7 research outputs found
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction
Data analytics helps basketball teams to create tactics. However, manual data
collection and analytics are costly and ineffective. Therefore, we applied a
deep bidirectional long short-term memory (BLSTM) and mixture density network
(MDN) approach. This model is not only capable of predicting a basketball
trajectory based on real data, but it also can generate new trajectory samples.
It is an excellent application to help coaches and players decide when and
where to shoot. Its structure is particularly suitable for dealing with time
series problems. BLSTM receives forward and backward information at the same
time, while stacking multiple BLSTMs further increases the learning ability of
the model. Combined with BLSTMs, MDN is used to generate a multi-modal
distribution of outputs. Thus, the proposed model can, in principle, represent
arbitrary conditional probability distributions of output variables. We tested
our model with two experiments on three-pointer datasets from NBA SportVu data.
In the hit-or-miss classification experiment, the proposed model outperformed
other models in terms of the convergence speed and accuracy. In the trajectory
generation experiment, eight model-generated trajectories at a given time
closely matched real trajectories
A Bayesian - Deep Learning model for estimating Covid-19 evolution in Spain
This work proposes a semi-parametric approach to estimate Covid-19
(SARS-CoV-2) evolution in Spain. Considering the sequences of 14 days
cumulative incidence of all Spanish regions, it combines modern Deep Learning
(DL) techniques for analyzing sequences with the usual Bayesian Poisson-Gamma
model for counts. DL model provides a suitable description of observed
sequences but no reliable uncertainty quantification around it can be obtained.
To overcome this we use the prediction from DL as an expert elicitation of the
expected number of counts along with their uncertainty and thus obtaining the
posterior predictive distribution of counts in an orthodox Bayesian analysis
using the well known Poisson-Gamma model. The overall resulting model allows us
to either predict the future evolution of the sequences on all regions, as well
as, estimating the consequences of eventual scenarios.Comment: Related to: https://github.com/scabras/covid19-bayes-d
Collapse warning system using LSTM neural networks for construction disaster prevention in extreme wind weather
Strong wind during extreme weather conditions (e.g., strong winds during typhoons) is one of the natural factors that cause the collapse of frame-type scaffolds used in façade work. This study developed an alert system for use in determining whether the scaffold structure could withstand the stress of the wind force. Conceptually, the scaffolds collapsed by the warning system developed in the study contains three modules. The first module involves the establishment of wind velocity prediction models. This study employed various deep learning and machine learning techniques, namely deep neural networks, long short-term memory neural networks, support vector regressions, random forest, and k-nearest neighbors. Then, the second module contains the analysis of wind force on the scaffolds. The third module involves the development of the scaffold collapse evaluation approach. The study area was Taichung City, Taiwan. This study collected meteorological data from the ground stations from 2012 to 2019. Results revealed that the system successfully predicted the possible collapse time for scaffolds within 1 to 6 h, and effectively issued a warning time. Overall, the warning system can provide practical warning information related to the destruction of scaffolds to construction teams in need of the information to reduce the damage risk