Application of Artificial Neural Networks for Processing and Interpretation of Data from a Scanning Magnetic Introscope

2021 ◽  
Author(s):  
Victor Evgenevich Kosarev ◽  
Ekaterina Anatolevna Yachmeneva ◽  
Aleksandr Vladimirovich Starovoyto ◽  
Dmitrii Ivanovich Kirgizov ◽  
Rustem Ramilevich Mukhamadiev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Learning Algorithms ◽  
Test Machine ◽  
Subjective Factor ◽  
Mathematical Algorithms ◽  
Speed Up ◽  
Artificial Neural

Summary This paper presents the efficiency of using artificial neural networks for solving problems of processing and interpreting geophysical data obtained by scanning magnetic introscopy. Neural networks of various architectures have been implemented to solve the problems of processing primary material, searching for well structure objects,identifying casing defects. The analysis of the capabilities of neural networks in comparison with mathematical algorithms is carried out. To test machine learning algorithms and mathematical algorithms for processing, visualizing and storing the results, a software shell was created in which all tasks are solved using a set of tools. It was found that the use of artificial neural networks can significantly speed up the process of data processing and interpretation, as well as improve the quality of the results in comparison with individual mathematical algorithms. Nevertheless, the use of mathematical algorithms in solving some problems gives consistently better results. In particular, the problematic aspects were identified at the stage of interpretation when identifying defects. This is due to the presence of conventions in the isolation of defects by the operator at the stage of preparing data for training neural networks, which is a subjective factor and requires a deeper study.

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals Provision of Data to Use in Artificial Intelligence Algorithms for Single Room Heating

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 523
Author(s):  
Kristin Majetta ◽  
Christoph Clauß ◽  
Christoph Nytsch-Geusen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Algorithms ◽  
Parameter Variation ◽  
Machine Learning Algorithms ◽  
Data Basis ◽  
Single Room ◽  
Artificial Neural

This paper describes a way to generate a great amount of data and to use it to find a relation between a room controller and a certain room. Therefore, simulation scenarios are defined and developed that contain different room, location, usage and controller models. With parameter variation and optimization of the corresponding controller parameters a data basis is created with about 5300 entries. On the basis of this data, machine learning algorithms like artificial neural networks can be used to investigate the relation between rooms and their best suited controllers.

scholarly journals Evaluating different machine learning techniques as surrogate for low voltage grids

2020 ◽  
Vol 3 (S1) ◽  
Author(s):  
Stephan Balduin ◽  
Tom Westermann ◽  
Erika Puiutta
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Technologies ◽  
Low Voltage ◽  
Surrogate Models ◽  
Machine Learning Algorithms ◽  
Ongoing Research ◽  
Grid Topology ◽  
Artificial Neural

Abstract The transition of the power grid requires new technologies and methodologies, which can only be developed and tested in simulations. Especially larger simulation setups with many levels of detail can become quite slow. Therefore, the number of possible simulation evaluations decreases. One solution to overcome this issue is to use surrogate models, i. e., data-driven approximations of (sub)systems. In a recent work, we built a surrogate model for a low voltage grid using artificial neural networks, which achieved satisfying results. However, there were still open questions regarding the assumptions and simplifications made. In this paper, we present the results of our ongoing research, which answer some of these questions. We compare different machine learning algorithms as surrogate models and exchange the grid topology and size. In a set of experiments, we show that algorithms based on linear regression and artificial neural networks yield the best results independent of the grid topology. Furthermore, adding volatile energy generation and a variable phase angle does not decrease the quality of the surrogate models.

scholarly journals Automatic Lithofacies Classification with t-SNE and K-Nearest Neighbors Algorithm

2021 ◽  
Vol 44 ◽  
Author(s):  
Guilherme Loriato Potratz ◽  
Smith Washington Arauco Canchumuni ◽  
Jose David Bermudez Castro ◽  
Júlia Potratz ◽  
Marco Aurélio C. Pacheco
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Unsupervised Classification ◽  
Machine Learning Algorithms ◽  
Hydrocarbon Exploration ◽  
Data Set ◽  
Log Data ◽  
Hidden Patterns ◽  
Artificial Neural

