Items filtered by date: September 2015

EEG classification for the detection of mental states

Published in Journal Articles

Abstract

The objective of the present work is to develop a method that is able to automatically determine mental states of vigilance; i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state of mind. For instance, pilots and medical staff are expected to be in a highly alert state and the proposed method could help to detect possible deviations from this expected state. This work poses a binary classification problem where the goal is to distinguish between a "relaxed" state and a baseline state ("normal") from the study of electroencephalographic signals (EEG) collected with a small number of electrodes. The EEG of 58 subjects in the two alertness states (116 records) were collected via a cap with 58 electrodes. After a data validation step, 19 subjects were retained for further analysis. A genetic algorithm was used to select a subset of electrodes. Common spatial pattern (CSP) coupled to linear discriminant analysis (LDA) was used to build a decision rule and thus predict the alertness of the subjects. Different subset sizes were investigated and the best compromise between the number of selected electrodes and the quality of the solution was obtained by considering 9 electrodes. Even if the present approach is costly in computation time (GA search), it allows to construct a decision rule that provides an accurate and fast prediction of the alertness state of an unseen individual.

  1. Laurent Vézard, Pierrick Legrand, Marie Chavent, Frédérique Faïta-Aïnseba and Leonardo Trujillo. EEG Classification for the Detection of Mental States. Appl. Soft Comput. 32(C):113–131, 2015. URL, DOI BibTeX

    @article{,
    	author = {V\'{e}zard, Laurent and Legrand, Pierrick and Chavent, Marie and Fa\"{i}ta-A\"{i}nseba, Fr\'{e}d\'{e}rique and Trujillo, Leonardo},
    	title = "EEG Classification for the Detection of Mental States",
    	journal = "Appl. Soft Comput.",
    	issue_date = "July 2015",
    	volume = 32,
    	number = "C",
    	month = "",
    	year = 2015,
    	issn = "1568-4946",
    	pages = "113--131",
    	numpages = 19,
    	url = "http://dx.doi.org/10.1016/j.asoc.2015.03.028",
    	doi = "10.1016/j.asoc.2015.03.028",
    	acmid = 2778066,
    	publisher = "Elsevier Science Publishers B. V.",
    	address = "Amsterdam, The Netherlands, The Netherlands",
    	keywords = "Alertness, Common spatial pattern, Electroencephalographic signals, Genetic algorithm, Variable selection"
    }
    
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Energy Consumption Forecasting using Semantics Based Genetic Programming with Local Search Optimizer

Published in Journal Articles

Abstract

Energy consumption forecasting (ECF) is an important policy issue in today's economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

  1. Mauro Castelli, Leonardo Trujillo and Leonardo Vanneschi. Energy Consumption Forecasting Using Semantic-based Genetic Programming with Local Search Optimizer. Intell. Neuroscience 2015:57:57–57:57, 2015. URL, DOI BibTeX

    @article{,
    	author = "Castelli, Mauro and Trujillo, Leonardo and Vanneschi, Leonardo",
    	title = "Energy Consumption Forecasting Using Semantic-based Genetic Programming with Local Search Optimizer",
    	journal = "Intell. Neuroscience",
    	issue_date = "January 2015",
    	volume = 2015,
    	month = "",
    	year = 2015,
    	issn = "1687-5265",
    	pages = "57:57--57:57",
    	articleno = 57,
    	numpages = 1,
    	url = "http://dx.doi.org/10.1155/2015/971908",
    	doi = "10.1155/2015/971908",
    	acmid = 2810687,
    	publisher = "Hindawi Publishing Corp.",
    	address = "New York, NY, United States"
    }
    
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Prediction of energy performance of residential buildings: a genetic programming approach

Published in Journal Articles

Abstract

Energy consumption has long been emphasized as an important policy issue in today's economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country's energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs, which is considered an important issue in the deregulated energy market. The proposed framework blends a recently developed version of genetic programming with a local search method and linear scaling. The resulting system enables us to build a model that produces an accurate estimation of both considered parameters. Extensive simulations on 768 diverse residential buildings confirm the suitability of the proposed method in predicting heating load and cooling load. In particular, the proposed method is more accurate than the existing state-of-the-art techniques.

  1. Mauro Castelli, Leonardo Trujillo, Leonardo Vanneschi and Aleš Popovič. Prediction of energy performance of residential buildings: A genetic programming approach. Energy and Buildings 102():67 - 74, 2015. URL, DOI BibTeX

    @article{,
    	title = "Prediction of energy performance of residential buildings: A genetic programming approach",
    	journal = "Energy and Buildings",
    	volume = 102,
    	number = "",
    	pages = "67 - 74",
    	year = 2015,
    	issn = "0378-7788",
    	doi = "http://dx.doi.org/10.1016/j.enbuild.2015.05.013",
    	url = "http://www.sciencedirect.com/science/article/pii/S0378778815003849",
    	author = "Mauro Castelli and Leonardo Trujillo and Leonardo Vanneschi and Aleš Popovič"
    }
    
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