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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'indice de qualité de l'air (IQA) est un indice non dimensionnel pour la description de la qualité de l'air et est largement utilisé dans les programmes de gestion de la qualité de l'air. Une nouvelle méthode de prévision de l'indice de qualité de l'air basée sur l'apprentissage profond par dictionnaire (AQIF-DDL) et la vision industrielle est proposée dans cet article. Une image du ciel est utilisée comme entrée de la méthode et la sortie est la valeur AQI prévue. L'apprentissage approfondi par dictionnaire est utilisé pour extraire automatiquement les caractéristiques de l'image du ciel et réaliser la prévision AQI. L'idée d'apprendre des niveaux de dictionnaire plus profonds issus de l'apprentissage profond est également incluse pour augmenter la précision et la stabilité des prévisions. L'AQIF-DDL proposé est comparé à d'autres méthodes basées sur l'apprentissage profond, telles que le réseau de croyance profonde, l'auto-encodeur empilé et le réseau neuronal convolutif. Les résultats expérimentaux indiquent que la méthode proposée conduit à de bonnes performances en matière de prévision de l'AQI.
Bin CHEN
Jiaxing University
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Bin CHEN, "Air Quality Index Forecasting via Deep Dictionary Learning" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 5, pp. 1118-1125, May 2020, doi: 10.1587/transinf.2019EDP7296.
Abstract: Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7296/_p
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@ARTICLE{e103-d_5_1118,
author={Bin CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Air Quality Index Forecasting via Deep Dictionary Learning},
year={2020},
volume={E103-D},
number={5},
pages={1118-1125},
abstract={Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.},
keywords={},
doi={10.1587/transinf.2019EDP7296},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - Air Quality Index Forecasting via Deep Dictionary Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1118
EP - 1125
AU - Bin CHEN
PY - 2020
DO - 10.1587/transinf.2019EDP7296
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.
ER -