Active Absorption of Random Waves in Wave Flume Using Artificial Neural Networks

Control Algorithm ◽

Wave Generation ◽

Reverse Direction ◽

Small Scale ◽

Wave Absorption ◽

Wave Flume ◽

Wave Channel ◽

Abstract A wave flume is primarily intended to reproduce actual sea conditions in order to provide a reliable means of testing for small scale models. The realization of scaled tests is extremely important for the validation of a project in real scale, since, through the laws of similitude, such tests make it possible to predict the behavior of structures in the ocean as well as their performance during operation. This research aims to develop, test and validate an active control algorithm for wave absorption in a 2D wave channel — that is, when the waves propagate in only one direction — based on artificial neural networks (ANN). The ANN control algorithm relies on the linear wave theory and the principle of time reversal of wave propagation, i.e. the phenomenon of wave absorption corresponds to the wave generation when observed in the reverse direction of time. Through this principle, data from wave generation experiments, after proper manipulation, are used to train an ANN capable of generating the control signal used to move the wave generator device, this time as a wave absorber.

Active Absorption of Random Waves in Wave Flume Using Artificial Neural Networks

10.1115/1.0001248v ◽

2021 ◽

Author(s):

Aureo Ramos ◽

Antonio Carlos Fernandes ◽

Vanessa M. Thomaz

Keyword(s):

Neural Networks ◽

Random Waves ◽

Active Absorption ◽

Wave Flume ◽

A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural networks

Computer Optics ◽

10.18287/2412-6179-co-826 ◽

2021 ◽

Vol 45 (2) ◽

pp. 277-285

Author(s):

A.V. Astafiev ◽

D.V. Titov ◽

A.L. Zhiznyakov ◽

A.A. Demidov

Keyword(s):

Neural Network ◽

Neural Networks ◽

Sensor Network ◽

Mobile Device ◽

Experimental Studies ◽

Test Sample ◽

Small Scale ◽

Approximation Function ◽

The paper considers the development of a method for positioning a mobile device using a sensor network of BLE-beacons, the approximation of RSSI values and artificial neural networks. The aim of the work is to develop a method for positioning small-scale industrial mechanization equipment for building unmanned systems for product movement tracking. The work is divided into four main parts: data synthesis, signal filtering, selection of BLE beacons, translation of the RSSI values into a distance, and multilateration. A simplified Kalman filter is proposed for filtering the input signal to suppress Gaussian noise. A description of two approaches to translating the RSSI value into a distance is given: an exponential approximation function with a coefficient of determination of 0.6994 and an artificial feedforward neural network. A comparison of the results of these approaches is carried out on several test samples: a training one, a test sample at a known distance (0–50 meters) and a test sample at an unknown distance (60–100 meters). The artificial neural network is shown to perform better in all experiments, except for the test sample at a known distance (0–50 meters), for which the r.m.s. error is higher by 0.02 m 2 than that for the approximation function, which can be neglected. An algorithm for positioning a mobile device based on the multilateration method is proposed. Experimental studies of the developed method have shown that the positioning error does not exceed 0.9 meters in a 5×5.5 m room under monitoring. The positioning accuracy of a mobile device using the proposed method in the experiment is 40.9 % higher. Experimental studies are also conducted in a 58.4×4.5 m room, showing more accurate results compared to similar studies.

Use of Artificial Neural Networks To Correct Computer Simulations of Small-Scale Propellers

AIAA AVIATION 2021 FORUM ◽

10.2514/6.2021-2498 ◽

2021 ◽

Author(s):

Lucas G. Souza ◽

Cristiane Martins ◽

Ney Sêcco

Keyword(s):

Neural Networks ◽

Computer Simulations ◽

Small Scale ◽

Flooding probability of urban area estimated by decision tree and artificial neural networks

Journal of Hydroinformatics ◽

10.2166/hydro.2008.009 ◽

2008 ◽

Vol 10 (1) ◽

pp. 57-67 ◽

Cited By ~ 5

Author(s):

Jeng-Chung Chen ◽

Ching-Sung Shu ◽

Shu-Kuang Ning ◽

Ho-Wen Chen

Keyword(s):

Neural Networks ◽

Small Scale ◽

Urban Drainage ◽

Rainfall Events ◽

Second Stage ◽

Downtown Area ◽

Observation Network ◽

Artificial Neural ◽

Flooding Probability

Remote sensing, such as from satellite, has been recognized as useful for monitoring the changes in hydrology. In this study, we propose a way that is able to estimate flooding probability based on satellite data from the observation network of the World Meteorological Organization. Through a two-stage probability analysis, we can depict the area with high flooding potential in near-real time. In the first stage, decision trees offered a prompt and rough estimation of the flooding probability; in the second stage, artificial neural networks handle the rainfall forecast in a small-scale area. Case studies, simulating two rainfall events on 20 May 2004 and 11 July 2001, proved that our proposed method is promising for mitigating the flooding damage along urban drainage within the downtown area of Kaohsiung city.

Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2009.08.008 ◽

2009 ◽

Vol 69 (2) ◽

pp. 198-208 ◽

Cited By ~ 20

Author(s):

Daniel Jiménez ◽

James Cock ◽

Héctor F. Satizábal ◽

Miguel A. Barreto S ◽

Andrés Pérez-Uribe ◽

...

Keyword(s):

Neural Networks ◽

Meteorological Data ◽

Small Scale ◽

Production Models ◽

Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm

Advances in Mechanical Engineering ◽

10.1177/16878140211009009 ◽

2021 ◽

Vol 13 (3) ◽

pp. 168781402110090

Author(s):

Jelena Svorcan ◽

Ognjen Peković ◽

Aleksandar Simonović ◽

Dragoljub Tanović ◽

Mohammad Sakib Hasan

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Wind Turbines ◽

Vertical Axis ◽

Urban Environments ◽

Small Scale ◽

Optimal Flow ◽

Vertical Axis Wind Turbines ◽

Wind energy extraction is one of the fastest developing engineering branches today. Number of installed wind turbines is constantly increasing. Appropriate solutions for urban environments are quiet, structurally simple and affordable small-scale vertical-axis wind turbines (VAWTs). Due to small efficiency, particularly in low and variable winds, main topic here is development of optimal flow concentrator that locally augments wind velocity, facilitates turbine start and increases generated power. Conceptual design was performed by combining finite volume method and artificial intelligence (AI). Smaller set of computational results (velocity profiles induced by existence of different concentrators in flow field) was used for creation, training and validation of several artificial neural networks. Multi-objective optimization of concentrator geometric parameters was realized through coupling of generated neural networks with genetic algorithm. Final solution from the acquired Pareto set is studied in more detail. Resulting computed velocity field is illustrated. Aerodynamic performances of small-scale VAWT with and without optimal flow concentrator are estimated and compared. The performed research demonstrates that, with use of flow concentrator, average increase in wind speed of 20%–25% can be expected. It also proves that contemporary AI techniques can significantly facilitate and accelerate design processes in the field of wind engineering.