Coupling physically-based modeling and deep learning for long-term global freshwater availability monitoring and prediction

2021 ◽  
Author(s):  
Hao Chen ◽  
Tiejun Wang
Keyword(s):  
Deep Learning ◽  
Physically Based Modeling ◽  
Physically Based ◽  
Freshwater Availability

scholarly journals Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch?

2019 ◽  
Vol 55 (2) ◽  
pp. 1179-1195 ◽  
Author(s):  
Alexander Y. Sun ◽  
Bridget R. Scanlon ◽  
Zizhan Zhang ◽  
David Walling ◽  
Soumendra N. Bhanja ◽  
...  
Keyword(s):  
Deep Learning ◽  
Satellite Data ◽  
Physically Based Modeling ◽  
Physically Based ◽  
Grace Satellite

Physically Based Modeling of the Long-Term Dynamics of Water Balance and Snow Water Storage Components in the Ob–Irtysh River Basin

2019 ◽  
Vol 46 (4) ◽  
pp. 493-503 ◽  
Author(s):  
E. M. Gusev ◽  
O. N. Nasonova ◽  
E. A. Shurkhno ◽  
L. Ya. Dzhogan ◽  
G. V. Aizel’
Keyword(s):  
Water Balance ◽  
River Basin ◽  
Water Storage ◽  
Physically Based Modeling ◽  
Physically Based ◽  
Snow Water ◽  
Irtysh River

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Physically Based Modeling of Cold Load Pickup

1981 ◽  
Vol PER-1 (9) ◽  
pp. 27-28 ◽  
Author(s):  
Satoru Ihara ◽  
Fred C. Schweppe
Keyword(s):  
Physically Based Modeling ◽  
Physically Based

Physically based modeling of the mechanical behavior of TRIP steels

2008 ◽  
Vol 1 (S1) ◽  
pp. 57-60 ◽  
Author(s):  
J. Bouquerel ◽  
K. Verbeken ◽  
J. Van Slycken ◽  
P. Verleysen ◽  
Y. Houbaert
Keyword(s):  
Mechanical Behavior ◽  
Physically Based Modeling ◽  
Trip Steels ◽  
Physically Based

Spoken content retrieval and understanding using deep learning

Impact ◽  
2021 ◽  
Vol 2021 (1) ◽  
pp. 9-11
Author(s):  
Lin-shan Lee
Keyword(s):  
Deep Learning ◽  
Scientific Community ◽  
Online Courses ◽  
The Internet ◽  
Human Voice ◽  
Content Retrieval ◽  
Youtube Videos ◽  
Access Information ◽  
Teaching Machines

Spoken content refers to all content over the Internet which includes human voice, essentially those in multimedia, such As YouTube videos and online courses. Today such content is retrieved via Google primarily based on human-generated text labels, because Google can only retrieve text over the Internet. The goal of this project is to produce technologies to retrieve accurately and efficiently such spoken content directly based on the included audio sounds instead of text labels, because machines today can listen to human voice just as they can read the text. The long term goal is to create a spoken version of Google, which may revolutionize the ways in which humans access information and improve their knowledge. Professor Lin-shan Lee at National Taiwan University is leading this project. He has been a distinguished leader in the global scientific community for the area of teaching machines to speak and listen to human voice for many years.

A Comparative Study of Data and Physically Based Gas Turbine Modeling for Long-Term Monitoring Scenarios: Part II — Emission Prediction Utilizing Different Levels of Design Information

2018 ◽  
Author(s):  
Moritz Lipperheide ◽  
Thomas Bexten ◽  
Manfred Wirsum ◽  
Martin Gassner ◽  
Stefano Bernero
Keyword(s):  
Gas Turbine ◽  
A Priori ◽  
Model Development ◽  
Fine Tuning ◽  
Plant Operator ◽  
Engine Model ◽  
Physically Based ◽  
Design Type ◽  
The Impact

Reliable engine and emission models allow for an online monitoring of commercial gas turbine operation and help the plant operator and the original equipment manufacturer (OEM) to ensure emission compliance of the aging engine. However, model development and validation require fine-tuning on the particular engines, which may differ in a fleet of a single design type by production, assembly and aging status. For this purpose, Artificial Neural Networks (ANN) offer a good and fast alternative to traditional physically-based engine modeling, because the model creation and adaption is merely an automatized process in commercially available software environments. However, ANN performance depends strongly on the availability of suitable data and a-priori data processing. The present work investigates the impact of specific engine information from the OEM’s design tools on ANN performance. As an alternative to a strictly data-based benchmark approach, engine characteristics were incorporated into ANNs by a pre-processing of the raw measurements with a simplified engine model. The resulting ‘virtual’ measurements, i.e. hot gas temperatures, then served as inputs to ANN training and application during long-term gas turbine operation. When processed input parameters were used for ANNs, overall long-term NOx prediction improved by 55%, and CO prediction by 16% in terms of RMSE, yielding comparable overall RMSE values to the physically-based model.

Physically based modeling of shear modulus-temperature relationship for thermosets

2015 ◽  
Vol 55 (12) ◽  
pp. 2893-2898 ◽  
Author(s):  
Wei Sun ◽  
Anastasios P. Vassilopoulos ◽  
Thomas Keller
Keyword(s):  
Shear Modulus ◽  
Temperature Relationship ◽  
Physically Based Modeling ◽  
Physically Based
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