Facing Losses of Telemetric Signal in Real Time Forecasting of Water Level using Artificial Neural Networks

2021 ◽  
Vol 35 (3) ◽  
pp. 1119-1133
Author(s):  
Juliano Santos Finck ◽  
Olavo Correa Pedrollo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Real Time ◽  
Artificial Neural

A Model for Real-Time Hand Gesture Recognition Using Electromyography (EMG), Covariances and Feed-Forward Artificial Neural Networks

2020 ◽  
Author(s):  
Marco E. Benalcazar ◽  
Jose Gonzalez ◽  
Andres Jaramillo-Yanez ◽  
Carlos E. Anchundia ◽  
Patricio Zambrano ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Feed Forward ◽  
Artificial Neural

MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES

2021 ◽  
pp. 14-22
Author(s):  
G. N. KAMYSHOVA ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Intelligent Control ◽  
Machine Learning Algorithms ◽  
Intelligent Control Systems ◽  
Artificial Neural ◽  
The One ◽  
Neural Predictive Control

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.

scholarly journals Climate change or regional human impacts? remote sensing tools, artificial neural networks, and wavelet approaches aim to solve the problem

2020 ◽  
Author(s):  
Ehsan Foroumandi ◽  
Vahid Nourani ◽  
Elnaz Sharghi
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Positive Trend ◽  
Water Level Change ◽  
Lake Urmia ◽  
Level Change ◽  
Artificial Neural

Abstract Lake Urmia, as the largest lake in Iran, has suffered from water-level decline and this problem needs to be investigated accurately. The major reason for the decline is controversial. The current paper aimed to study the hydro-environmental variables over the Lake Urmia basin using remote sensing tools, artificial neural networks, wavelet transforms, and Mann–Kendall trend tests from 1995 to 2019 in order to determine the primary reason of the decline and to find the most important hydrologic periodicities over the basin. The results indicated that for the monthly-, seasonally-, and annually-based time series, the components with 4-month and 16-month, 24- and 48-month, and 2- and 4-year, respectively, are the most dominant periodicities over the basin. The agricultural increase according to the vegetation index and evapotranspiration and their close relationship with the water-level change indicated that human land-use is the main reason for the decline. The increasing agriculture, in the situations that the precipitation has not increased, caused the inflow runoff to the lake to decline and the remaining smaller discharge is not sufficient to stabilize the water level. Temperature time series, also, has experienced a significant positive trend which intensified the water-level change.

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

2014 ◽  
Vol 33 (6) ◽  
pp. 419-432 ◽  
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

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

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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