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1

Hybrid wavelet transform - MLR and ANN models for river flow prediction: Case study of Brahmaputra river (Pancharatna station)

Khandekar Sachin Dadu, Aswar Dinesh Shrikrishna, Sabale Pandurang Digamber, Khandekar Varsha Sachin, Bajad Mohankumar Namdeorao, Khaple Shivakumar

Przegląd Naukowy Inżynieria i Kształtowanie Środowiska

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2024

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Vol. 33, No. 1

69--94

EN

In this research, discrete wavelet transform (DWT) is combined with MLR and ANN to develop WMLR and WANN hybrid models, respectively, for the Brahmaputra river (Pancharatna station) flow forecasting. Daily flow data for the period of 10 year were decomposed (up to fifth level) into detailed and approximation coefficients (using Daubechies wavelets db1, db2, db3, db8 and db10) which were fed as input to MLR and ANN to get the predicted discharge values two days, four days, seven days and 14 days ahead. For all lead times, the WMLR-db10 model was found to be superior as compared to WANN-db1, WANN-db2, WANN-db3, WANN-db8, WMLR-db1, WMLR-db2, WMLR-db3, WMLR-db8 and single MLR and ANN models. During testing period, the values of determination coefficient (R2) and RMSE for WMLR-db10 model for two-, four-, seven- and 14-day lead time were found to be, respectively, 0.996 (751.87 m3·s–1), 0.991 (1,174.80 m3·s–1), 0.984 (1,585.02 m3·s–1), and 0.968 (2,196.46 m3·s–1). Also, it was observed that for lower order wavelets (db1, db2, db3) WANN’s performance was better, and for higher order wavelets (db8, db10) WMLR’s performance was better. Correspondingly, it was observed that all hybrid models’ efficiency increased with increase in the decomposition level.

2

Application of artificial neural networks for the prediction of the service conditions of an elastohydrodynamic EHL contact in the presence of solid pollutant

Mattallah Sabrina, Kelaiaia Ridha, Louahem M’Sabah Hanane, Kerboua Adlen

Diagnostyka

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2024

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Vol. 25, No. 1

art. no. 2024107

EN

Lubricated mechanical mechanisms operate under service conditions influenced by several environmental parameters, and their life times may be threatened due to inappropriate use or by the presence of solid contaminants. The objective of this work is to study the effect of three operating parameters, namely: rotational speed 𝑉, load 𝑄 and kinematic viscosity 𝜈 in the presence of three sizes of solid contaminants 𝑇, on the degradation of an EHL contact, to predict the ranges of effects that may lead to the damage of the contacting surfaces. In our investigation, anexperimental design of nine trials is used to combine four factors with three levels each to accomplish the experimental investigation. Artificial neural network regression and the desirability function were used for the interpretation and modelling of the responses, whichare: wear 𝑊, arithmetic mean height 𝑅𝑎, total profile height 𝑅𝑡 and maximum profile height 𝑅𝑧. From these methods we observed that the sand grain sizes have a significant impact on the wear 𝑊 and the roughness 𝑅𝑎, but that viscosity has the primary influence on the variation of the roughnesses 𝑅𝑡 and 𝑅𝑧. We also found that the quality of the predicted models is very good, with overall determination coefficients of 𝑅2 learning = 0.9985 and 𝑅2 validation = 0.9996. Several levels of degradation depending on the operating conditions are predicted using the desirability function.

3

Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

Falkowicz Katarzyna, Kulisz Monika

Advances in Science and Technology. Research Journal

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2024

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Vol. 18, no 1

231--243

EN

This article presents the use of Artificial Neural Networks (ANNs) to analysis of the composite plate elements with cut-outs which can work as a spring element. The analysis were based on results from numerical approach. ANNs models have been developed utilizing the obtained numerical data to predict the composite plate’s flexural-torsional form of buckling as natural form for different cut-outs and angels configurations. The ANNs models were trained and tested using a large dataset, and their accuracy is evaluated using various statistical measures. The developed ANNs models demonstrated high accuracy in predicting the critical force and buckling form of thin-walled plates with different cut-out and fiber angels configurations under compression. The combination of numerical analyses with ANNs models provides a practical and efficient solution for evaluating the stability behaviour of composite plates with cut-outs, which can be useful for design optimization and structural monitoring in engineering applications.

