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Bengali, Umme Salma Yusuf. "Pixel classification of iris transillumination defects." Thesis, University of Iowa, 2012. https://ir.uiowa.edu/etd/3260.
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Wang, Hui. "Software Defects Classification Prediction Based On Mining Software Repository." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-216554.
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An important goal during the cycle of software development is to find and fix existing defects as early as possible. This has much to do with software defects prediction and management. Nowadays,many big software development companies have their own development repository, which typically includes a version control system and a bug tracking system. This has no doubt proved useful for software defects prediction. Since the 1990s researchers have been mining software repository to get a deeper understanding of the data. As a result they have come up with some software defects prediction models the past few years. There are basically two categories among these prediction models. One category is to predict how many defects still exist according to the already captured defects data in the earlier stage of the software life-cycle. The other category is to predict how many defects there will be in the newer version software according to the earlier version of the software defects data. The complexities of software development bring a lot of issues which are related with software defects. We have to consider these issues as much as possible to get precise prediction results, which makes the modeling more complex. This thesis presents the current research status on software defects classification prediction and the key techniques in this area, including: software metrics, classifiers, data pre-processing and the evaluation of the prediction results. We then propose a way to predict software defects classification based on mining software repository. A way to collect all the defects during the development of software from the Eclipse version control systems and map these defects with the defects information containing in software defects tracking system to get the statistical information of software defects, is described. Then the Eclipse metrics plug-in is used to get the software metrics of files and packages which contain defects. After analyzing and preprocessing the dataset, the tool(R) is used to build a prediction models on the training dataset, in order to predict software defects classification on different levels on the testing dataset, evaluate the performance of the model and comparedifferent models’ performance.
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Wilson, Duncan John. "Classification of defects using uncertainty in industrial web inspection." Thesis, University of London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286894.
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Balakrishnan, Harinarayanan. "FDICS : a vision-based system for identification and classification of fabric defects." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/8465.
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Allanqawi, Khaled Kh S. Kh. "A framework for the classification and detection of design defects and software quality assurance." Thesis, Kingston University, 2015. http://eprints.kingston.ac.uk/34534/.
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In current software development lifecyeles of heterogeneous environments, the pitfalls businesses have to face are that software defect tracking, measurements and quality assurance do not start early enough in the development process. In fact the cost of fixing a defect in a production environment is much higher than in the initial phases of the Software Development Life Cycle (SDLC) which is particularly true for Service Oriented Architecture (SOA). Thus the aim of this study is to develop a new framework for defect tracking and detection and quality estimation for early stages particularly for the design stage of the SDLC. Part of the objectives of this work is to conceptualize, borrow and customize from known frameworks, such as object-oriented programming to build a solid framework using automated rule based intelligent mechanisms to detect and classify defects in software design of SOA. The framework on design defects and software quality assurance (DESQA) will blend various design defect metrics and quality measurement approaches and will provide measurements for both defect and quality factors. Unlike existing frameworks, mechanisms are incorporated for the conversion of defect metrics into software quality measurements. The framework is evaluated using a research tool supported by sample used to complete the Design Defects Measuring Matrix, and data collection process. In addition, the evaluation using a case study aims to demonstrate the use of the framework on a number of designs and produces an overall picture regarding defects and quality. The implementation part demonstrated how the framework can predict the quality level of the designed software. The results showed a good level of quality estimation can be achieved based on the number of design attributes, the number of quality attributes and the number of SOA Design Defects. Assessment shows that metrics provide guidelines to indicate the progress that a software system has made and the quality of design. Using these guidelines, we can develop more usable and maintainable software systems to fulfil the demand of efficient systems for software applications. Another valuable result coming from this study is that developers are trying to keep backwards compatibility when they introduce new functionality. Sometimes, in the same newly-introduced elements developers perform necessary breaking changes in future versions. In that way they give time to their clients to adapt their systems. This is a very valuable practice for the developers because they have more time to assess the quality of their software before releasing it. Other improvements in this research include investigation of other design attributes and SOA Design Defects which can be computed in extending the tests we performed.
