5 research outputs found

    Intelligent classification and data augmentation for high accuracy AI applications for quality assurance of mineral aggregates

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    In this work, a method for automatic analysis of natural aggregates using hyperspectral imaging and high-resolution RGB imaging combined with AI algorithms consisting of an intelligent deep-learning-based recognition routine in form of hybrid cascaded recognition routine, and a necessary demonstration setup are demonstrated. Mineral aggregates are an essential raw material for the production of concrete. Petrographic analysis represents an elementary quality assurance measure for the production of high-quality concrete. Petrography is still a manual examination by specially trained experts, and the difficulty of the task lies in a large intra-class variability combined with low inter-class variability. In order to be able to increase the recognition performance, innovative new classification approaches have to be developed. As a solution, this paper presents an innovative cascaded deep-learning-based classification and uses a deep-learning-based data augmentation method to synthetically generate images to optimize the results

    Korrektur von Abbildungsfehlern fΓΌr optische Messverfahren

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    Dieser Beitrag behandelt die QualitÀt und Genauigkeit von Bildverarbeitungssystemen zur Messung von Objekten. Eine Hauptursache von systematischen Abweichungen von Bildverarbeitungsmesssystemen wird nÀher beschrieben. Anhand der Untersuchung der Bilder wird die Verzeichnung des optischen Messsystems gemessen, mathematisch beschrieben und anschließend korrigiert. Hierbei ist das Ziel die Korrektur der Bildfehler, um somit die Genauigkeit von Messungen zu erhâhen. Diese Arbeit ist im Rahmen des Michail Lomonosov Stipendiumprogramms mit Unterstützung des DAAD und des Russischen Ministeriums für Forschung und Bildung entstanden

    Analysis and correction of errors of optical measuring systems based on CCD-sensors

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    Published in: Proceedings of the 14th Joint International IMEKO TC1 + TC7 + TC 13 Symposium : "Intelligent quality measurements - theory, education and training" ; in conjunction with the 56th IWK, Ilmenau University of Technology and the 11th SpectroNet Collaboration Forum ; 31. August - 2. September 2011, JenTower Jena, Germany. - Ilmenau : Univ.-Bibliothek, ilmedia, 2011. URN: urn:nbn:de:gbv:ilm1-2011imeko:

    The application of texture features to quality control of metal surfaces

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    Quality assessment is an important step in production processes of metal parts. This step is required in order to check whether surface quality meets the requirements. Progress in the field of computing technologies and computer vision gives the possibility of visual surface quality control with industrial cameras and image processing methods. Authors of different papers proposed various texture feature algorithms which are suitable for different fields of images processing. In this research 27 texture features were calculated for surface images taken in different lighting conditions. Correlation coefficients between these 2D texture features and 11 roughness 3D parameters were calculated. A strong correlation between 2D features and 3D parameters occurred for images captured under ring light conditions

