03673ntm a22005177i 4500 000677032 CZ-PrVSE 20200829134027.0 m d cr n|||||||||| 200829s2020 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Vagenknecht, Martin ISIS:112139 dis Deep learning solution for in-line microscopy monitoring and particle size analysis eng Deep learning solution for in-line microscopy monitoring and particle size analysis / Martin Vagenknecht 2020 ?? stran : digital, PDF soubor Vedoucí práce: Milan Bašta Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2020 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2020 We introduce a novel deep learning based image analysis method for in- line microscopy monitoring and particle size analysis. This methodology allows us to quantify the size of crystals from low-quality Particle Vision and Measurement (PVM) images taken during the crystallization process.PVM technology captures particles and particle mechanisms as they exist in the process. Due to the variability of concentrations and the size of the crystals, the appearance of the PVM images is hazy and the individual crystals are difficult to recognize, which makes the analysis of particle size challenging.The proposed method consists of two major components: a novel way to generate training image and label pairs with a high level of credibility via a Cycle-Consistent Adversarial Network (CycleGAN), and a Mask-RCNN model trained with the generated data for the particle detection task. Compared with manual labeling and conventional supervised deep learning approaches, which are normally labor-intensive and error-prone, the method is more automatic and objective since it can benefit from highly realistic training data synthesis.The method has been applied to images of two particle morphologies with varying particle sizes and solids concentrations (typical concentrations of actual industrial conditions). The results demonstrate that the method can successfully perform relative size comparisons for the lower end of the solids concentrations, suggesting that this method could be used to monitor crystallization mechanisms such as growth, agglomeration, and breakage in the lower end of the industrial concentration spectrum. Způsob přístupu: Internet statistika [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas crystallization deep learning CycleGAN object detection particle size analysis Bašta, Milan ISIS:51301 ths Zimmermann, Pavel, 1980- jo20251268570 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/70197/podrobnosti VŠKP v InSIS https://aleph.vse.cz/neverejny_soubor_prace.pdf Hlavní práce https://insis.vse.cz/zp/70197/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/70197/posudek/oponent/67689 Oponentura https://insis.vse.cz/zp/70197/podrobnosti dc:identifier NEPOSILAT VSKP vse70197 200826 70197