Wyniki wyszukiwania - BazTech

1

Simulation analysis of artificial neural network and XGBoost algorithms in time series forecasting

Sroka Łukasz

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2024

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z. 195

561--575

EN

Purpose: The aim of the article was to prepare a simulation analysis of artificial neural network and XGBoost algorithm with determining which of the method was characterized by a lower level of forecast errors for time series predictions. Design/methodology/approach: The objective of the article was reached by applying, a simulation study on a sample of 1000 artificially generated time series. The analyzed XGBoost algorithm and the artificial neural network ANN model were intended to prepare forecasts for five periods ahead. These forecasts were compared with the actual implementations of the time series and proposed forecast error measures. Findings: It is possible to use simulated time series to check which of the presented algorithms were characterized by a lower forecast error. The study showed that applying of the artificial neural networks ANN to forecast future observations generated a lower level of MAPE, MAE and RMSE errors than in the case of the XGBoost algorithm. It was found that both methods generate a lower level of forecast error for time series characterized by a high level of mean value, standard deviation and variance, and levels of kurtosis and skewness close to 0. Practical implications: The research results can be used by both investors and enterprises to better adjust their business decisions to changing market prices by using a model with a lower forecast bias. Originality/value: The original contribution of this article is a comprehensive comparison of forecasts generated by the XGBoost and ANN algorithm, along with determining for which types of time series of the algorithms forecast future values with less error. Moreover, due to the use of simulated artificial time series, it was possible to test each algorithm for various market conditions.

2

Artificial intelligence-powered pulse sequences in nuclear magnetic resonance and magnetic resonance imaging: historical trends, current innovations and perspectives

Tokarz Paweł

Scientiae Radices

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2024

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Vol. 3, Iss. 1

30--52

EN

This review article explores the historical background and recent advances in the application of artificial intelligence (AI) in the development of radiofrequency pulses and pulse sequences in nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI). The introduction of AI into this field, which traces back to the late 1970s, has recently witnessed remarkable progress, leading to the design of specialized frameworks and software solutions such as DeepRF, MRzero, and GENETICS-AI. Through an analysis of literature and case studies, this review tracks the transformation of AI-driven pulse design from initial proof-of-concept studies to comprehensive scientific programs, shedding light on the potential implications for the broader NMR and MRI communities. The fusion of artificial intelligence and magnetic resonance pulse design stands as a promising frontier in spectroscopy and imaging, offering innovative enhancements in data acquisition, analysis, and interpretation across diverse scientific domains.

3

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.

4

Predicting open-pit mine production using machine learning techniques

Kumah Faustin Nartey, Alex Kwasi Saim, Oppong Millicent Nkrumah, Arthur Clement Kweku

Journal of Sustainable Mining

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2024

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Vol. 23, iss. 2

118--131

EN

In mining, where production is affected by several factors, including equipment availability, it is necessary to develop reliable models to accurately predict mine production to improve operational efficiency. Hence, in this study, four (4) machine learning algorithms - namely: artificial neural network (ANN), random forest (RF), gradient boosting regression (GBR) and decision tree (DT)) - were implemented to predict mine production. Multiple Linear Regression (MLR) analysis was used as a baseline study for comparison purposes. In that regard, one hundred and twenty-six (126) datasets from an open-pit gold mine were used. The developed models were evaluated and compared using the correlation coefficient (R2), mean absolute percentage error (MAPE) and variance accounted for (VAF). It has been shown in this study that the ANN model can best estimate open-pit mine production by comparing its performance to that of the other machine learning models. The R2, MAPE, RMSE and VAF of the models were 0.8003, 0.7486, 0.7519, 0.6538, 0.6044, 4.23%, 5.07%, 5.44%, 6.31%, 6.15% and 79.66%, 74.69%, 74.10%, 65.16% and 60.11% for ANN, RF, GBR, DT and MLR, respectively. Overall, this study has shown that machine learning algorithms predict mine production with higher accuracy.

