Grzegorz Kłosowski

Assistant Professor

Lublin University of Technology

Poland

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Grzegorz Kłosowski is an Assistant Professor at Lublin University of Technology in Poland. His research areas include machine learning applications in electrical process tomography, reliability of industrial reactors, and quality assessment in welding techniques. Recent articles focus on optimizing neural networks for energy efficiency, utilizing artificial intelligence in ultrasonic and electrical tomography, and innovative methods for moisture detection in building materials.

Articles (16)

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2025

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2025

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2024

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2024

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2024

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2024

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2023

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2023

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2022

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2022

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2022

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2021

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2021

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2021

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2021

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2020

Collaborators (8)

Monika Kulisz

Lublin University of Technology

POLAND

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Magdalena Rzemieniak

Deputy Dean / Head of Department / Ph.D., D.Sc. (Eng.), Associate Professor

Lublin University of Technology

POLAND

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Anna Hoła

Wrocław University of Scince and Technology

POLAND

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Michał Maj

Lecturer

POLAND

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Łukasz Kański

assistant professor

Maria Curie-Skłodowska University

POLAND

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Tomasz Rymarczyk

University of Economics and Innovation

POLAND

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Jolanta Słoniec

Head of the department

Lublin University of Technology

POLAND

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Tomasz Cieplak

Assistant Professor

Lublin University of Technology

POLAND

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Grzegorz Kłosowski

Research Interests

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Articles (16)

Improving the reliability of industrial reactors by using differential neural network architecture in ultrasonic tomography

Application of machine learning in electrical process tomography with variable frequency measurement sequences

The use of the multi-sequential LSTM in electrical tomography for masonry wall moisture detection

The Use of Artificial Intelligence for Quality Assessment of Refill Friction Stir Spot Welded Thin Joints

Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors

Ultrasound Brain Tomography: Comparison of Deep Learning and Deterministic Methods

Using Machine Learning in Electrical Tomography for Building Energy Efficiency through Moisture Detection

Use of the double-stage LSTM network in electrical tomography for 3D wall moisture imaging

Machine Learning-Enhanced Radio Tomographic Device for Energy Optimization in Smart Buildings

Intellectual Capital: A New Predictive Indicator for Project Management Improvement

APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN WALL MOISTURE IDENTIFICATION BY EIT METHOD

The Concept of Using LSTM to Detect Moisture in Brick Walls by Means of Electrical Impedance Tomography

Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography

Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls

Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms

Effect of features extraction on improving LSTM network quality in ECG signal classification

Collaborators (8)

Monika Kulisz

Magdalena Rzemieniak

Anna Hoła

Michał Maj

Łukasz Kański

Tomasz Rymarczyk

Jolanta Słoniec

Tomasz Cieplak

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