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Bond, Brian Jr. "Characterization of Wood Features Using Color, Shape, and Density Parameters." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30629.
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Automated defect detection methods allow the forest products industry to better utilize its resources by improving yield, reducing labor costs, and allowing minimum lumber grades to be utilized more intelligently. While many methods have been proposed on what sensors and processing methods should be used to detect and classify wood features, there exists a lack of understanding of what parameters are best used to differentiate wood features.
The goal of this research is to demonstrate that by having an in depth knowledge of how wood features are represented by color, shape, and density parameters, more accurate classification methods can be developed. This goal was achieved through describing wood features using parameters derived from color and x-ray images and characterizing the variability and interrelationships of these parameters, determining the effect of resolution and species on these relationships, and determining the importance and contribution of each parameter for differentiating between wood features using a statistical prediction model relating feature types to the parameters. Knots, bark pockets, stain and mineral streak, and clearwood were selected as features from red oak, (Quercus rubra), hard maple, (Acer saccharum), and Eastern white pine (Pinus stobus). Color (RGB and HSI), shape (eccentricity and roundness), and density (gray-scale values) parameters were measured.
Parameters were measured for each wood feature from images and parameter differences between feature types were tested using analysis of variance techniques (ANOVA) and Tukey's pairwise comparisons with a=0.05. Discriminant classifiers were then developed to demonstrate that an in-depth knowledge of how parameters relate between feature types could be used to develop the best possible classification methods. Classifiers developed using the knowledge of parameter relationships were found to provide higher classification accuracies for all features and species than those which used all parameters and where variable selection procedures had been used<
It was determined that differences exist between all feature types and can be characterized and classified based on two color means, one color standard deviation, the mean density, and a shape parameter. A reduction in image resolution was determined not to affect the relationship of parameters. For different species, the intensity of features was to be related to the intensity of clearwood. The ability to explain classification errors using the knowledge gained about feature parameters was demonstrated. This knowledge could be used to reduce future classification errors.
It was determined that combining parameters collected using multiple sensors increases classification accuracy of wood features. Shape and density were found not to provide good classification variables for features when used separately, but were found to contribute to classification of features when used with other parameters. The ability to differentiate between the feature types examined in this research was found be equal when using the RGB or HSI colorspace.Ph. D.
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Nouri, Arash. "Correlation-Based Detection and Classification of Rail Wheel Defects using Air-coupled Ultrasonic Acoustic Emissions." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/78139.
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Defected wheel are one the major reasons endangered state of railroad vehicles safety statue, due to vehicle derailment and worsen the quality of freight and passenger transportation. Therefore, timely defect detection for monitoring and detecting the state of defects is highly critical.
This thesis presents a passive non-contact acoustic structural health monitoring approach using ultrasonic acoustic emissions (UAE) to detect certain defects on different structures, as well as, classifying the type of the defect on them. The acoustic emission signals used in this study are in the ultrasonic range (18-120 kHz), which is significantly higher than the majority of the research in this area thus far. For the proposed method, an impulse excitation, such as a hammer strike, is applied to the structure. In addition, ultrasound techniques have higher sensitivity to both surface and subsurface defects, which make the defect detection more accurate. Three structures considered for this study are: 1) a longitudinal beam, 2) a lifting weight, 3) an actual rail-wheel. A longitudinal beam was used at the first step for a better understanding of physics of the ultrasound propagation from the defect, as well, develop a method for extracting the signature response of the defect. Besides, the inherent directionality of the ultrasound microphone increases the signal to noise ratio (SNR) and could be useful in the noisy areas. Next, by considering the ultimate goal of the project, lifting weight was chosen, due to its similarity to the ultimate goal of this project that is a rail-wheel. A detection method and metric were developed by using the lifting weight and two type of synthetic defects were classified on this structure. Also, by using same extracted features, the same types of defects were detected and classified on an actual rail-wheel.Master of Science
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Niewalda, Tobias. "Deep Learning Based Classification of Rail Defects Using On-board Monitoring in the Stockholm Underground." Thesis, KTH, Spårfordon, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273576.