5

Modeling of Dispersed Red 17 Dye Removal from an Aqueous Solution Using Artificial Neural Network

Ibrahim Abdullah I., Asmel Nabel K., Alabdraba Waleed M. S., Al-Nima Raid R. O.

Journal of Ecological Engineering

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2024

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Vol. 25, nr 2

10--19

EN

A significant amount of hazardous compounds has leaked into the environment due to the widespread usage of organic dyes, and it is essential that these dangerous contaminants be removed in a sustainable way. This study used varying amounts of H2O2 (0, 0.5, 1.5, 3, and 5) mM/L to extract the dye from the aqueous solution. Furthermore, concentrations of 0.4, 1, 1.7, and 2.3 mM/L of Fe+2 as FeSO4•7H2O were also utilized. Batch Advanced Oxidation Process (AOP) was carried out under various working conditions, including: contact time (5–60 min), mixing speed (100–300 rpm), and UV light intensity (0–40 W). Utilizing experimental data, the AOP efficiency of Dispersed Red 17 Dye was calculated. Genetic Cascade-forward Neural Network (GCNN) was employed as a machine-learning tool to forecast the oxidation efficiency and the amount of dye that would be removed from the aqueous solution, specifically Dispersed Red 17. When compared to experimental data, the best model had an R2 correlation value of 0.955. The findings of the importance analysis showed that the studied parameters affected the discoloration efficiency with order of: H2O2, UV, Fe+2, mixing speed, and contact time. The obtained results demonstrated the effectiveness of GCNN as a novel approach in forecasting the AOP efficiency of Dispersed Red 17 Dye.

6

Estimation of Photovoltaic Module Performance with L-Shaped Aluminum Fins Using Weather Data

Hamdan Mohammad, Abdelhafez Eman, Musa Akram, Ajib Salman

Journal of Ecological Engineering

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2024

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Vol. 25, nr 1

336--344

EN

Photovoltaic (PV) power prediction is vital for efficient and effective solar energy utilization within the energy ecosystem. It enables grid stability, cost savings, and the seamless integration of solar power into the broader energy infrastructure. In this work, previously obtained data on the estimation of the power produced by a PV, which is cooled by L-shaped aluminum fins attached to the backside of the PV at different spacings, is used to predict the power produced by the PV. This is achieved by employing both neural network models and multiple linear regression (MLR) techniques to assess the correlation between power generated by PV with L-shaped aluminum fins and its input variables. Two distinct approaches were employed for this purpose. The first approach involved the conventional MLR model, while the second utilized a neural network, specifically the multilayer perceptron (MLP) model. The estimated outcomes were subsequently compared against the previously measured data. The MLP model showed a great ability to identify the relationship between input and output variables, it was noted. The statistical error study provided evidence of data mining’s acceptable accuracy when using the MLP model. Conversely, the results indicated that the MLR technique exhibited the least ability to estimate the power generated by PV with L-shaped aluminum fins.

7

Prediction of Underground Mine’s Surface Subsidence using a Recursive Multi-Step Forecasting Model with an Artificial Neural Network

Le Tam Thanh Thi, Nguyen Trong Gia, Pham Chung Van, Le Canh Van, Nguyen Huy Dinh

Inżynieria Mineralna

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2024

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Vol. 2, no. 1

257--262

EN

The subsidence of the surface due to mining activities is a significant issue in mining areas. Therefore, predicting surface subsidence is a necessary task to ensure safety and production efficiency. This article applied an Artificial Neural Network (ANN) model to predict surface subsidence resulting from underground mining operations in the Mong Dương mine. The ANN model proposed in this research uses a recursive multi-step forecasting model, where the predicted value at the previous step is added to the time series to forecast the next value. The experimental dataset consists of 12 monitoring cycles over 24 months, with a 2-month interval, divided into a training set containing the first 9 measurement cycles and a test set containing the last 3 cycles. First, the K-fold cross-validation method is applied to the training set to determine the best parameters for the model. Then, these parameters are used to predict surface subsidence for the values in the test set. The prediction error depends on the time gap between the last measurement cycle and the forecasting cycle. The relative errors in the tenth cycle for the four points are 0.9%, -1.7%, -1.7%, and 1.4%. These error values increase to 1.4%, -1.8%, -1.8%, and -1.7% in the eleventh cycle and further to 2.0%, -2.2%, -2.2%, and 2.5% in the twelfth cycle. The absolute errors are determined to be small, within the range of 20 mm. These results demonstrate that the proposed method and ANN model are suitable for the time-series monitoring data in mining areas.