One of the critical processes in the exploration of hydrocarbons is the identification and prediction of lithofacies that constitute the reservoir. One of the cheapest and most efficient ways to carry out that process is from the interpretation of well log data, which are often obtained continuously and in the majority of drilled wells. The main methodologies used to correlate log data to data obtained in well cores are based on statistical analyses, machine learning models and artificial neural networks. This study aims to test an algorithm of dimension reduction of data together with an unsupervised classification method of predicting lithofacies automatically. The performance of the methodology presented was compared to predictions made with artificial neural networks. We used the t-Distributed Stochastic Neighbor Embedding (t-SNE) as an algorithm for mapping the wells logging data in a smaller feature space. Then, the predictions of facies are performed using a KNN algorithm. The method is assessed in the public dataset of the Hugoton and Panoma fields. Prediction of facies through traditional artificial neural networks obtained an accuracy of 69%, where facies predicted through the t-SNE + K-NN algorithm obtained an accuracy of 79%. Considering the nature of the data, which have high dimensionality and are not linearly correlated, the efficiency of t SNE+KNN can be explained by the ability of the algorithm to identify hidden patterns in a fuzzy boundary in data set. It is important to stress that the application of machine learning algorithms offers relevant benefits to the hydrocarbon exploration sector, such as identifying hidden patterns in high-dimensional datasets, searching for complex and non-linear relationships, and avoiding the need for a preliminary definition of mathematic relations among the model’s input data.

Performance Prediction for Crop Irrigation Using Different Machine Learning Approaches

2021 ◽  
pp. 61-79
Author(s):  
Tarun Jain ◽  
Payal Garg ◽  
Pradeep Kumar Tiwari ◽  
Vamsi Krishna Kuncham ◽  
Mrinal Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Main Challenge ◽  
Artificial Neural ◽  
Crop Irrigation

Irrigation is an ancient practice that evolved over the years. Irrigation is the method through which a controlled amount of water is applied to plants making the most important recourse of irrigation. Earth is composed of 70% of water of which only 2.5% is fresh. Most of it trapped in snowfields and glaciers with only 0.007% of the earth's water for the needs of mankind. Limited water resources had become the main challenge in farming. In the chapter, machine learning algorithms and neural networks are used to reduce the usage of water in crop irrigation systems. This chapter focus on four mainstream machine learning calculations (KNN [k-nearest neighbor], GNB [Gauss Naive Bayes], SVM [support vector machine], DT [decision tree]) and a neural networks technique (artificial neural networks [ANN]) to expectation models utilizing the huge dataset (510 irrigation cases), bringing about productive and precise dynamic. The outcomes showed that k-nearest neighbors and artificial neural networks are the best indicators with the most elevated effectiveness of 98% and 90% respectively.

Convolutional Neural Network

2021 ◽  
pp. 12-26
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals Feature Reduction Based on Hybrid Efficient Weighted Gene Genetic Algorithms with Artificial Neural Network for Machine Learning Problems in the Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Tareq Abed Mohammed ◽  
Shaymaa Alhayali ◽  
Oguz Bayat ◽  
Osman N. Uçan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Search Time ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Feature Size ◽  
Artificial Neural

A large amount of data being generated from different sources and the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data arises from many factors such as the high number of features, existence of lost data, and variety of data. One of the most effective solutions that used to overcome the huge amount of big data is the feature reduction process. In this paper, a set of hybrid and efficient algorithms are proposed to classify the datasets that have large feature size by merging the genetic algorithms with the artificial neural networks. The genetic algorithms are used as a prestep to significantly reduce the feature size of the analyzed data before handling that data using machine learning techniques. Reducing the number of features simplifies the task of classifying the analyzed data and enhances the performance of the machine learning algorithms that are used to extract valuable information from big data. The proposed algorithms use a new gene-weight mechanism that can significantly enhance the performance and decrease the required search time. The proposed algorithms are applied on different datasets to pick the most relative and important features before applying the artificial neural networks algorithm, and the results show that our proposed algorithms can effectively enhance the classifying performance over the tested datasets.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

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