Prediction of Indoor Climate and Long-term Air Quality Using a Building Thermal Transient model, Artificial Neural Networks and Typical Meteorological Year

2009 ◽  
Author(s):  
Gang Sun ◽  
Steven J Hoff
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Indoor Climate ◽  
Transient Model ◽  
Thermal Transient ◽  
Artificial Neural

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

Ion-gating synaptic transistors with long-term synaptic weight modulation

2021 ◽  
Vol 9 (16) ◽  
pp. 5396-5402
Author(s):  
Youngjun Park ◽  
Min-Kyu Kim ◽  
Jang-Sik Lee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Weight ◽  
Recognition Accuracy ◽  
Artificial Neural ◽  
High Recognition Accuracy

This paper presents synaptic transistors that show long-term synaptic weight modulation via injection of ions. Linear and symmetric weight update is achieved, which enables high recognition accuracy in artificial neural networks.

scholarly journals ARTIFICIAL NEURAL NETWORKS: AN APPLICATION IN THE STUDY OF AIR QUALITY

2014 ◽  
Vol 13 ◽  
Author(s):  
Amaury De Souza ◽  
Hamilton Germano Pavão ◽  
Ana Paula Garcia Oliveira
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Multilayer Perceptron ◽  
Artificial Neural

A estimativa da concentração do ozônio de superfície propicia a geração de dados para o planejamento de previsão da qualidade do ar, útil na gestão de saúde publica. O objetivo deste trabalho foi elaborar uma Rede Neural Artificial (RNAs) para estimar a concentração do ozônio de superfície em função de dados diários de clima. A RNA, do tipo FeedForward Multilayer Perceptron, foi treinada tomando-se por referência da concentração diária do ozônio medida. Nas camadas intermediárias e de saída foram utilizadas funções de ativação do tipo tan-sigmóide e lineares, respectivamente. O desempenho da RNA desenvolvida foi muito bom, podendo-se considerá-la como integrante do conjunto de métodos indiretos para estimativa da concentração do ozônio de superfície. O modelo proposto pode ser utilizado pelo governo público como ferramenta para ativar ações de ferramentas durante os períodos de estagnação atmosférica, quando os níveis de ozônio na atmosfera possam representar riscos à saúde publica.

A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia

2012 ◽  
Vol 518-523 ◽  
pp. 2969-2979 ◽  
Author(s):  
Ayari Samia ◽  
Nouira Kaouther ◽  
Trabelsi Abdelwahed
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Urban Areas ◽  
Moving Average ◽  
Warning System ◽  
Model Combining ◽  
Ann Models ◽  
Artificial Neural

Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.

On the Analysis of Turbulent Flow Signals by Artificial Neural Networks and Adaptive Techniques

2007 ◽  
Author(s):  
F. Lo´pez Pen˜a ◽  
F. Bellas ◽  
R. J. Duro ◽  
P. Farin˜as
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Irregular Sampling ◽  
Linear Filter ◽  
Signal Characteristics ◽  
Prediction Mode ◽  
Sampled Signal ◽  
Artificial Neural ◽  
Long Term Prediction

Artificial Neural Networks (ANNs) and evolution are applied to the analysis of turbulent signals. In a first instance, a new trainable delay based artificial neural network is used to analyze Hot Wire Anemometer (HW) signals obtained at different positions within the wake of a circular cylinder with Reynolds number values ranging from 2000 to 8000. Results show that these networks are capable of performing accurate short term predictions of the turbulent signal. In addition, the ANNs can be set in a long term prediction mode resulting in a sort of non linear filter able to extract the features having to do with the larger eddies and coherent structures. In a second stage these networks are used to reconstruct a regularly sampled signal straight from the irregularly sampled one provided by a Laser Doppler Anemometer (LDA). The irregular sampling dynamics of the LDA signals is governed by the arrival of the seeding particles, superimposing the already complex turbulent signal characteristics. To cope with this complexity, an evolutionary based strategy is used to perform an adaptive and continuous online training of the ANNs. This approach permits obtaining a regularly sampled signal not by interpolating the original one, as it is often done, but by modeling it.

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