PL

Osuwanie się powierzchni z powodu działalności górniczej jest istotnym problemem w obszarach górniczych. Dlatego przewidywanie osiadania powierzchni jest niezbędnym zadaniem, aby zapewnić bezpieczeństwo i efektywność produkcji. W tym artykule zastosowano model sztucznej sieci neuronowej (ANN) do przewidywania osiadania powierzchni wynikającego z podziemnych operacji górniczych w kopalni Mong Dương. Proponowany w tym badaniu model ANN wykorzystuje rekurencyjny model prognozowania wieloetapowego, w którym przewidywana wartość z poprzedniego kroku jest dodawana do szeregu czasowego, aby prognozować następną wartość. Zbiór danych eksperymentalnych składa się z 12 cykli monitorowania w ciągu 24 miesięcy, z dwumiesięcznym odstępem, podzielonych na zestaw treningowy zawierający pierwsze 9 cykli pomiarowych i zestaw testowy zawierający ostatnie 3 cykle. Najpierw metoda walidacji krzyżowej K-fold jest stosowana do zestawu treningowego, aby określić najlepsze parametry dla modelu. Następnie te parametry są uży-wane do przewidywania osiadania powierzchni dla wartości w zestawie testowym. Błąd prognozy zależy od przerwy czasowej między ostatnim cyklem pomiarowym a cyklem prognozowania. Błędy względne w dziesiątym cyklu dla czterech punktów wynoszą 0,9%, -1,7%, -1,7% i 1,4%. Te wartości błędów wzrastają do 1,4%, -1,8%, -1,8% i -1,7% w jedenastym cyklu i dalej do 2,0%, -2,2%, -2,2% i 2,5% w dwunastym cyklu. Błędy bezwzględne są określane jako małe, w zakresie 20 mm. Wyniki te pokazują, że proponowana metoda i model ANN są odpowiednie dla danych monitorowania szeregów czasowych w obszarach górniczych.

8

Artificial Neural Networks vs Long Short-Term Memory Prediction of Solid Flow in Tafna Basin (North-West Algeria)

Medfouni Mohamed Nadjib, Korichi Khaled, Marouf Nadir

Ecological Engineering & Environmental Technology

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2024

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Vol. 25, iss. 3

275--286

EN

The main objective of this work is to select the most reliable machine learning model to predict the generated solid flow in the Tafna basin (North-West of Algeria). It is about the artificial neural networks (ANN) and long short-term memory (LSTM). The sediment load is recorded through three hydrometric stations. The efficiency and performance of the two models is verified using the correlation coefficient (R2), the Nash-Sutcliffe coefficient (NSC) and the root mean square error (RMSE). The obtained simulated solids load shows a very good correlation in terms of precision although the ANN model gave relatively better results compared to the LSTM model where low RMSE values were recorded, which confirms that the artificial intelligence models remain also effective for the treatment and the prediction of hydrological phenomena such as the estimation of the solid load in a such watershed.

9

Detection and identification of fault in transmission lines based on ANN

Abdullah Ahmed G., Ibrahim Mohammad A., Ibrahim Waseem Kh.

Diagnostyka

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2024

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Vol. 25, No. 2

art. no. 2024206

EN

Along with power transmission lines' efficiency, another crucial factor in electrical power transmission networks is reliability, which guarantees power transmission stability. One of the crucial and essential tasks for maintaining the continuity and stability of power transmission in transmission networks Capacity without any significant failures is identifying errors and malfunctions in power transmission lines as soon as possible. The goal of this article is to develop and apply ANN technology to overcome the obstacles faced by the electrical power transmission network. In order for the ANN to learn useful patterns and features from raw current measurements, pre-processing and feature extraction techniques are used during the training process. Real-time applications can benefit from the ANN's architecture, which is optimized for high accuracy, quick response times, and scalability. To validate the performance of the ANN-based fault detection system, extensive simulations are conducted using data from different transmission line scenarios, including various fault types that short-circuit. The results demonstrate the capability of the ANN model to accurately detect and classify faults, as well as disconnect the power grid after detect any fault. The results showed the accuracy and high speed of the proposed method using a neural network compared to traditional methods.

