Albina Jegorowa

发表

Data augmentation techniques for transfer learning improvement in drill wear classification using convolutional neural network

A. Orłowski, J. Górski, B. Świderski, 2019, Machine Graphics and Vision.

Initial study on the use of support vector machine (SVM) in tool condition monitoring in chipboard drilling

J. Górski, J. Kurek, M. Kruk, 2019, European Journal of Wood and Wood Products.

Advanced Feature Extraction Methods from Images of Drillings in Melamine Faced Chipboard for Automatic Diagnosis of Drill Wear

J. Kurek, A. Krupa, M. Kruk, 2023, Sensors.

Time-efficient approach to drill condition monitoring based on images of holes drilled in melamine faced chipboard

Izabella Antoniuk, P. Czarniak, Jaroslaw Kurek, 2020 .

Deep learning methods for drill wear classification based on images of holes drilled in melamine faced chipboard

P. Czarniak, J. Kurek, Albina Jegorowa, 2020, Wood Science and Technology.

Effect of Nitrogen Ion Implantation on the Tool Life Used in Particleboard CNC Drilling

Z. Werner, M. Barlak, J. Wilkowski, 2022, Materials.

Automatic Estimation of Drill Wear Based on Images of Holes Drilled in Melamine Faced Chipboard with Machine Learning Algorithms

B. Świderski, J. Kurek, A. Krupa, 2023, Forests.

Diagnostic System of Drill Condition in Laminated Chipboard Drilling Process

Michal Kruk, Bartosz Swiderski, Jaroslaw Kurek, 2017 .

Multiclass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard

Albina Jegorowa, Grzegorz Wieczorek, Marcin Chlebus, 2021, Sensors.