aim at leveraging advances in machine learning, deep learning, and pattern recognition to develop a lightning nowcasting model. Current numerical weather models rely on lightning parametrization. These models suffer from two drawbacks; the sequential nature of the model limits the computation speed, especially for nowcasting, and the recorded data are only used in the parametrization step and not in the prediction.<sup>6,7</sup></p><p>To cope with these drawbacks, we propose to leverage the large amounts of available data to develop a fully data-driven approach with enhanced prediction speed based on deep neural networks. The developed lightning nowcasting model is based on a residual U-net architecture.<sup>8</sup> The model consists of two paths from the input to the output: (i) a highway path copying the input to the output in the same way as the persistent baseline model does, and (ii) a fully convolutional U-net which learns to adjust the former path to reach the desired output. The U-net itself consists of a contracting part with alternating convolution, and max pooling layers followed by an expanding part of alternating upsampling, convolution, and concatenation layers.<sup>9–11</sup></p><p>Our dataset consists of post-processed data of recorded lightning occurrences in 15-minute intervals over 60 days obtained from the GOES satellite over the Americas. We have optimized the model using data from the northern part of South America, a region characterized by high lightning activity. The model was then applied to other regions of the Americas. We are using 70-15-15% separation for training, validation, and test datasets. Upon completion of the training process, the model can achieve an overall F1 score of 70% with a lead time of 30 minutes over South America in fractions of a second. This is more than 25% increase in the F1 score compared to the persistent model which is used as our baseline forecast method.</p><p>To the best of our knowledge, our model is the first data-driven approach for lightning prediction. The developed model can pave the way to large-scale, efficient, and practical lightning prediction, which in turn can protect lives and save resources.</p>

Sign in / Sign up

Export Citation Format

Share Document