scholarly journals Real-Time Prediction of the Trend of Ground Motion Intensity Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Tao Liu ◽  
Zhijun Dai
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Ground Motion ◽  
Structural Damage ◽  
Time Prediction ◽  
The Neural Network ◽  
Input Sample ◽  
Response Measures ◽  
Deep Learning Model ◽  
Emergency Measures

In order to predict the intensity of earthquake damage in advance and improve the effectiveness of earthquake emergency measures, this paper proposes a deep learning model for real-time prediction of the trend of ground motion intensity. The input sample is the real-time monitoring recordings of the current received ground motion acceleration. According to the different sampling frequencies, the neural network is constructed by several subnetworks, and the output of each subnetwork is combined into one. After the training and verification of the model, the results show that the model has an accuracy rate of 75% on the testing set, which is effective on real-time prediction of the ground motion intensity. Moreover, the correlation between the Arias intensity and structural damage is stronger than the correlation between peak acceleration and structural damage, so the model is useful for determining real-time response measures on earthquake disaster prevention and mitigation compared with the current more common antiseismic measures based on predictive PGA.

A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Parth K. Shah ◽  
Jennifer C. Ginestra ◽  
Lyle H. Ungar ◽  
Paul Junker ◽  
Jeff I. Rohrbach ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Prospective Evaluation ◽  
Time Prediction ◽  
Deep Learning Model

The input vector space optimization for LSTM deep learning model in real-time prediction of ship motions

2020 ◽  
Vol 213 ◽  
pp. 107681
Author(s):  
Yucheng Liu ◽  
Wenyang Duan ◽  
Limin Huang ◽  
Shiliang Duan ◽  
Xuewen Ma
Keyword(s):  
Deep Learning ◽  
Vector Space ◽  
Real Time ◽  
Learning Model ◽  
Input Vector ◽  
Ship Motions ◽  
Time Prediction ◽  
Deep Learning Model

scholarly journals Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study (Preprint)

2019 ◽  
Author(s):  
Junetae Kim ◽  
Yu Rang Park ◽  
Jeong Hoon Lee ◽  
Jae-Ho Lee ◽  
Young-Hak Kim ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Intensive Care ◽  
Deep Learning ◽  
Retrospective Study ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Time Dependent ◽  
Time Prediction ◽  
Deep Learning Model

BACKGROUND Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted because of the complex and time-dependent data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning–based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records. OBJECTIVE This study aimed to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential. METHODS A retrospective study of 759 ICU patients was conducted between January 2013 and July 2015. A character-level gated recurrent unit with a Weibull distribution algorithm was used to develop a real-time prediction model. Fivefold cross-validation testing (training set: 80% and validation set: 20%) determined the consistency of model accuracy. The time-dependent area under the curve (TAUC) was analyzed based on the aggregation of 5 validation sets. RESULTS The TAUCs of the implemented model were 0.963, 0.942, 0.917, 0.875, 0.850, 0.842, and 0.761 before cardiac arrest at 1, 8, 16, 24, 32, 40, and 48 hours, respectively. The sensitivity was between 0.846 and 0.909, and specificity was between 0.923 and 0.946. The distribution of risk between the cardiac arrest group and the non–cardiac arrest group was generally different, and the difference rapidly increased as the time left until cardiac arrest reduced. CONCLUSIONS A deep learning model for forecasting cardiac arrest was implemented and tested by considering the cumulative and fluctuating effects of time-dependent clinical data gathered from a large medical center. This real-time prediction model is expected to improve patient’s care by allowing early intervention in patients at high risk of unexpected cardiac arrests.

scholarly journals Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study

10.2196/16349 ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. e16349 ◽  
Author(s):  
Junetae Kim ◽  
Yu Rang Park ◽  
Jeong Hoon Lee ◽  
Jae-Ho Lee ◽  
Young-Hak Kim ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Intensive Care ◽  
Deep Learning ◽  
Retrospective Study ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Time Dependent ◽  
Time Prediction ◽  
Deep Learning Model

Background Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted because of the complex and time-dependent data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning–based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records. Objective This study aimed to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential. Methods A retrospective study of 759 ICU patients was conducted between January 2013 and July 2015. A character-level gated recurrent unit with a Weibull distribution algorithm was used to develop a real-time prediction model. Fivefold cross-validation testing (training set: 80% and validation set: 20%) determined the consistency of model accuracy. The time-dependent area under the curve (TAUC) was analyzed based on the aggregation of 5 validation sets. Results The TAUCs of the implemented model were 0.963, 0.942, 0.917, 0.875, 0.850, 0.842, and 0.761 before cardiac arrest at 1, 8, 16, 24, 32, 40, and 48 hours, respectively. The sensitivity was between 0.846 and 0.909, and specificity was between 0.923 and 0.946. The distribution of risk between the cardiac arrest group and the non–cardiac arrest group was generally different, and the difference rapidly increased as the time left until cardiac arrest reduced. Conclusions A deep learning model for forecasting cardiac arrest was implemented and tested by considering the cumulative and fluctuating effects of time-dependent clinical data gathered from a large medical center. This real-time prediction model is expected to improve patient’s care by allowing early intervention in patients at high risk of unexpected cardiac arrests.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Application of YOLO Deep Learning Model for Real Time Abandoned Baggage Detection

2018 ◽  
Author(s):  
Tossaporn Santad ◽  
Piyarat Silapasupphakornwong ◽  
Worawat Choensawat ◽  
Kingkarn Sookhanaphibarn
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping

2021 ◽  
Vol 14 (6) ◽  
pp. 3421-3435
Author(s):  
Zhenjiao Jiang ◽  
Dirk Mallants ◽  
Lei Gao ◽  
Tim Munday ◽  
Gregoire Mariethoz ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Land Surface ◽  
Regional Scale ◽  
Learning Model ◽  
Generic Structure ◽  
Squared Error ◽  
The Neural Network ◽  
Deep Learning Model ◽  
Surface Observations

Abstract. This study introduces an efficient deep-learning model based on convolutional neural networks with joint autoencoder and adversarial structures for 3D subsurface mapping from 2D surface observations. The method was applied to delineate paleovalleys in an Australian desert landscape. The neural network was trained on a 6400 km2 domain by using a land surface topography as 2D input and an airborne electromagnetic (AEM)-derived probability map of paleovalley presence as 3D output. The trained neural network has a squared error <0.10 across 99 % of the training domain and produces a squared error <0.10 across 93 % of the validation domain, demonstrating that it is reliable in reconstructing 3D paleovalley patterns beyond the training area. Due to its generic structure, the neural network structure designed in this study and the training algorithm have broad application potential to construct 3D geological features (e.g., ore bodies, aquifer) from 2D land surface observations.

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

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