4

Research and applications of artificial neural networks in spatial analysis: Review

Garczyńska Ilona

Zeszyty Naukowe Akademii Morskiej w Szczecinie

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2023

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nr 74 (146)

35--45

EN

The conducted review presents the possibility of using artificial neural networks in sectors related to environmental protection, agriculture, forestry, land uses, groundwater and bathymetric. Today there is a lot of research in these areas with different research methodologies. The result is the improvement of decision-making processes, design, and prediction of certain events that, with appropriate intervention, can prevent severe consequences for society. The review shows the capabilities to optimize and automate the processes of modeling urban and land dynamics. It examines the forecasts of assessment of the damage caused by natural phenomena. Detection of environmental changes via the analysis of certain time intervals and classification of objects on the basis of different images is presented. The practical aspects of this work include the ability to choose the correct artificial neural network model depending on the complexity of the problem. This factor is a novel element since previously reviewed articles did not encounter a study of the correlation between the chosen model or algorithm, depending on the use case or area of the problem. This article seeks to outline the reason for the interest in artificial intelligence. Its purpose is to find answers to the following questions: How can artificial neural networks be used for spatial analysis? What does the implementation of detailed algorithms depend on? It is proved that an artificial intelligence approach can be an effective and powerful tool in various domains where spatial aspects are important.

5

Modern method based on artificial intelligence for safe control in the marine environment

Mohamed-Seghir Gdynia Maritime University, Gdynia, Poland

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2023

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Vol. 17 No. 2

283--288

EN

The article presents an approach to formulating a ship control process model in order to solve the problem of determining a safe ship trajectory in collision situations. Fuzzy process properties are included in the model to bring it closer to reality, as in many situations the navigator makes a subjective decision. A special neural network was used to solve the presented problem. This artificial neural network is characterized by minimum and maximum operations when set. In order to confirm the correctness of the operation of the proposed algorithm, the results of the simulations obtained were presented and an discussion was conducted.

6

The modelling of layered rocks using a numerical homogenisation technique and an artificial neural network

Urbański Aleksander, Ligęza Szymon, Przecherski Piotr

Technical Transactions

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2023

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Vol. 120, iss. 1

art. no. e2023007

EN

A method of creating a constitutive model of layered rocks based on an artificial neural network (ANN) is reported in this work. The ANN gives an implicit constitutive function Ʃⁿ⁺¹= F( Ʃⁿ , ΔE), relating the new state of homogenized stresses Ʃⁿ⁺¹ with the old state Ʃⁿ and with the increment of homogenized strains ΔƩ. The first step is to repeatedly run a strain- controlled homogenisation on an uni-dimensional finite element model of a periodic cell with elastic-plastic models (Drucker-Prager) of the components. Paths are created in (Ʃ, E) space, from which, a set of patterns is formed to train the ANN. A description of how to prepare this data and a discussion on ANN training issues are presented. Finally, the procedure based on trained ANN is put into a finite-element code (ZSoil.PC) as a user-delivered constitutive function. The approach is verified by comparing the results of the developed model basing on ANN with a direct (single-scale) analysis, which showed acceptable accuracy.

7

A Novel Method for Optimizing Parameters influencing the Bearing Capacity of Geosynthetic Reinforced Sand Using RSM, ANN, and Multiobjective Genetic Algorithm

Lafifi Brahim, Rouaiguia Ammar, Soltani El Alia

Studia Geotechnica et Mechanica

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2023

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Vol. 45, nr 2

174--196

EN

In this study, a novel method is proposed to optimize the reinforced parameters influencing the bearing capacity of a shallow square foundation resting on sandy soil reinforced with geosynthetic. The parameters to be optimized are reinforcement length (L), the number of reinforcement layers (N), the depth of the topmost layer of geosynthetic (U), and the vertical distance between two reinforcement layers (X). To achieve this objective, 25 laboratory small-scale model tests were conducted on reinforced sand. This laboratory-scale model has used two geosynthetics as reinforcement materials and one sandy soil. Firstly, the effect of reinforcement parameters on the bearing load was investigated using the analysis of variance (ANOVA). Both response surface methodology (RSM) and artificial neural networks (ANN) tools were applied and compared to model bearing capacity. Finally, the multiobjective genetic algorithm (MOGA) coupled with RSM and ANN models was used to solve multi objective optimization problems. The design of bearing capacity is considered a multi-objective optimization problem. In this regard, the two conflicting objectives are the need to maximize bearing capacity and minimize the cost. According to the obtained results, an informed decision regarding the design of the bearing capacity of reinforced sand is reached.

