Jaroslaw Kurek

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The paper presents an automatic computerized system for the diagnosis of the rotor bars of the induction electrical motor by applying the support vector machine. Two solutions of diagnostic system have been elaborated. The first one, called fault detection, discovers only the case of the fault occurrence. The second one (complex diagnosis) is able to find(More)
This chapter presents the computerized system for automatic analysis of the medical image of the colon biopsy, able to extract the important diagnostic knowledge useful for supporting the medical diagnosis of the inflammatory bowel diseases. Application of the artificial intelligence methods included in the developed automatic system allowed the authors to(More)
Machine learning approaches are generally adopted in many fields including data mining, image processing, intelligent fault diagnosis etc. As a classic unsupervised learning technology, fuzzy C-means cluster analysis plays a vital role in machine learning based intelligent fault diagnosis. With the rapid development of science and technology, the monitoring(More)
The paper presents the automatic method of the diagnosis of the cage of the asynchronous electrical engine by applying the Support Vector Machine. Two solutions of diagnostic system have been elaborated. The first, called simple diagnosis, discovers only if the fault has occurred. The second one (complex diagnosis) is able to find how many bars have been(More)
The paper presents an application of transfer learning using convolutional neural network (CNN) in recognition of the drill state on the basis of hole images drilled in the laminated chipboard. Three classes are recognized: red, yellow and green, which correspond with 3 stages of drill state. Red class indicates the drill, which is worn out and should be(More)
http://dx.doi.org/10.1016/j.eswa.2014.08.021 0957-4174/ 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author at: Warsaw University of Technology, Faculty of Electrical Engineering, Koszykowa 75, Warsaw, Poland. Tel.: +48 22 234 7235; fax: +48 22 234 5642. E-mail addresses: jbswiderski@wp.pl (B. Swiderski), sto@iem.pw.edu.pl (S. Osowski),(More)
This paper presents an automatic algorithm to recognize the condition of drills on the basis of analysis of the drilling hole images. The algorithm includes the image preprocessing leading to extraction of the diagnostic features, which are used as the input attributes for the classification system. The condition of drill is classified into two groups: the(More)