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Morphological discrimination of granular materials by measurement of pixel intensity distribution (PID)

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper provides statistical analysis of the photographs of four various granular materials (peas, pellets, triticale, wood chips). For analysis, the (parametric) ANOVA and the (nonparametric) Kruskal-Wallis tests were applied. Additionally, the (parametric) two-sample t-test and (non-parametric) Wilcoxon Rank-Sum Test for pairwise comparisons were performed. In each case, the Bonferroni correction was used. The analysis shows a statistical evidence of the presence of differences between the respective average discrete pixel intensity distributions (PID), induced by the histograms in each group of photos, which cannot be explained only by the existing differences among single granules of different materials. The proposed approach may contribute to the development of a fast inspection method for comparison and discrimination of granular materials differing from the reference material, in the production process.
Rocznik
Strony
297--308
Opis fizyczny
Bibliogr. 29 poz., rys., wykr., wzory
Twórcy
  • University of Agriculture in Krakow, Faculty of Production and Power Engineering, Balicka 120, 30-149, Cracow, Poland
  • Jagiellonian University, Faculty of Mathematics and Computer Science, Łojasiewicza 6, 30-348 Cracow, Poland
autor
  • Jagiellonian University, Faculty of Mathematics and Computer Science, Łojasiewicza 6, 30-348 Cracow, Poland
  • Ecole Centrale de Lyon, Laboratoire de Tribologie et Dynamique des Systèmes LTDS, CNRS, France
Bibliografia
  • [1] BIPM, IEC, IFCC, ISO, IUPAC, IUPAP, OIML: Guide to the Expression of Uncertainty in Measurement. International Organization for Standardization, Geneva. First Edition 1993, corrected and reprinted 1995.
  • [2] Wójcik, A., Niemczewska-Wójcik, M., Sładek, J. (2017). Assessment of free-form surfaces’ reconstruction accuracy. Metrol. Meas. Syst., 24(2), 303-312.
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  • [4] Mathia, T.G., Pawlus P., Wieczorowski M. (2011). Recent trends in surface metrology. Wear, 271, 3-4, 494-508.
  • [5] Królczyk, J.B. (2016). Metrological changes in the surface morphology of cereal grains in the mixingprocess. Int. Agrophys., 30, 193-202.
  • [6] Markowski, M., Żuk-Gołaszewska, K., Kwiatkowskia, D. (2013). Influence of variety on selected physical and mechanical properties of wheat. Industrial Crops and Products, 47, 113-117.
  • [7] Wójcik, A., Frączek, J., Wota, A.W. (2019). The methodical aspects of the friction modeling of plantgranular materials. Powder Technology, 344, 504-513.
  • [8] Wójcik, A., Klapa, P., Mitka, B., Sładek, J. (2018). The use of the photogrammetric method for measurement of the repose angle of granular materials. Measurement, 115, 19-26.
  • [9] Wójcik, A., Frączek, J. (2017). The influence of the repose angle and porosity of granular plant materials on the angle of internal friction and cohesion. Tribologia, 5, 117-123.
  • [10] Rezaei, H., Jim Lim, C., Lau, A., Sokhansanj, S. (2016). Size, Shape and Flow Characterization of Ground Wood Chip and Ground Wood Pellet Particles. textitPowder Technology.
  • [11] Hartmann, H., Böhm, T., Jensenb, P.D., Temmerman, M., Rabierc, F., Golserd, M. (2006). Methods for size classication of wood chips. Biomass and Bioenergy, 30, 944-953.
  • [12] Kristensen, E.F., Kofman, P.D. (2000). Pressure resistance to air flow during ventilation of different types of wood fuel chip. Biomass and Bioenergy, 18, 175-180.
  • [13] Mattsson, J.E. (1990). Basic handling characteristics of wood fuel: angle of repose, friction against surfaces and tendency to bridge building for different assortments. Scandinavian Journal of Forest Research, 5, 583-597.
  • [14] Jensen, P.D., Mattsson, J.E., Kofman, P.D., Klausner, A. (2004). Tendency of wood fuels from whole trees, logging residues and round wood to bridge over openings. Biomass and Bioenergy, 26, 107-113.
  • [15] Nabawy, B.S. (2014). Estimating porosity and permeability using Digital Image Analysis (DIA) technique for highly porous sandstones. textitArab J. Geosci.
  • [16] Andrä, H., Combaret, N., Dvorkin, J., Glatt, E., Han, J., Kabel, M., Keehm, Y., Krzikalla, F., Lee, M., Madonna, C, Marsh, M., Mukerji, T., Saenger, E.H., Sain, R., Saxena, N., Ricker, S., Wiegmann, A., Zhan, X. (2013). Digital rock physics benchmarks-Part I: Imaging and segmentation. Computers &Geosciences, 50, 25-32.
  • [17] Wójcik, A., Przybyla, W., Francik, S., and Knapczyk, A. (2018). The Research into Determination of the Particle-Size Distribution of Granular Materials by Digital Image Analysis. Mudryk, K., Werle, S. eds., Renewable energy sources: engineering, technology, innovation: ICORES 2017. Springer International Publishing AG, 623-630.
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  • [19] Roscher, R., Herzog, K., Kunkel, A., Kicherer, A., Töpfer, R., Förstner, W. (2014). Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields. Computers and Electronics in Agriculture, 100, 148-158.
  • [20] Wang, Q., Wanga, H., Xie, L., Zhang, Q. (2012). Outdoor color rating of sweet cherries using computer vision. Computers and Electronics in Agriculture, 87, 113-120.
  • [21] Yadav, B.K., Jindal, V.K. (2001). Monitoring milling quality of rice by image analysis. Computers and Electronics in Agriculture, 33, 19-33.
  • [22] Liu, W., Tao, Y., Siebenmorgen, T.J., Chen, H. (1998). Digital Image Analysis Method for Rapid Measurement of Rice Degree of Milling. Cereal Chem., 75, 380-385.
  • [23] Nyul, L.G., Udupa, J.K. (1999). On Standardizing the MR Image Intensity Scale. Magnetic Resonance in Medicine, 42, 1072-1081.
  • [24] Khoo, S.W., Karuppanan, S., Tan, C.S. (2016). A review of surface deformation and strain measurement using two-dimensional digital image correlation. 23(3), 461-480.
  • [25] Hocking, R.R. (2003). Methods and Applications of Linear Models: Regression and the Analysis of Variance, Wiley Series in Probability and Statistics. John Wiley & Sons, New York.
  • [26] Kruskal, W.H., Wallis, W.A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47, 583-621.
  • [27] Hollander, M., Wolfe, D.A. (1973). Nonparametric Statistical Methods. John Wiley & Sons, New York.
  • [28] Bretz, F., Hothorn, T., Westfall, P. (2010). Multiple Comparisons Using R. CRC Press.
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Uwagi
EN
1. This research was supported by the Ministry of Science and Higher Education of the Republic of Poland.
2. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e5dfbe75-72bd-464d-ba51-60adb35f0132
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