8

Performance enhancement of pv system using artificial neural network (ANN) as a maximum power point tracking

Bensiali Nadia, Benalia Nadia, Zerzouri Nora

Przegląd Elektrotechniczny

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2023

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R. 99, nr 4

262--265

EN

Since the generalization of the use of electricity, energy consumption, conversion and energy storage has led to research and development of new sources of supply. This interest has increased due to the depletion of fossil fuels, their impact on the environment and the waste they generate. Photovoltaic (PV) technology is an attractive solution as a replacement or complementary to conventional sources of electricity supply due to its many advantages. This work focuses on studying, modeling and designing of an advanced controller for a step up converter that uses an artificial neural network (ANN) as a maximum power point tracking (MPPT) algorithm to generate maximum power. The effectiveness was proved for climatic conditions variation. A comparative study between the proposed method and the conventional ones was carried out using Matlab/Simulink software.

PL

Od czasu uogólnienia wykorzystania energii elektrycznej jej zużycie, konwersja i magazynowanie energii doprowadziły do badań i rozwoju nowych źródeł zaopatrzenia. Zainteresowanie to wzrosło ze względu na wyczerpywanie się paliw kopalnych, ich wpływ na środowisko i generowane przez nie odpady. Technologia fotowoltaiczna (PV) jest atrakcyjnym rozwiązaniem jako zamiennik lub uzupełnienie konwencjonalnych źródeł zaopatrzenia w energię elektryczną ze względu na swoje liczne zalety. Praca ta koncentruje się na badaniu, modelowaniu i projektowaniu zaawansowanego sterownika do przetwornicy podwyższającej, która wykorzystuje sztuczną sieć neuronową (ANN) jako algorytm śledzenia punktu mocy maksymalnej (MPPT) do generowania maksymalnej mocy. Skuteczność została wykazana przy zmienności warunków klimatycznych. Badanie porównawcze proponowanej metody z metodami konwencjonalnymi przeprowadzono z wykorzystaniem oprogramowania Matlab/Simulink.

9

Investigation of the possibility of using artificial neural networks in the process of assessing the threat of lightning

Sobieska Emilia, Sobolewski Konrad

Przegląd Elektrotechniczny

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2023

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R. 99, nr 1

211--214

EN

The article presents issues related to the possibility of using an artificial neural network to classify factors describing the lightning hazard. It also analyzed the selection of the most optimal type of neural network for this type of task, as well asthe issue of input data, which both teach the neural network and form the basis for the final classification. Finally, the conclusions and assumptions necessary for the practical implementation of the task were drawn. The practical part of the task is planned as the following consideration stage.

PL

W artykule została przedstawiona problematyka związana z możliwością wykorzystania sztucznej sieci neuronowej do przeprowadzenia klasyfikacji współczynników opisujących zagrożenie piorunowe. Przeanalizowano wybór najbardziej optymalnego rodzaju sieci neuronowej dla tego rodzaju zadania, a także kwestię danych wejściowych, które posłużą zarówno do uczenia sieci neuronowej, jak również stanowią podstawę do końcowej klasyfikacji. Na koniec wyciągnięte zostały wnioski oraz założenia niezbędne do praktycznej realizacji zadania. Praktyczna część zadania będzie stanowić kolejny etap rozważań.