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The purpose of this work is to find out if an artificial neural network can be useful purpose of this work is to find out if an artificial neural network can be useful in order to detect rail squats with the existing Quiet Track Measurement System (QTMS). Squats are surface-initiated rail defects which arise due to rolling contact fatigue. The monitoring system, installed on seven trains running on the green line in the Stockholm underground, aims to improve the maintenance process. The early detection and surveillance of defects helps to extend the service life of the tracks and reduce operating costs. An artificial neural network is used to analyse the the continuously recorded measurements, which consist of vertical bogie acceleration and surrounding noise, each sampled with a frequency of 22 kHz.In particular, the power spectral density as input for multi-layer Fully-connected Neural Network (FNN) has proven to be promising for accurate squat predictions. The supervised learning was carried out according to the one-vs-all principle, i.e. squats versus all other events. A two-hidden-layer FNN has finally been chosen to complement the QTMS. The usage of the full available frequency range from almost DC up to 11kHz, but minimum 7 kHz, allows good prediction with only low false prediction rates. When concatenating all six measurement channels to a single classifier input, an accuracy of over 96% for the squat class and up to 99.98% can in total be achieved. The chosen network type also showed high stability despite quite strong parameter variations and a massive under-representation of squat observations in the measurement data.However, since limited maintenance information about actual squats is available for labelling and testing, more evaluation is needed. The correct identification of mis-labelled squats indicates the high potentials of artificial neural networks.Squats uppkommer på rälsytor på grund av rullkontaktutmattning och kan ställa till stora problem om de inte upptäcks i tid. Att upptäcka fel i ett tidigt skede kan minska underhållskostnaderna. Syftet med det här examensarbetet är att studera om man med hjälp av ett neuralt nätverk kan detektera så kallade räls-squats med hjälp ett mätsystem som heter Quiet Track Measurement System (QTMS). Mätsystemet är installerat hos sju tåg på den gröna linjen i Stockholms tunnelbana. Systemet kan användas för att övervaka rälsslitage och kan därför effektivisera underhållet. Den här rapporten presenterar ett artificiellt neuralt nätverk för att kontinuerligt kunna analysera mätdata. Mätdata består av vertikal boggiacceleration och ljudmätningar, båda samplas med frekvensen 22 kHz.Frekvensdomänrepresentationen av uppmätta data i form av spektraltätheten i kombination med flerskikts- och helt anslutna neurala nätverk (FNN) visade sig vara lovande för korrekta förutsägelser. övervakad inlärning tillämpades enligt the one-verus-all principen, dvs antingen har man squats eller så har man inte squats. En artificiell neuron-modell med två dolda lager valdes till slut för att komplettera existerande mätsystem. Användningen av ett brett frekvens-område från nästan DC upp till 7 kHz möjliggör god förutsägelse med bara låga förutsägelser av falska squats. När man sammanlänkar alla sex mätkanaler till en enskild klassificeringsinput kan man uppnå en noggrannhet över 96%, som högst kan man uppnå 99.98%. Nätverket visade också hög stabilitet trots ganska starka parametervariationer och en obalans mellan tillgängliga data för de två klasserna.Eftersom bara några få underhållsprotokoll fanns tillgängliga krävs mer utvärdering, men korrekt identifiering av felklassificerade squats indikerar att den verkliga noggrannheten kan bli ännu bättre. Detta ger också förutsättningar för att snabbare kunna prediktera squats.
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Partyka-Jankowska, Ewa, Bogdan Sepiol, Fritz Gröstlinger, Gero Vogl, Józef Korecki, Tomasz Ślęzak, Marcin Zając, and Aleksandr Chumakov. "Dynamic of defects in an iron monolayer on W (110)." Diffusion fundamentals 11 (2009) 51, S. 1-2, 2009. https://ul.qucosa.de/id/qucosa%3A12963.