10

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.

11

A novel artificial intelligence-based hybrid system to improve breast cancer detection using DCE-MRI

Akgül İsmail, Kaya Volkan, Karavaş Erdal, Aydin Sonay, Baran Ahmet

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2024

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Vol. 72, nr 3

art. no. e149172

EN

The interpretation of breast magnetic resonance imaging (MRI) in the healthcare field depends on the good knowledge and experience of radiologists. Recent developments in artificial intelligence (AI) have shown advances in the field of radiology. However, the desired levels have not been reached in the field of radiology yet. In this study, a novel model structure is proposed to characterize the diagnostic performance of AI technology for individual breast dynamic contrast material–enhanced (DCE) MRI sequences. In the proposed model structure, Inception-v3, EfficientNet-B3, and DenseNet-201 models were used as hybrids together with the Yolo-v3 algorithm to detect breast and cancer regions. In the proposed model, DCE-MRI sequences (T2, ADC, Diffusion, Non-Contrast Fat Non-Suppressed T1, Non-Contrast Fat Suppressed T1, Contrast Fat Suppressed T1, and Subtraction T1) were evaluated separately and validation was made, thus providing a unique perspective. According to the validation results, the model structure with the best performance was determined as Yolo-v3 + DenseNet-201. With this model structure, 92.41% accuracy, 0.5936 loss, 92.44% sensitivity, and 92.44% specificity rates were obtained. In addition, it was determined that the results obtained without using contrast material in the best model were 91.53% accuracy, 0.9646 loss, 92.19% sensitivity, and 92.19% specificity. Therefore, it is predicted that the need for contrast material use can be reduced with the help of this model structure.

12

A systematic review of artificial neural network techniques for analysis of foot plantar pressure

Wang Chongguang, Evans Kerrie, Hartley Dean, Morrison Scott, Veidt Martin, Wang Gui

Biocybernetics and Biomedical Engineering

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2024

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Vol. 44, no. 1

197--208

EN

Plantar pressure distribution offers insights into foot function, gait mechanics, and foot-related issues. This systematic review presents an analysis of the use of artificial neural network techniques in the context of plantar pressure analysis. 60 studies were included in the review. Sample size, pathology, pressure sensor number, data collection device, utilization of other sensor devices, ground-truth methods, pre-processing dataset, neural network type, and evaluation metrics were evaluated. Utilization of customized wearable footwear devices for the acquisition of data was common amongst both healthy participants and patients. Inertial measurement units emerged as an effective compensatory measure to address the limitations associated with the distribution of plantar pressure. Ground truth methods predominantly relied on the usage of both annotations and reference devices. Multilayer perceptron, convolutional neural networks, and recurrent neural networks were identified as the most frequently employed artificial neural network algorithms across the reviewed studies. Finally, the evaluation of performance largely drew upon statistical descriptions and other machine learning methods. This review provides a comprehensive understanding of the use of artificial neural network techniques in plantar pressure analysis, highlighting opportunities for future research.

13

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.

