Air quality forecasting using artificial neural networks with real time dynamic error correction in highly polluted regions

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

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Real time road sign recognition system using artificial neural networks for bengali textual information box

2008 International Symposium on Information Technology ◽

10.1109/itsim.2008.4631688 ◽

2008 ◽

Cited By ~ 7

Author(s):

Mohammad Osiur Rahman ◽

Fouzia Asharf Mousumi ◽

Edgar Scavino ◽

Aini Hussain ◽

Hassan Basri

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Real Time ◽

Recognition System ◽

Textual Information ◽

Sign Recognition ◽

Road Sign ◽

Road Sign Recognition ◽

Artificial Neural

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

Revista Brasileira de Climatologia ◽

10.5380/abclima.v13i0.33066 ◽

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.

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Real-Time Pricing and Hedging of Options on Currency Futures with Artificial Neural Networks

Journal of Forecasting ◽

10.1002/for.2311 ◽

2014 ◽

Vol 33 (6) ◽

pp. 419-432 ◽

Cited By ~ 9

Author(s):

Christian von Spreckelsen ◽

Hans-Jörg von Mettenheim ◽

Michael H. Breitner

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Real Time ◽

Currency Futures ◽

Artificial Neural ◽

Real Time Pricing

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Real-Time Recoloring Ishihara Plates Using Artificial Neural Networks for Helping Colorblind People

Advances in Systems Analysis, Software Engineering, and High Performance Computing - User-Centered Software Development for the Blind and Visually Impaired ◽

10.4018/978-1-5225-8539-8.ch009 ◽

2020 ◽

pp. 138-156

Author(s):

Martín Montes Rivera ◽

Alejandro Padilla ◽

Juana Canul-Reich ◽

Julio Ponce

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Artificial Neural Networks ◽

Real Time ◽

Input Data ◽

Traffic Signals ◽

Educational Materials ◽

Ishihara Test ◽

Artificial Neural ◽

Intelligence Technique

Vision sense is achieved using cells called rods (luminosity) and cones (color). Color perception is required when interacting with educational materials, industrial environments, traffic signals, among others, but colorblind people have difficulties perceiving colors. There are different tests for colorblindness like Ishihara plates test, which have numbers with colors that are confused with colorblindness. Advances in computer sciences produced digital assistants for colorblindness, but there are possibilities to improve them using artificial intelligence because its techniques have exhibited great results when classifying parameters. This chapter proposes the use of artificial neural networks, an artificial intelligence technique, for learning the colors that colorblind people cannot distinguish well by using as input data the Ishihara plates and recoloring the image by increasing its brightness. Results are tested with a real colorblind people who successfully pass the Ishihara test.

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