    Application of microwave photonics in fiber optical sensors

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    Microwave photonics is a new scientific and technical area of research, which was formed as a result of intensive development of such fields as fiber, integrated and nonlinear optics, laser physics, optoelectronics and microelectronics. A positive trend in the field of microwave photonic devices development has appeared in recent decades. The trend is related to the fact that these devices can operate in ultra-high and super-high frequencies and microwave ranges, and have parameters, which are unattainable by conventional electronic devices. Technical characteristics of microwave photonic measuring systems are comparable with those of traditional fiber-optic sensors. This technology can be used both for creation of new measuring devices and improvement of existing other types of measuring systems. This paper presents an analytical review of microwave photonics application technologies in fiber-optic measuring instruments. The general design concept for microwave photonic fiber-optic measuring devices is considered in the first part of the review paper. Microwave photonic filters are presented, which are the key elements of microwave photonic fiber-optic measuring devices. Their design technologies are described with indication of the features, advantages and disadvantages. Methods for creation of microwave photonic finite impulse response filters with positive and negative coefficients are considered. The following sections are devoted directly to the analysis of microwave photonic fiber-optic measuring devices and contain classification of such devices according to their principle of operation. The classification of spectral and interferometric microwave photonic fiber-optic measuring devices with indication of their distinctive features is proposed. Experimental data of the most common sensors is presented and analyzed; the main characteristics and areas of their practical application are presented for each of them. New approaches and methods are considered for creation of microwave photonic measuring systems and improvement of tactical and technical characteristics of existing devices. Comparison between microwave photonic fiber-optic measuring devices and traditional fiber-optic measuring systems is performed. According to comparison results, conclusions can be drawn about applicability of microwave photonic fiber-optic measuring devices and advantages of their use compared to other fiber-optic sensors.Π Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½ΠΈΠΊΠ° являСтся Π½ΠΎΠ²Ρ‹ΠΌ Π½Π°ΡƒΡ‡Π½ΠΎ-тСхничСским Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΠ΅ΠΌ, ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠ΅ ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π»ΠΎΡΡŒ Π² Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π΅ ΠΈΠ½Ρ‚Π΅Π½-сивного развития Ρ‚Π°ΠΊΠΈΡ… областСй, ΠΊΠ°ΠΊ волоконная, ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Π»ΡŒΠ½Π°Ρ ΠΈ нСлинСйная ΠΎΠΏΡ‚ΠΈΠΊΠ°, лазСрная Ρ„ΠΈΠ·ΠΈΠΊΠ°, ΠΎΠΏΡ‚ΠΎ- ΠΈ микроэлСктроника. Π’ послСдниС дСсятилСтия Π½Π°Π±Π»ΡŽΠ΄Π°Π΅Ρ‚ΡΡ ΠΏΠΎΠ»ΠΎΠΆΠΈΡ‚Π΅Π»ΡŒΠ½Π°Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° ΠΏΠΎ созданию Ρ€Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Ρ… устройств, эта тСндСнция связана с Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡ‚ΡŒΡŽ ΡΠΎΠ·Π΄Π°Π²Π°Ρ‚ΡŒ устройства ΡƒΠ»ΡŒΡ‚Ρ€Π°Π²Ρ‹ΡΠΎΠΊΠΈΡ… ΠΈ свСрхвысоких частот с ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°ΠΌΠΈ, нСдостиТимыми ΠΎΠ±Ρ‹Ρ‡Π½Ρ‹ΠΌΠΈ элСктронными устройствами. Π₯арактСристики Ρ€Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Ρ… ΠΈΠ·ΠΌΠ΅Ρ€ΠΈ-Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… систСм сопоставимы с характСристиками Ρ‚Ρ€Π°Π΄ΠΈΡ†ΠΈΠΎΠ½Π½Ρ‹Ρ… Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-оптичСских Π΄Π°Ρ‚Ρ‡ΠΈΠΊΠΎΠ², данная Ρ‚Π΅Ρ…Π½ΠΎ-логия ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ использована ΠΊΠ°ΠΊ для создания Π½ΠΎΠ²Ρ‹Ρ… ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΈΠ±ΠΎΡ€ΠΎΠ², Ρ‚Π°ΠΊ ΠΈ для ΡƒΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ ΡƒΠΆΠ΅ ΡΡƒΡ‰Π΅ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΡ… ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… систСм Π΄Ρ€ΡƒΠ³ΠΈΡ… Ρ‚ΠΈΠΏΠΎΠ². Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ прСдставлСн аналитичСский ΠΎΠ±Π·ΠΎΡ€ способов примСнСния Ρ€Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Ρ… Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-оптичСских ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΈΠ±ΠΎΡ€Π°Ρ…. Π’ ΠΏΠ΅Ρ€Π²ΠΎΠΉ части ΠΎΠ±Π·ΠΎΡ€Π½ΠΎΠΉ ΡΡ‚Π°Ρ‚ΡŒΠΈ рассмотрСн ΠΎΠ±Ρ‰ΠΈΠΉ ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏ построСния Ρ€Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Ρ… Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-оптичСских ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΈΠ±ΠΎΡ€ΠΎΠ². ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»Π΅Π½Ρ‹ ΠΊΠ»ΡŽΡ‡Π΅Π²Ρ‹Π΅ элСмСнты ΠΏΠΎΠ΄ΠΎΠ±Π½ΠΎΠ³ΠΎ Ρ€ΠΎΠ΄Π° систСм β€” Ρ€Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Π΅ Ρ„ΠΈΠ»ΡŒΡ‚Ρ€Ρ‹. ΠžΠΏΠΈΡΠ°Π½Ρ‹ Ρ‚Π΅Ρ…-Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈΡ… построСния с ΡƒΠΊΠ°Π·Π°Π½ΠΈΠ΅ΠΌ особСнностСй, прСимущСств ΠΈ нСдостатков. РассмотрСны способы создания Ρ€Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Ρ… Ρ„ΠΈΠ»ΡŒΡ‚Ρ€ΠΎΠ² с ΠΊΠΎΠ½Π΅Ρ‡Π½ΠΎΠΉ ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½ΠΎΠΉ характСристикой с ΠΏΠΎΠ»ΠΎΠΆΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΠΈ ΠΎΡ‚Ρ€ΠΈΡ†Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹ΠΌΠΈ коэф-Ρ„ΠΈΡ†ΠΈΠ΅Π½Ρ‚Π°ΠΌΠΈ. ΠŸΠΎΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠ΅ Ρ€Π°Π·Π΄Π΅Π»Ρ‹ посвящСны нСпосрСдствСнно Π°Π½Π°Π»ΠΈΠ·Ρƒ Ρ€Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Ρ… Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-ΠΎΠΏΡ‚ΠΈΡ‡Π΅-ских ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΈΠ±ΠΎΡ€ΠΎΠ² ΠΈ содСрТат ΠΊΠ»Π°ΡΡΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡŽ Ρ‚Π°ΠΊΠΈΡ… устройств ΠΏΠΎ ΠΈΡ… ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΡƒ Ρ€Π°Π±ΠΎΡ‚Ρ‹. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° классификация ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΈ интСрфСромСтричСских Ρ€Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Ρ… Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-оптичСских ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΈΠ±ΠΎΡ€ΠΎΠ² с ΡƒΠΊΠ°Π·Π°Π½ΠΈΠ΅ΠΌ ΠΈΡ… ΠΎΡ‚Π»ΠΈΡ‡ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ². ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»Π΅Π½Ρ‹ ΠΈ ΠΏΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρ‹Π΅ Π΄Π°Π½Π½Ρ‹Π΅, основныС характСристики ΠΈ области практичСского примСнСния Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ распространСнных Π΄Π°Ρ‚Ρ‡ΠΈ-ΠΊΠΎΠ². РассмотрСны Π½ΠΎΠ²Ρ‹Π΅ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Ρ‹ ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ ΠΏΠΎ созданию Ρ€Π°Π΄ΠΈΠΎΡ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Ρ… ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… систСм ΠΈ ΡƒΠ»ΡƒΡ‡ΡˆΠ΅Π½ΠΈΡŽ Ρ‚Π°ΠΊΡ‚ΠΈΠΊΠΎ-тСхничСских характСристик ΡΡƒΡ‰Π΅ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΡ… ΠΏΡ€ΠΈΠ±ΠΎΡ€ΠΎΠ². ΠŸΡ€ΠΈΠ²Π΅Π΄Π΅Π½ΠΎ сопоставлСниС характСристик Ρ€Π°Π΄ΠΈΠΎ- Ρ„ΠΎΡ‚ΠΎΠ½Π½Ρ‹Ρ… Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-оптичСских ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΈΠ±ΠΎΡ€ΠΎΠ² ΠΈ Ρ‚Ρ€Π°Π΄ΠΈΡ†ΠΈΠΎΠ½Π½Ρ‹Ρ… Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-оптичСских Π΄Π°Ρ‚Ρ‡ΠΈΠΊΠΎΠ², ΠΏΠΎ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π°ΠΌ ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠ³ΠΎ ΠΌΠΎΠΆΠ½ΠΎ ΡΠ΄Π΅Π»Π°Ρ‚ΡŒ Π²Ρ‹Π²ΠΎΠ΄ ΠΎ примСнимости соврСмСнного Ρ‚ΠΈΠΏΠ° ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΈΠ±ΠΎΡ€ΠΎΠ², Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΎ прСимущСствах ΠΈΡ… использования ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с Π΄Ρ€ΡƒΠ³ΠΈΠΌΠΈ Π²ΠΎΠ»ΠΎΠΊΠΎΠ½Π½ΠΎ-оптичСскими Π΄Π°Ρ‚Ρ‡ΠΈΠΊΠ°ΠΌΠΈ
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