143 research outputs found
Facial Point Detection using Boosted Regression and Graph Models
Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point’s location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors
Energy Consumption Forecasting Using Ensemble Learning Algorithms
DCAI 2019: Distributed Computing and Artificial Intelligence, 16th International Conference, Special SessionsThe increase of renewable energy sources of intermittent nature has brought several new challenges for power and energy systems. In order to deal with the variability from the generation side, there is the need to balance it by managing consumption appropriately. Forecasting energy consumption becomes, therefore, more relevant than ever. This paper presents and compares three different ensemble learning methods, namely random forests, gradient boosted regression trees and Adaboost. Hour-ahead electricity load forecasts are presented for the building N of GECAD at ISEP campus. The performance of the forecasting models is assessed, and results show that the Adaboost model is superior to the other considered models for the one-hour ahead forecasts. The results of this study compared to previous works indicates that ensemble learning methods are a viable choice for short-term load forecast.This work has received funding from National Funds through FCT (Fundaçao da Ciencia e Tecnologia) under the project SPET – 29165, call SAICT 2017.info:eu-repo/semantics/publishedVersio
Building lighting energy consumption modelling with hybrid neural-statistic approaches
"In the proposed work we aim at modelling building lighting energy consumption. We compared several classical methods to the latest Artificial Intelligence. modelling technique: Artificial Neural Networks Ensembling (ANNE). Therefore, in this study we show how we built the ANNE and a new hybrid model based on the. statistical-ANNE combination. Experimentation has been carried out over a three. months data set coming from a real office building located in the ENEA ‘Casaccia’. Research Centre. Experimental results show that the proposed hybrid statistical-ANNE approach can get a remarkable improvement with respect to the best classical method(the statistical one).
Feature importance for machine learning redshifts applied to SDSS galaxies
We present an analysis of importance feature selection applied to photometric
redshift estimation using the machine learning architecture Decision Trees with
the ensemble learning routine Adaboost (hereafter RDF). We select a list of 85
easily measured (or derived) photometric quantities (or `features') and
spectroscopic redshifts for almost two million galaxies from the Sloan Digital
Sky Survey Data Release 10. After identifying which features have the most
predictive power, we use standard artificial Neural Networks (aNN) to show that
the addition of these features, in combination with the standard magnitudes and
colours, improves the machine learning redshift estimate by 18% and decreases
the catastrophic outlier rate by 32%. We further compare the redshift estimate
using RDF with those from two different aNNs, and with photometric redshifts
available from the SDSS. We find that the RDF requires orders of magnitude less
computation time than the aNNs to obtain a machine learning redshift while
reducing both the catastrophic outlier rate by up to 43%, and the redshift
error by up to 25%. When compared to the SDSS photometric redshifts, the RDF
machine learning redshifts both decreases the standard deviation of residuals
scaled by 1/(1+z) by 36% from 0.066 to 0.041, and decreases the fraction of
catastrophic outliers by 57% from 2.32% to 0.99%.Comment: 10 pages, 4 figures, updated to match version accepted in MNRA
Wind power forecasting with machine learning: single and combined methods
In Portugal, wind power represents one of the largest renewable sources of energy in the national energy mix. The investment in wind power started several decades ago and is still on the roadmap of political and industrial players. One example is that by 2030 it is estimated that wind power is going to represent up to 35% of renewable energy production in Portugal. With the growth of the installed wind capacity, the development of methods to forecast the amount of energy generated becomes increasingly necessary. Historically, Numerical Weather Prediction (NWP) models were used. However, forecasting accuracy depends on many variables such as on-site conditions, surrounding terrain relief, local meteorology, etc. Thus, it becomes a challenge to obtain improved results using such methods. This article aims to report the development of a machine learning pipeline with the objective of improving the forecasting capability of the NWP’s to obtain an error lower than 10%.info:eu-repo/semantics/publishedVersio
Anomaly detection for machine learning redshifts applied to SDSS galaxies
We present an analysis of anomaly detection for machine learning redshift
estimation. Anomaly detection allows the removal of poor training examples,
which can adversely influence redshift estimates. Anomalous training examples
may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies
with one or more poorly measured photometric quantity. We select 2.5 million
'clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730
'anomalous' galaxies with spectroscopic redshift measurements which are flagged
as unreliable. We contaminate the clean base galaxy sample with galaxies with
unreliable redshifts and attempt to recover the contaminating galaxies using
the Elliptical Envelope technique. We then train four machine learning
architectures for redshift analysis on both the contaminated sample and on the
preprocessed 'anomaly-removed' sample and measure redshift statistics on a
clean validation sample generated without any preprocessing. We find an
improvement on all measured statistics of up to 80% when training on the
anomaly removed sample as compared with training on the contaminated sample for
each of the machine learning routines explored. We further describe a method to
estimate the contamination fraction of a base data sample.Comment: 13 pages, 8 figures, 1 table, minor text updates to macth MNRAS
accepted versio
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