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Rust, Stephanus Marthinus. "Classification of timber from Pinus radiata trees exposed to forest fires." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/98097.
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Thesis (MSc)--Stellenbosch University, 2015.ENGLISH ABSTRACT: This study aimed to classify wood from trees that were exposed to forest fires with regards to their end use. Exposure to high temperatures over time is known to degrade wood in various ways. This degradation could limit the end use by altering mechanical, chemical and physical properties, leading to difficulty in processing or failing to meet required specifications for various grades. In this study wood from Pinus radiata trees that were exposed to forest fires of different levels of heat intensity was analysed with regards to its anatomical and physical changes. Trees were visually classified into three classes of burn severity. Moisture content measurements were taken from 135 standing trees, divided among the three classes. 30 trees, 10 from each of the three classes, were sampled and used for CT analysis. Samples were taken to include growth from before and after the fire. Two samples were taken from each tree, one from the charred and one from the uncharred side. The CT data was analysed and used to measure properties like growth ring width, cell wall thickness, lumen diameter and cell wall density. The data was used to compare properties from the charred and uncharred sides within a given year, as well as compare properties between years. The study showed that there were significant differences in the MC between the burnt and unburnt sides of trees from classes 2 and 3. The difference between the MC measurements on the burnt sides of three classes differed significantly from each other. Lightness measurements were taken on samples from classes 2 and 3. These samples showed no significant difference between the burnt and unburnt sides for either of the two classes. The samples from the less exposed class were lighter, but not significantly so. The macroscopic wood density was determined using core samples. A decrease in wood density was observed with an increase in fire exposure. The mean densities for all three classes however still fulfilled the requirements for structural timber set by the SABS. Growth ring width, cell wall thickness and lumen diameter analysis gave varied results, with some cases showing a decline in properties while others were seemingly unaffected. For many of the outcomes of this study, results found by previous studies could not be reproduced.
AFRIKAANSE OPSOMMING: Hierdie studie het gepoog om bome wat aan plantasiebrande blootgestel is volgens hul eindgebruik te klassifiseer. Dit is bekend dat blootstelling aan hoë tempreature hout in vele maniere afbreuk. Hierdie afbreuking kan die eindgebruik van die hout beperk deur die meganiese, fisiese en chemiese eienskappe sodanig te verander dat dit kan lei tot probleme met verwerking of ongeskiktheid vir sterktegrade. In hierdie studie is Pinus radiata bome wat aan plantasiebrande van verskillende grade blootgestel is ondersoek in terme van hul fisiese en anatomiese veranderinge. Bome is visueel in drie klasse van verskillende brandskade gegroepeer. Voglesings is op 135 staande bome, verdeel tussen die drie klasse, geneem. Monsters is van 30 bome, 10 uit elke klas, geneem vir CT analiese. Monsters is so geneem dat dit groei van voor en na die brand ingesluit het. Daar is twee monsters van elke boom geneem, een van die gebrande en een van die ongebrande kant. Die CT data is geanalieseer en gebruik om eienskappe soos jaarringwydte, selwanddikte, lumendiameter en selwand digtheid te meet. Die data is gebruik om eienskappe tussen die gebrande en ongebrande kante, sowel as tussen jare te vergelyk. Die studie het gewys dat daar noemenswaardige verskille is tussen die voginhoud van die gebrande en ongebrande kante van bome uit klasse 2 en 3. Die voginhoud van die gebrande kante van al drie klasse verkil ook noemenswaardig van mekaar. Ligtheidmetings is gedoen op monsters van klasse 2 en 3. Die monsters het nie ‘n noemenswaardige verskil tussen die gebrande en ongebrande kante getoon nie. Alhoewel die klas 2 monsters ligter vertoon het as die klas 3 monsters, was die verskil nie betekenisvol nie. Houtdigtheid is bepaal deur fisiese metings op die monsters wat vir die CT skandering gebruik is te doen. ‘n Daling in digtheid met ‘n toename in blootstelling aan die brand het duidelik na vore gekom. Die digtheid is egter nog hoog genoeg om aan die vereistes vir strukturele hout te voldoen, soos die die SABS bepaal. Jaarringwydte, selwanddikte en lumen diameter het wisselende resultate opgelewer, met sommige gevalle wat ‘n afname in eienskappe wys en ander wat ooglopend onveranderd was. Vir vele van hierdie uitkoms kon die resultate van vorige studies nie bevestig word nie.
