Кафедра фізики і електротехніки

Постійне посилання зібрання

https://elar.khmnu.edu.ua/handle/123456789/577

Переглянути

Перегляд Кафедра фізики і електротехніки за Автор "Pietraszek, Jacek"

Зараз показуємо 1 - 2 з 2
  • Ескіз
    Документ
    The Heuristic Approach to the Selection of Experimental Design, Model and Valid Pre-Processing Transformation of DoE Outcome
    (Trans Tech Publications, 2014) Pietraszek, Jacek; Goroshko, Andrii
    The typical problem in the design of experiments methodology is to avoid the risk of obtaining predictions outside of ranges with a technological or physical sense. Such a situation occurs very often if a simple linear regression or another unbounded one is involved as a predictive model. The possible solution is to provide mappings of the outcome from the range of interest into the full range from negative to plus infinity before the linear regression is applied. The intermediate values obtained from the unbounded predictive model are re-transformed into the physical domain by the inverse transformation. The key issue is to decide if the mapping is required and –subsequently – what mapping is necessary. The author proposed a simple heuristic solution supporting this decision and tested such a solution in some examples described in this paper.
  • Ескіз
    Документ
    The Principal Component Analysis of Tribological Tests of Surface Layers Modified with IF-WS2 Nanoparticles
    (Trans Tech Publications, Switzerland, 2015) Pietraszek, Jacek; Korzekwa, Joanna; Goroshko, Andrii
    The investigation described in this paper resulted in some complicated statistical analysis. The first level was an experimental design with technological parameters as factorials input and geometrical surface layer properties as quantitative outputs. The second level was an analysis generally leading to an optimization inverse problem: what parameters result in desired surface layer properties. The principal component analysis was made to identify possibility of a dimensionality reduction and simplify the optimization. Obtained results showed that the experimental dataset is practically two-dimensional but PCA projection involves all factors into the skewed hyper-plane. This paper contains a description of the problem, obtained results, analysis and conclusions.