10

Comparative analysis of optimization value between artificial neural network and long short-term memory for prediction particulate matter (PM2.5) in Bangkok Thailand

Atiwanwong Thanpitcha, Jantakun Adirek, Sangsongfa Adisak

Przegląd Elektrotechniczny

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2023

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R. 99, nr 7

89--95

EN

The crisis that the general public is worried about is particulate matter as small as 2.5 microns, which is invisible to the naked eye, causing a great lack of awareness of health hazards. One of the key goals and visions of government leaders around the world is to tackle PM2.5 particulate matter, but without measurements, reports and predictions, how will it lead to emission reduction and remedial steps? Therefore, the prediction of PM2.5 is considered as the main factor that will help to reduce the pollution of PM2.5. So, Neural networks have been widely used in predictive research, but the problem is What type of neural network would be most suitable for predicting the value of PM2.5? In this research, the predictions were compared between Artificial Neural Network (ANN) and Long Short -Term Memory (LSTM) using values measured from the performance test results with accuracy. The results showed that when the values of similar hyperparameters were given different results, the average ANN accuracy is 91.1460%. The average accuracy LSTM is 96.8496%. The values obtained from the comparison clearly show that for the prediction of PM2.5, the LSTM neural network was significantly more suitable than the ANN neural network.

PL

Kryzys, który niepokoi opinię publiczną, to pył zawieszony o wielkości zaledwie 2,5 mikrona, który jest niewidoczny gołym okiem, powodując ogromny brak świadomości zagrożeń dla zdrowia. Jednym z kluczowych celów i wizji przywódców rządów na całym świecie jest rozwiązanie problemu pyłu zawieszonego PM2,5, ale bez pomiarów, raportów i prognoz, w jaki sposób doprowadzi to do redukcji emisji i działań zaradczych? Dlatego prognoza PM2,5 jest uważana za główny czynnik, który pomoże zmniejszyć zanieczyszczenie PM2,5. Tak więc sieci neuronowe były szeroko stosowane w badaniach predykcyjnych, ale problem polega na tym, jaki typ sieci neuronowej byłby najbardziej odpowiedni do przewidywania wartości PM2,5? W tym badaniu porównano przewidywania między sztuczną siecią neuronową (ANN) a pamięcią długokrótkoterminową (LSTM) przy użyciu wartości zmierzonych z wynikami testu wydajności z dużą dokładnością. Wyniki pokazały, że przy różnych wartościach podobnych hiperparametrów średnia dokładność ANN wynosi 91,1460%. Średnia dokładność LSTM wynosi 96,8496%. Uzyskane z porównania wartości jednoznacznie wskazują, że do predykcji PM2,5 sieć neuronowa LSTM okazała się znacznie bardziej odpowiednia niż sieć neuronowa ANN.

11

Autonomiczna kamera do wykrywania wolnych miejsc parkingowych

Lamża Aleksander

Przegląd Elektrotechniczny

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2023

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R. 99, nr 4

192--195

PL

W artykule została opisana architektura systemu autonomicznej kamery do wykrywania wolnych miejsc parkingowych. System został zrealizowany i przetestowany zarówno w warunkach laboratoryjnych, jak i rzeczywistych. Omówione zostały kluczowe elementy systemu, ich współdziałanie, a także możliwości modyfikacji i rozwoju.

EN

The article describes the architecture of an autonomous camera system to detect free parking spaces. The system was implemented and tested both in laboratory and real conditions. The key elements of the system, their interaction, as well as the possibilities of modification and development were discussed.

12

Using Artificial Neural Networks for predicting ship fuel consumption

Nguyen Van Giao, Sakthivel Rajamohan, Rudzik Krzysztof, Kozak Janusz, Sharma Prabhakar, Pham Nguyen Dang Khoa, Nguyen Phuoc Quy Phong, Nguyen Xuan Phuong

Polish Maritime Research

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2023

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nr 2

39--60

EN

In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.

13

Artificial neural network (ANN) modelling to estimate bubble size from macroscopic image and object features

Vinnett Luis, León Roberto, Mesa Diego

Physicochemical Problems of Mineral Processing

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2023

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Vol. 59, iss. 5

art. no. 185759

EN

Bubble size measurements in aerated systems such as froth flotation cells are critical for controlling gas dispersion. Commonly, bubbles are measured by obtaining representative photographs, which are then analyzed using segmentation and identification software tools. Recent developments have focused on enhancing these segmentation tools. However, the main challenges around complex bubble cluster segmentation remain unresolved, while the tools to tackle these challenges have become increasingly complex and computationally expensive. In this work, we propose an alternative solution, circumventing the need for image segmentation and bubble identification. An Artificial Neural Network (ANN) was trained to estimate the Sauter mean bubble size (D32) based on macroscopic image features obtained with simple and inexpensive image analysis. The results showed excellent prediction accuracy, with a correlation coefficient, R, over 0.998 in the testing stage, and without bias in its error distribution. This machine learning tool paves the way for robust and fast estimation of bubble size under complex bubble images, without the need of image segmentation.

