Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Jarosław Kurek"'
Publikováno v:
Applied Sciences, Vol 14, Iss 17, p 7462 (2024)
The furniture manufacturing sector faces significant challenges in machining composite materials, where quality issues such as delamination can lead to substandard products. This study aims to improve the classification of drilled holes in melamine-f
Externí odkaz:
https://doaj.org/article/a6811d385f7c404db271628340858dfc
Autor:
Agata Przybyś-Małaczek, Izabella Antoniuk, Karol Szymanowski, Michał Kruk, Alexander Sieradzki, Adam Dohojda, Przemysław Szopa, Jarosław Kurek
Publikováno v:
Applied Sciences, Vol 14, Iss 13, p 5913 (2024)
This evaluation of deep learning and traditional machine learning methods for tool state recognition in milling processes aims to automate furniture manufacturing. It compares the performance of long short-term memory (LSTM) networks, support vector
Externí odkaz:
https://doaj.org/article/a9f1abbd362d4a76ab84c501a0d95c99
Publikováno v:
Applied Sciences, Vol 14, Iss 11, p 4730 (2024)
The paper presents the comparative analysis of Long short-term memory (LSTM) and one-dimensional convolutional neural networks (1-D CNNs) for tool wear classification in chipboard milling processes. The complexity of sequence data in various fields m
Externí odkaz:
https://doaj.org/article/8690ee150ac747cfb0965b5b4e698fa9
Autor:
Jarosław Kurek, Tomasz Latkowski, Michał Bukowski, Bartosz Świderski, Mateusz Łępicki, Grzegorz Baranik, Bogusz Nowak, Robert Zakowicz, Łukasz Dobrakowski
Publikováno v:
Applied Sciences, Vol 14, Iss 6, p 2601 (2024)
In the evolving realities of recruitment, the precision of job–candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as a
Externí odkaz:
https://doaj.org/article/dd45e5169e6b45afba115b7346a39c98
Publikováno v:
Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol 71, Iss 6 (2023)
In this paper, we present an improved efficient capsule network (CN) model for the classification of the Kuzushiji-MNIST and Kuzushiji-49 benchmark datasets. CNs are a promising approach in the field of deep learning, offering advantages such as robu
Externí odkaz:
https://doaj.org/article/771192afdfca49f4933a70237858d68a
Publikováno v:
Sensors, Vol 24, Iss 4, p 1092 (2024)
The advancement of machine learning in industrial applications has necessitated the development of tailored solutions to address specific challenges, particularly in multi-class classification tasks. This study delves into the customization of loss f
Externí odkaz:
https://doaj.org/article/fe8334c1b8fa4dd6940c12f24b6dc1da
Autor:
Jarosław Kurek, Gniewko Niedbała, Tomasz Wojciechowski, Bartosz Świderski, Izabella Antoniuk, Magdalena Piekutowska, Michał Kruk, Krzysztof Bobran
Publikováno v:
Agriculture, Vol 13, Iss 12, p 2259 (2023)
This research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based veg
Externí odkaz:
https://doaj.org/article/1120107b6ca440cd9d26c4916d45bec7
Publikováno v:
Sensors, Vol 23, Iss 13, p 5850 (2023)
In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in rea
Externí odkaz:
https://doaj.org/article/8e51061604a94041a2682499f568199b
Autor:
Izabella Antoniuk, Jarosław Kurek, Artur Krupa, Grzegorz Wieczorek, Michał Bukowski, Michał Kruk, Albina Jegorowa
Publikováno v:
Sensors, Vol 23, Iss 3, p 1109 (2023)
In this paper, a novel approach to evaluation of feature extraction methodologies is presented. In the case of machine learning algorithms, extracting and using the most efficient features is one of the key problems that can significantly influence o
Externí odkaz:
https://doaj.org/article/babb70c5853342f89e40b44d727493b4
Autor:
Jarosław Kurek, Artur Krupa, Izabella Antoniuk, Arlan Akhmet, Ulan Abdiomar, Michał Bukowski, Karol Szymanowski
Publikováno v:
Sensors, Vol 23, Iss 1, p 448 (2023)
In this article, an automated method for tool condition monitoring is presented. When producing items in large quantities, pointing out the exact time when the element needs to be exchanged is crucial. If performed too early, the operator gets rid of
Externí odkaz:
https://doaj.org/article/aaa4bd7eb7b742029a82618efb4d61ad