{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:41:10Z","timestamp":1760240470803,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T00:00:00Z","timestamp":1561593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The fourth digital revolution of industry makes substantive changes to the rate and methodology of work performance. Machines and robots do the majority of work in robotized and automated factories, while people only supervise them. After an increase of production efficiency, quality control became a critical point. Therefore, quality control systems of computer visions are increasingly installed. The branch of chemical industry requires measurements of quality at as great a frequency as possible. Consequently, indirect measurements are effectively used at this point. This research presents the method of indirect particle measurement. Particles are measured using digital image processing. The algorithm is used for particle measurement to automatically adjust the measurement results. Numerical intelligence is added to the algorithm to increase the accuracy of correction results. The research deals with the problem of matching the results of indirect measurements and the results of the control equipment. For data analysis, fertilizer diameter, mean diameter, aspect ratio, symmetry, sphericity, convexity and some other parameters are used. The mismatch of the artificial neural network results with the control equipment results is slightly higher than 1%.<\/jats:p>","DOI":"10.3390\/sym11070838","type":"journal-article","created":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T02:50:15Z","timestamp":1561603815000},"page":"838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Research of the Equipment Self-Calibration Methods for Different Shape Fertilizers Particles Distribution by Size Using Image Processing Measurement Method"],"prefix":"10.3390","volume":"11","author":[{"given":"Andrius","family":"Laucka","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St.50-438, LT-51368 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6165-2796","authenticated-orcid":false,"given":"Vaida","family":"Adaskeviciute","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St.50-438, LT-51368 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9862-8917","authenticated-orcid":false,"given":"Darius","family":"Andriukaitis","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Kaunas University of Technology, Studentu St.50-438, LT-51368 Kaunas, Lithuania"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.13164\/re.2015.1033","article-title":"Research of the Defects in Anesthetic Masks","volume":"24","author":"Laucka","year":"2015","journal-title":"Radioengineering"},{"key":"ref_2","first-page":"11","article-title":"Determination of Particle Size Distributions by Laser Diffraction","volume":"67","year":"2012","journal-title":"Tech. New Matter"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S1672-2515(07)60055-4","article-title":"In-line particle sizing for process control in new dimensions","volume":"2","author":"Witt","year":"2004","journal-title":"China Particuol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1364\/AO.11.000265","article-title":"Particle size analyser","volume":"11","author":"Cornillault","year":"1972","journal-title":"Appl. Opt."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0892-6875(01)00206-0","article-title":"On-line measurement of particle size in mineral slurries","volume":"15","author":"Coghill","year":"2002","journal-title":"Miner. Eng."},{"key":"ref_6","unstructured":"McClements, D.J. (2019, June 03). Available online: 10.1002\/9780470027318.a1518."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1016\/j.jsv.2005.05.002","article-title":"Near field acoustic holography (NAH) theory for cyclostationary sound field and its application","volume":"290","author":"Wan","year":"2006","journal-title":"J. Sound Vib."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1002\/jps.20276","article-title":"Determination of fluidized bed granulation end point using near-infrared spectroscopy and phenomenological analysis","volume":"94","author":"Findlay","year":"2005","journal-title":"J. Pharm. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1002\/ppsc.200290002","article-title":"Simultaneous measurements of particle size and particle velocity by the spatial filtering","volume":"19","author":"Petrak","year":"2002","journal-title":"Part. Part. Syst. Charact."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.apt.2010.11.002","article-title":"In-line particle sizing for real-time process control by fibre-optical spatial filtering technique (SFT)","volume":"22","author":"Dieter","year":"2011","journal-title":"Adv. Powder Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shiina, T., and Muramoto, K. (2010, January 25\u201330). Z-R relation for snowfall using two small doppler radars and snow particle images. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5651170"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1021\/i560093a017","article-title":"Particle Size Determination by Sedimentation","volume":"13","author":"Kammermeyer","year":"1941","journal-title":"Ind. Eng. Chem. Anal. Ed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111","DOI":"10.3329\/dujs.v61i1.15106","article-title":"Determination of Particle Size Distribution of Used Black Tea Leaves by Scanning Electron Microscope","volume":"61","author":"Hossain","year":"2013","journal-title":"Dhaka Univ. J. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ray, O., Banik, B., and Pani, C. (2017, January 5\u20137). Computational Size Measurement & Study of Nanoparticle Using Transmission Electron Microscopy Data by Image-Processing. Proceedings of the 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, India.","DOI":"10.1109\/IACC.2017.0138"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.ultramic.2010.10.011","article-title":"A Simple Algorithm for Measuring Particle Size Distributions on an Uneven Background from TEM Images","volume":"111","author":"Gontard","year":"2011","journal-title":"Ultramicroscopy"},{"key":"ref_16","unstructured":"Yantong, Z., Guoying, Z., and Yu, G. (2017, January 1\u20134). Particle Size Measurement Based on Image Multivariate Multiscale Entropy. Proceedings of the 2017 IEEE Trustcom\/BigDataSE\/ICESS, Sydney, Australia."},{"key":"ref_17","unstructured":"Jorgensen, T., Reinholt, F., and Johnsen, O.M. (2001). Automatic Particle Analyzing System. (7154600), US Patent, Available online: https:\/\/www.google.lt\/patents\/US7154600."},{"key":"ref_18","unstructured":"Freiherr von Hodenberg, M. (2008). Device for Determining Parameters of a Bulk Material Particle Flow. (2008046914), WO Patent, Available online: https:\/\/www.google.lt\/patents\/WO2008046914A1?cl=en."},{"key":"ref_19","unstructured":"Jorgensen, T.K. (2012). Online Sampling Apparatus and Method for Online Sampling. (2012083966), WO Patent, Available online: https:\/\/www.google.lt\/patents\/WO2012083966A1?cl=en."},{"key":"ref_20","unstructured":"Maa\u00df, S., Rojahn, J., Emmerich, J., and Kraume, M. (2012, January 10\u201313). On-line monitoring of fluid particle size distributions in agitated vessels using automated image analysis. Proceedings of the 14th European Conf. on Mixing, Warsaw, Poland. Available online: http:\/\/mixing14.eu\/p\/mixing14eu_39.pdf."},{"key":"ref_21","first-page":"1","article-title":"Particle Morphology Analysis of Biomass Material Based on Improved Image Processing Method","volume":"2017","author":"Lu","year":"2017","journal-title":"Int. J. Anal. Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"21466","DOI":"10.3390\/s141121466","article-title":"High speed stereovision setup for position and motion estimation of fertilizer particles leaving a centrifugal spreader","volume":"14","author":"Hijazi","year":"2014","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1208\/s12249-008-9043-y","article-title":"A New Rapid On-Line Imaging Method to Determine Particle Size Distribution of Granules","volume":"9","author":"Antikainen","year":"2008","journal-title":"AAPS PharmSciTec."},{"key":"ref_24","first-page":"247","article-title":"Particle size and shape analysis using Imagej with customized tools for segmentation of particles","volume":"4","author":"Kumari","year":"2015","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1248\/cpb.48.1154","article-title":"On-line Monitoring of Granule Growth in High Shear Granulation by an Image Processing System","volume":"48","author":"Watano","year":"2000","journal-title":"Int. J. Chem. Pharm. Bull."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.powtec.2012.03.033","article-title":"Particle size, size distribution and morphological evaluation of airbone dust particles of diverse woods by Scanning Electron Microscopy and image processing program","volume":"225","author":"Mazzoli","year":"2012","journal-title":"Int. J. Powder Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/S0032-5910(00)00332-6","article-title":"A fuzzy control system of high shear granulation using image processing","volume":"115","author":"Watano","year":"2001","journal-title":"Int. J. Powder Technol."},{"key":"ref_28","unstructured":"Sakamoto, Y., Tamura, Y., and Kawaguchi, K. (1981). Measuring Particle Size Distribution. (4288162), U.S. Patent, Available online: https:\/\/www.google.lt\/patents\/US4288162."},{"key":"ref_29","unstructured":"Penumadu, D., Zhao, R., and Steadman, E.F. (1981). Particle Size and Shape Distribution Analyser. (6960756), U.S. Patent, Available online: https:\/\/www.google.lt\/patents\/US6960756."},{"key":"ref_30","unstructured":"Lieber, K.J., Browne, I.B., and Tuttle, J. (2005). Control Feedback System and Method for Bulk Material Industrial Processes Using Automated Object or Particle. (6885904), U.S. Patent, Available online: https:\/\/www.google.lt\/patents\/US6885904."