Innovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors

Rymarczyk, Tomasz; Kłosowski, Grzegorz

doi:10.17531/ein.2019.2.10

Cited by 34 publications

(21 citation statements)

References 28 publications

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“…Artificial networks are found in numerous predictive applications [22][23][24][25][26]. Typically, in the majority of models, the ANN will consist of three layers: the input layer that provides input to the model; the hidden layer where all the computational simulations on the input data occur; and the output layer that produces the outcome.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Kulisz

Kujawska

2020

Sustainability

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Planning is a crucial component of short- and long-term municipal waste management. Establishing the relationships between the factors that determine the amount of waste generated by municipalities and forecasting the waste management needs plays a fundamental role in the development of effective planning strategies and implementation of sustainable development. Artificial Neural Network employed for verifying the forecasts pertaining to the amount of rainfall in Poland were presented in the studies. The proposed models included selected explanatory indices in order to reflect the impact of social, demographic and economic factors on the amount of generated waste. Mean squared error (MSE) and regression value (R) are used as indices of efficiency of the developed models. The ANN models exhibited high accuracy of forecasts at high R values (R = 0.914, R = 0.989) and low MSE values. Derived from the socioeconomic data for 2003–2019, the model predicts that the future waste generation in 2024 will increase by 2%. The results indicate that the employed ANN models are effective in predicting the amount of waste and can be considered a cost-effective approach to planning integrated waste management systems.

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Section: Introductionmentioning

confidence: 99%

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Kulisz

Kujawska

2020

Sustainability

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“…The arrangement of electrodes was developed in such a way as to enable measurement and reconstruction of 3D tomographic images with such techniques as Body Surface Potential Mapping (BSPM), Electrical Impedance Tomography (EIT) [53], and Electrical Capacitance Tomography (ECT) [6,54]. The electrode system also allows you to perform a full 12-channel ECG.…”

Section: Model Validation In Real Conditionsmentioning

confidence: 99%

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

Kłosowski

Rymarczyk

Wójcik³

et al. 2020

Electronics

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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.

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“…Testing the correctness of the model is checked by testing the probability distribution of so-called Cox-Snell residues. Schoenfeld's residuals For the vector covariates calculated at time Schoenfeld's residuals are: (19) where set a set of those objects that remain endangered until, In the event that Cox regression model assumptions are met rather than asymptotically . When fixing instead of we take…”

Section: Model Of Fault Predictionmentioning

confidence: 99%

“…1). There are many methods for solving optimisation problems [2,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Autonomy, optimisation and integration of analytical approaches is related to the operation of sensor networks, large amounts of data, analysis and interpretation of information, taking into account security aspects [1,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Data From Measuring Sensors for Prediction in Production Process Control Systems

Rymarczyk

Przysucha

Kowalski

et al. 2019

IAPGOS

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The article presents a solution based on a cyber-physical system in which data collected from measuring sensors was analysed for prediction in the production process control system. The presented technology was based on intelligent sensors as part of the solution for Industry 4.0. The main purpose of the work is to reduce data and select the appropriate covariate to optimise modelling of defects using the Cox model for a specific mechanical system. The reliability of machines and devices in the production process is a condition for ensuring continuity of production. Predicting damage, especially its movement, gives the ability to monitor the current state of the machine. In a broader perspective, this enables streamlining the production process, service planning or control. This ensures production continuity and optimal performance. The presented model is a regressive survival analysis model that allows you to calculate the probability of failure occurring over a given period of time.

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Innovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors

Cited by 34 publications

References 28 publications

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

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

Analysis of Data From Measuring Sensors for Prediction in Production Process Control Systems

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