14

Estimation of zooplankton density with artificial neural networks (a new statistical approach) method, Elazığ-Türkiye

Bulut Hilal

Oceanological and Hydrobiological Studies

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2023

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Vol. 52, No. 4

502--515

EN

This study was carried out to predict the zooplankton density in the Cip reservoir (Elazığ) with an artificial neural network, using some water quality parameters. The plankton samples were collected monthly from Cip Reservoir in 2021- 2022, using a standard plankton net from three stations. Water temperature, dissolved oxygen, pH, electrical conductivity, secchi disk, alkalinity, total nitrogen and total phosphorus were measured. The actual values of zooplankton density and results obtained from the artificial neural networks were compared. Mean absolute percent error (MAPE) values were calculated with actual values and ANNs values. ANNs values were determined to be close to the real data. MAPE percentage value at the first station was determined as 1.143 for Rotifer, 0.118 for Cladocera, and 0.141 for Copepoda. The MAPE percentage value at the second station was determined as 0.941 for Rotifer, 0.377 for Cladocera, and 0.185 for Copepoda. The MAPE percentage value at the third station was determined as 0.342 for Rotifer, 0.557 for Cladocera, and 0.301 for Copepoda. In the present study, it has been seen that artificial neural networks with a learning feature are successful in predicting zooplankton densities in an aquatic environment. It can be concluded from the study that ANNs are a powerful tool for understanding their relationships with the environment.

15

Wykorzystanie sieci neuronowych do modelowania pracy młyna kulowego w Cementowni WARTA S.A.

Śnieżyński Bartłomiej, Gajewski Paweł, Stępień Piotr, Roszczynialski Wojciech K., Golonka Paulina, Wolski Grzegorz

Materiały Budowlane

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2023

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nr 8

12--17

PL

Zastosowanie Sztucznych Sieci Neuronowych (SSN) do sterowania procesem przemiału cementu jest w pełni uzasadnione ze względu na złożoność procesu mielenia oraz nieliniowość charakteryzujących go parametrów. Stabilna praca młyna uzyskana przy wsparciu samouczących się SSN może przełożyć się na minimalizację jednostkowego zużycia energii przy utrzymaniu właściwego stopnia rozdrobnienia. Jako dane wejściowe zasilające algorytm SSN wykorzystano wybrane parametry technologiczne monitorowane podczas pracy młyna kulowego pracującego w warunkach przemysłowych. Eksperymenty wykazały, że mały błąd predykcji dają modele uwzględniające małą liczbę parametrów, biorące pod uwagę dane wejściowe z krótszego okna czasowego i 30-minutowym oknem wygładzania danych wejściowych. Najlepsze konfiguracje sieci neuronowej pozwalają na predykcję parametrów pracy młyna ze średnim bezwzględnym błędem procentowym poniżej 5% dla horyzontu czasowego 10 min oraz poniżej 7% dla horyzontu czasowego 15 min.

EN

The use of Artificial Neural Networks (ANNs) to control the cement grinding process is fully justified, taking into account the complexity of the grinding process and the non-linearity of its parameters. Stable operation of the mill, obtained with the support of self-learning ANNs, may translate into minimization of unit energy consumption while maintaining the desired degree of fragmentation. As input data powering the ANN algorithm, selected technological parameters monitored during the operation of the ball mill in an industrial setting were used. Experiments have shown that models with a smaller number of parameters, taking into account input data from a shorter time window and a 30-minute input smoothing window, yield a smaller prediction error. The best configurations of the neural network allow for the prediction of the mill operation parameters with an average absolute percentage error of less than 5% for the time horizon of 10 minutes and less than 7% for the time horizon of 15 minutes.