14

A comparative study of artificial neural networks and multivariate regression for predicting groundwater depths in the Arak aquifer

Hanifian Soadat, Khaleghi Mohammad Reza, Najarchi Mohsen, Jafarnia Reza, Varvani Javad

Acta Geophysica

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2024

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Vol. 72, no. 1

419--432

EN

In recent years, the groundwater resources of Arak plain have been under severe stress, so in some areas, due to the drying up of wells, the depth of wells has increased to access water. In some areas, the groundwater depth is high, which will lead to the salinization of those lands in the future. Regional modeling was used to organize and measure the response of the groundwater resources of Arak plain against the implementation of different management and implementation scenarios. This study aims to investigate the effective factors in the groundwater depth to provide a regional model with multiple linear regression (MLR) methods for Arak plain aquifer. For this purpose, the average groundwater potential maps (GPMs) in the Arak plain, as a dependent variable, and the transmissivity of the aquifer formations, groundwater exploitation values, altitude, average precipitation of the region, the amount of evaporation, and the distance from water resources are considered independent variables and regression analysis is done in SPSS software media. It was done to present a linear model. In the next stage, the presented model was evaluated by applying it to places where its statistics and information were not used to present the model, and finally, by applying this model in the GIS environment, the GPMs for the region were created. The study was prepared. Also, an artificial neural network (ANN) was used to simulate the depth of underground water. The performance of the ANN was measured through parameters such as root-mean-square error (RMSE) and correlation coefficient between real and desired outputs (R). The results of both methods indicate that factors such as the transmissivity of aquifer formations, GPMs drawdown, topography (the height of the well site on the level of the watershed), the groundwater exploitation values at the maximum operating radius of the well, and the distance from water resources are the main factors of GPMs drawdown. But the effectiveness of ANN in estimating GPMs drawdown is higher than the MLR method. The implemented methodology could be generalized to other watersheds with water scarcity problems for groundwater management.

15

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.

16

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.

17

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.

18

Reduction of Numerical Model in Some Geotechnical Problems

Góral Artur, Lefik Marek, Wojciechowski Marek

Studia Geotechnica et Mechanica

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2023

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Vol. 45, nr s1

333--349

EN

The concept of equivalence of the realistic, initial reference model and the simplified, reduced model is proposed. In reduced models, the action of the soil on the structure is replaced by the action of a layer with prescribed properties, defined by a set of parameters. The main difficulty here is to find the parameter values required by the simplified theory. The subject of this work is to find the dependence of the parameters of the reduced model on the parameters of the full model, including realistic soil behavior, in order to ensure the equivalence of both models. We show the potential of the method by presenting two examples: Winkler and Pasternak's model of a plate on the ground. We assume that both models are equivalent if they give identical results (displacements) at a finite number of observation points. An artificial neural network (ANN) is built in order to approximate and record the dependence of the parameters of the reduced model (at the network output) from the parameters of the full model (given at the network input). The complex network acts as a formula that assigns the parameters of the reduced model to a realistic description of the soil structure that is used for finite element method (FEM) modeling. The formalism we propose is quite general and can be applied to many engineering problems. The presented procedure is entirely numerical; it allows to calculate the parameters of the reduced model without resorting to symbolic calculations or additional theoretical considerations.

19

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.

20

System wizyjny do automatycznego rozpoznawania zwierząt na nagraniach z wideorejestratorów samochodowych

Balcerek Julian, Pawłowski Paweł, Trzciński Błażej

Przegląd Elektrotechniczny

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2023

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R. 99, nr 10

279--281

PL

W niniejszym artykule zaproponowano system do automatycznego rozpoznawania zwierząt na obrazach z nagrań wideorejestratorów samochodowych wykorzystujący sztuczne sieci neuronowe. Na przygotowanej bazie obrazów przeprowadzono trening, walidację i testy pod kątem rozpoznawania m.in. krów, dzików, saren oraz innych, także mniejszych, zwierząt spotykanych na polskich drogach. Osiągnięto satysfakcjonujące wyniki rozpoznawania nawet na obrazach o niskiej jakości. Do obsługi systemu przygotowano aplikację użytkownika umożliwiającą przeprowadzenie testów rozpoznawania na pojedynczych zdjęciach i plikach wideo.

EN

This article proposes a system for automatic recognition of animals based on images from car video recorders using artificial neural net works. Training, validation and tests were conducted on the prepared image database in terms of recognizing, among others: cows, wild boars, roe deer and other, also smaller, animals encountered on Polish roads. Satisfactory recognition results were achieved even on low-quality image frames. A user application has been prepared to operate the system, enabling recognition tests to be conducted on photos and video files.