},{"key":"ref_31","unstructured":"Nase, T.F., and Vourisalo, Y.R. (1990). Procedure and Apparatus for Determining Size and\/or Shape Distribution. (1990012310), WO Patent, Available online: https:\/\/www.google.lt\/patents\/WO1990012310A1?cl=en."},{"key":"ref_32","unstructured":"Ettmuller, J., Reindel, K., and Schafer, M. (2007). Sample\u2018s Particle Individual, Three Dimensional form e.g. Powder Form, Automated Determining Method, Involves Observing Particles from Two Observation Directions, Where Particle Axis is Aligned along Line Transverse to Observation Direction. (102005055825), DE Patent, Available online: https:\/\/www.google.lt\/patents\/DE102005055825A1?cl=en."},{"key":"ref_33","unstructured":"Canty, T.M., O\u2019Brien, P.J., and Marks, C.P. (2006). Granular Product Inspection Device. (7009703), U.S. Patent, Available online: https:\/\/www.google.lt\/patents\/US7009703."},{"key":"ref_34","unstructured":"Niwa, T. (1995). Particle Size Measuring Device. (5379113), U.S. Patent, Available online: https:\/\/www.google.lt\/patents\/US5379113."},{"key":"ref_35","unstructured":"Schumann, M. (1994). Procedure for the Determination of Particle Size Distribution in Particle Mixtures. (5309215), U.S. Patent, Available online: https:\/\/www.google.lt\/patents\/US5309215."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.powtec.2016.05.012","article-title":"3D image segmentation for analysis of multisize particles in a packed particle bed","volume":"301","author":"Wang","year":"2016","journal-title":"Powder Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Priadarsini, S., Ganesan, S., Shanthi, C., and Pappa, N. (2014, January 8\u201310). Model based object recognition for particle size distribution. Proceedings of the 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramanathapuram, India.","DOI":"10.1109\/ICACCCT.2014.7019322"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"64","DOI":"10.5755\/j01.eie.23.6.19696","article-title":"A Real-Time Parallel Image Processing Approach on Regular PCs with Multi-Core CPUs","volume":"23","author":"Atasoy","year":"2017","journal-title":"Elektronika ir Elektrotechnika"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"62","DOI":"10.5755\/j01.eie.21.6.13764","article-title":"Prediction of the Optical Character Recognition Accuracy based on the Combined Assessment of Image Binarization Results","volume":"21","author":"Lech","year":"2015","journal-title":"Elektronika ir Elektrotechnika"},{"key":"ref_40","unstructured":"Kumara, J.J., Hayano, K., and Ogiwara, K. (2019, June 03). Fundamental Study on Particle Size Distribution of Coarse Materials by Image Analysis. Available online: https:\/\/www.researchgate.net\/publication\/301692754_Fundamental_study_on_particle_size_distribution_of_coarse_materials_by_image_analysis."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tadeusiewicz, R., Ogiela, L., and Ogiela, M.R. (2006, January 12\u201316). Cognitive Analysis Techniques in Business Planning and Decision Support Systems. Proceedings of the Artificial Intelligence and Soft Computing, Zakopane, Poland.","DOI":"10.1007\/11785231_108"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3","DOI":"10.5755\/j01.eie.24.4.21469","article-title":"Automatic Classification of Motor Impairment Neural Disorders from EEG Signals Using Deep Convolutional Neural Networks","volume":"24","author":"Vrbancic","year":"2018","journal-title":"Elektronika ir Elektrotechnika"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ferrari, S., Piuri, V., and Scotti, F. (2008, January 14\u201316). Image processing for granulometry analysis via neural networks. Proceedings of the 2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Instanbul, Turkey.","DOI":"10.1109\/CIMSA.2008.4595827"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Z. (2015, January 19\u201320). An estimation of coal density distributions by weight based on image analysis and MIV-SVM. Proceedings of the 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC.2015.7428731"},{"key":"ref_45","unstructured":"White, H. (1992). Artificial Neural Networks: Approximation and Learning Theory, Blackwell Publishers, Inc."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ogiela, L., Tadeusiewicz, R., and Ogiela, M.R. (2006, January 12\u201316). Cognitive Analysis in Diagnostic DSS-type IT Systems. Proceedings of the Artificial Intelligence and Soft Computing, Zakopane, Poland.","DOI":"10.1007\/11785231_101"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/7\/838\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:01:33Z","timestamp":1760187693000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/7\/838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,27]]},"references-count":46,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["sym11070838"],"URL":"https:\/\/doi.org\/10.3390\/sym11070838","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,6,27]]}}}