Approximate Interval-Based Temporal Dependencies: The Complexity Landscape

2014 ◽  
Author(s):  
Pietro Sala
Keyword(s):  
Temporal Dependencies

scholarly journals Adaptive Method for Modeling of Temporal Dependencies between Fields of Vision in Multi-Camera Surveillance Systems

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1303
Author(s):  
Karol Lisowski ◽  
Andrzej Czyżewski
Keyword(s):  
Statistical Data ◽  
Input Parameter ◽  
Gaussian Mixture ◽  
Adaptive Method ◽  
Transition Time ◽  
Surveillance Systems ◽  
Modified Particle Swarm Optimization ◽  
Novel Approach ◽  
Transition Times ◽  
Temporal Dependencies

A method of modeling the time of object transition between given pairs of cameras based on the Gaussian Mixture Model (GMM) is proposed in this article. Temporal dependencies modeling is a part of object re-identification based on the multi-camera experimental framework. The previously utilized Expectation-Maximization (EM) approach, requiring setting the number of mixtures arbitrarily as an input parameter, was extended with the algorithm that automatically adapts the model to statistical data. The probabilistic model was obtained by matching to the histogram of transition times between a particular pair of cameras. The proposed matching procedure uses a modified particle swarm optimization (mPSO). A way of using models of transition time in object re-identification is also presented. Experiments with the proposed method of modeling the transition time were carried out, and a comparison between previous and novel approach results are also presented, revealing that added swarms approximate normalized histograms very effectively. Moreover, the proposed swarm-based algorithm allows for modelling the same statistical data with a lower number of summands in GMM.

Exploring deep learning approaches to predict hourly evolution of surge levels

2021 ◽  
Author(s):  
Timothy Tiggeloven ◽  
Anaïs Couasnon ◽  
Chiem van Straaten ◽  
Sanne Muis ◽  
Philip Ward
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Storm Surge ◽  
Reference Model ◽  
Flood Hazard ◽  
Tide Gauge ◽  
Neural Network Models ◽  
Coastal Flood ◽  
Wide Range ◽  
Temporal Dependencies

<p>In order to better understand current coastal flood risk, it is critical to be able to predict the characteristics of non-tidal residuals (from here on referred to as surges), such as their temporal variation and the influence of coastal complexities on the magnitude of storm surge levels. In this study, we use an ensemble of Deep Learning (DL) models to predict hourly surge levels using four different types of neural networks and evaluate their performance. Among deep learning models, artificial neural networks (ANN) have been popular neural network models for surge level prediction, but other DL model types have not been investigated yet. In this contribution, we use three DL approaches - CNN, LSTM, and a combined CNN-LSTM model- , to capture temporal dependencies, spatial dependencies and spatio-temporal dependencies between atmospheric conditions and surges for 736 tide gauge locations. Using the high temporal and spatial resolution atmospheric reanalysis datasets ERA5 from ECMWF as predictors, we train, validate and test surge based on observed hourly surge levels derived from the GESLA-2 dataset. We benchmark our results obtained with DL to those provided by a simple probabilistic reference model based on climatology. This study shows promising results for predicting the temporal evolution of surges with DL approaches, and gives insight into the capability to gain skill using DL approaches with different Architectures for surge prediction. We therefore foresee a wide range of advantages in using DL models for coastal applications: probabilistic coastal flood hazard assessment, rapid prediction of storm surge estimates, future predictions of surge levels.</p>

scholarly journals Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3427 ◽  
Author(s):  
Geovanny Marulanda ◽  
Antonio Bello ◽  
Jenny Cifuentes ◽  
Javier Reneses
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Time Scales ◽  
Electricity Markets ◽  
Distribution Functions ◽  
Wind Power Generation ◽  
Environmental Benefits ◽  
Different Time Scales ◽  
Temporal Dependencies

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France.

Mining approximate interval-based temporal dependencies

2015 ◽  
Vol 53 (6-8) ◽  
pp. 547-585 ◽  
Author(s):  
Carlo Combi ◽  
Pietro Sala
Keyword(s):  
Temporal Dependencies

Residual memory networks: Feed-forward approach to learn long-term temporal dependencies

2017 ◽  
Author(s):  
Murali Karthick Baskar ◽  
Martin Karafiat ◽  
Lukas Burget ◽  
Karel Vesely ◽  
Frantisek Grezl ◽  
...  
Keyword(s):  
Feed Forward ◽  
Temporal Dependencies
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