Application of selected Levy processes for degradation modelling of long range mine belt using real-time data

Wójcik³

et al. 2020

Electronics

This paper refers to the method of using the deep neural long-short-term memory (LSTM) network for the problem of electrocardiogram (ECG) signal classification. ECG signals contain a lot of subtle information analyzed by doctors to determine the type of heart dysfunction. Due to the large number of signal features that are difficult to identify, raw ECG data is usually not suitable for use in machine learning. The article presents how to transform individual ECG time series into spectral images for which two characteristics are determined, which are instantaneous frequency and spectral entropy. Feature extraction consists of converting the ECG signal into a series of spectral images using short-term Fourier transformation. Then the images were converted using Fourier transform again to two signals, which includes instantaneous frequency and spectral entropy. The data set transformed in this way was used to train the LSTM network. During the experiments, the LSTM networks were trained for both raw and spectrally transformed data. Then, the LSTM networks trained in this way were compared with each other. The obtained results prove that the transformation of input signals into images can be an effective method of improving the quality of classifiers based on deep learning.

Section: Discussionmentioning

confidence: 99%

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

Wójcik³

et al. 2020

Electronics

“…This technique causes a reduction of estimators of unknown parameters. Because of this the use of the elastic net method to solve the inverse problem in tomography allows accurate and stable reconstruction images to be obtained [8].…”

Section: Methodsmentioning

confidence: 99%

“…According to Hadamard, well-conditioned problems must meet three criteria: the solution exists, the solution is unambiguous and the solution is stable [5]. A well-posed problem is likely to be solved on a computer using a stable algorithm [6,7,8]. If the problem is not well-posed, it should be reformulated in a way that allows the use of a numeric algorithm [9].…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression for Machine Learning in Process Tomography

Kozłowski

et al. 2019

Sensors

122

The main goal of the research presented in this paper was to develop a refined machine learning algorithm for industrial tomography applications. The article presents algorithms based on logistic regression in relation to image reconstruction using electrical impedance tomography (EIT) and ultrasound transmission tomography (UST). The test object was a tank filled with water in which reconstructed objects were placed. For both EIT and UST, a novel approach was used in which each pixel of the output image was reconstructed by a separately trained prediction system. Therefore, it was necessary to use many predictive systems whose number corresponds to the number of pixels of the output image. Thanks to this approach the under-completed problem was changed to an over-completed one. To reduce the number of predictors in logistic regression by removing irrelevant and mutually correlated entries, the elastic net method was used. The developed algorithm that reconstructs images pixel-by-pixel is insensitive to the shape, number and position of the reconstructed objects. In order to assess the quality of mappings obtained thanks to the new algorithm, appropriate metrics were used: compatibility ratio (CR) and relative error (RE). The obtained results enabled the assessment of the usefulness of logistic regression in the reconstruction of EIT and UST images.

“…There are many methods and algorithms used in optimization problems [52,53,54,55,56,57]. In this article, the authors chose deterministic algorithms based on the Gauss-Newton method as a reference to the machine learning methods.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Kozłowski

et al. 2019

Sensors

The main goal of this work was to compare the selected machine learning methods with the classic deterministic method in the industrial field of electrical impedance tomography. The research focused on the development and comparison of algorithms and models for the analysis and reconstruction of data using electrical tomography. The novelty was the use of original machine learning algorithms. Their characteristic feature is the use of many separately trained subsystems, each of which generates a single pixel of the output image. Artificial Neural Network (ANN), LARS and Elastic net methods were used to solve the inverse problem. These algorithms have been modified by a corresponding increase in equations (multiply) for electrical impedance tomography using the finite element method grid. The Gauss-Newton method was used as a reference to machine learning methods. The algorithms were trained using learning data obtained through computer simulation based on real models. The results of the experiments showed that in the considered cases the best quality of reconstructions was achieved by ANN. At the same time, ANN was the slowest in terms of both the training process and the speed of image generation. Other machine learning methods were comparable with the deterministic Gauss-Newton method and with each other.