16

Artificial neural network and energy budget method to predict daily evaporation of Boudaroua reservoir (northern Morocco)

En-nkhili Hicham, Nizar Imane, Igouzal Mohammed, Touazit Azzeddin, Youness Nizar, Etebaai Issam

Journal of Water and Land Development

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2023

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no. 57

107--115

EN

Evaporation is one of the main essential components of the hydrologic cycle. The study of this parameter has significant consequences for knowing reservoir level forecasts and water resource management. This study aimed to test the three artificial neural networks (feed-forward, Elman and nonlinear autoregressive network with exogenous inputs (NARX) models) and multiple linear regression to predict the rate of evaporation in the Boudaroua reservoir using the calculated values obtained from the energy budget method. The various combinations of meteorological data, including solar radiation, air temperature, relative humidity, and wind speed, are used for the training and testing of the model’s studies. The architecture that was finally chosen for three types of neural networks has the 4-10-1 structure, with contents of 4 neurons in the input layer, 10 neurons in the hidden layer and 1 neuron in the output layer. The calculated evaporation rate presents a typical annual cycle, with low values in winter and high values in summer. Moreover, air temperature and solar radiation were identified as meteorological variables that mostly influenced the rate of evaporation in this reservoir, with an annual average equal to 4.67 mm∙d-1. The performance evaluation criteria, including the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) approved that all the networks studied were valid for the simulation of evaporation rate and gave better results than the multiple linear regression (MLR) models in the study area.

17

Designing Smart Antennas Using Machine Learning Algorithms

Samantaray Barsa, Das Kunal Kumar, Roy Jibendu Sekhar

Journal of Telecommunications and Information Technology

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2023

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nr 4

46--52

EN

Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.

18

Predicting Young’s modulus of Indian coal measure rock using multiple regression and artificial neutral network

Chakraborty Sayantan, Bisai Rohan, Roy Rohit, Palaniappan Sathish Kumar, Pal Samir Kumar, Rao Karanam Uma Maheshwar

Journal of Sustainable Mining

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2023

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Vol. 22, iss. 1

41--54

EN

Accurate information on Young’s modulus (E) is required for simulating rock deformation in mines; on the other hand, it is very cumbersome to obtain in the laboratory and collecting drilled cores in sufficient amounts, especially in the case of soft rocks, is quite impossible. Empirical equations were deducted for - from easily determinable rock properties, and the final model was selected through different statistical strength parameter tests. The generalization of the equation was verified through the normal distribution tests of residues of the equation. R2 came to be 0.609 and was validated using an artificial neural network with an improved value of 0.73.

19

An investigation of the relationship between encoder difference and thermo-elastic machine tool deformation

Brecher Christian, Dehn Mathias, Neus Stephan

Journal of Machine Engineering

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2023

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Vol. 23, No. 3

26--37

EN

New approaches, using machine learning to model the thermo-elastic machine tool error, often rely on machine internal data, like axis speed or axis position as input data, which have a delayed relation to the thermo-elastic error. Since there is no direct relation to the thermo-elastic error, this can lead to an increased computation inaccuracy of the model or the need for expensive sensor equipment for additional input data. The encoder difference is easy to obtain and has a direct relationship with the thermo-elastic error and therefore has a high potential to improve the accuracy thermo-elastic error models. This paper first investigates causes of the encoder difference and its relationship with the thermo-elastic error. Afterwards, the model is presented, which uses the encoder difference to compute the thermo-elastic error. Due to the complexity of the relationship, it is necessary, to use a machine learning approach for this. To conclude, the potential of the encoder difference as an input of the model is evaluated.

20

The Effect of Nanomaterial Type on Water Disinfection Using Data Mining

Hamdan Mohammad, Khalil Rana Haj, Abdelhafez Eman, Ajib Salman

Journal of Ecological Engineering

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2023

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Vol. 24, nr 4

244--251

EN

Multiple linear regression and artificial neural network (ANN) models were utilized in this study to assess the type influence of nanomaterials on polluted water disinfection. This was accomplished by estimating E. coli (E.C) and the total coliform (TC) concentrations in contaminated water while nanoparticles were added at various concentrations as input variables, together with water temperature, PH, and turbidity. To achieve this objective, two approaches were implemented: data mining with two types of artificial neural networks (MLP and RBF), and multiple linear regression models (MLR). The simulation was conducted using SPSS software. Data mining was revealed after the estimated findings were checked against the measured data. It was found that MLP was the most promising model in the prediction of the TC and E.C concentration, s followed by the RBF and MLR models, respectively.