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Ghaffarian, Niasar Mohamad. "Partial Discharge Signatures of Defects in Insulation Systems Consisting of Oil and Oil-impregnated Paper." Licentiate thesis, KTH, Elektroteknisk teori och konstruktion, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105785.
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Partial discharge measurement is a common method for monitoring and diagnostics of power transformers, and can detect insulation malfunctions before they lead to failure. Different parameters extracted from the measured PD activity can be correlated to the PD source, and as a result it is possible to identify the PD source by analyzing the PD activity. In this thesis, possible defects that could cause harmful PDs in transformers were investigated. These defects include corona in oil, a void in pressboard, a metal object at floating potential, surface discharge in oil, a free bubble in oil and small free metallic particles in oil. The characteristics of disturbing discharge sources were analyzed, like corona in air, surface discharge in air, and discharge from an unearthed object near to the test setup. The PD activity was recorded both in the time domain and phase domain, and possible characteristics for each PD pattern and waveform were extracted in order to find the best characteristic for the purpose of classification. The results show that in the phase domain parameters such as phase of occurrence, repetition rate and shape of PD Pattern are most suitable for classification while magnitude of discharge can only be useful in specific cases. The results show that the PD waveforms correlated to different defects are similar; however the time domain data include all the information from the phase domain, and also has the power to identify the number of PD sources. The PD dependency on temperature was investigated on the four test objects including surface discharges in oil, corona in oil, bubble discharges in oil, and metal object at floating potential. The effect of humidity was investigated for corona in oil. The results show that at higher temperature the corona activity in oil and PD activity due to a metal object at floating potential in oil decrease. However, for a bubble in oil and for surface discharge in oil the PD activity increases with the increase of the oil temperature. It was shown that the amount of moisture in oil has a strong impact on number of corona pulses in oil. The last part focused on ageing of oil-impregnated paper due to PD activity. Investigation was made of the behavior of PD activity and its corresponding parameters such as PD repetition rate and magnitude, from inception until complete puncture breakdown. The results show that both the number and magnitude of PD increase over time until they reach to a peak value. After this point over time both curves decrease slowly, and eventually full breakdown occurs. The effect of thermal ageing of oil impregnated paper on time to breakdown and PD parameters was investigated. The results show that thermal aging of oil-impregnated paper increases the number and magnitude of PD. Dielectric spectroscopy was performed on the samples before and after PD ageing and the result was used in order to explain the behavior of PD over time.QC 20121129
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Schmidt, Florian, Stefan Müller, Wenckstern Holger von, Gabriele Benndorf, Rainer Pickenhain, and Marius Grundmann. "Impact of strain on electronic defects in (Mg,Zn)O thin films." American Institute of Physics, 2014. https://ul.qucosa.de/id/qucosa%3A31192.
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We have investigated the impact of strain on the incorporation and the properties of extended and
point defects in (Mg,Zn)O thin films by means of photoluminescence, X-ray diffraction, deep-level
transient spectroscopy (DLTS), and deep-level optical spectroscopy. The recombination line Y2,
previously detected in ZnO thin films grown on an Al-doped ZnO buffer layer and attributed to tensile
strain, was exclusively found in (Mg,Zn)O samples being under tensile strain and is absent in
relaxed or compressively strained thin films. Furthermore a structural defect E3′ can be detected
via DLTS measurements and is only incorporated in tensile strained samples. Finally it is shown
that the omnipresent deep-level E3 in ZnO can only be optically recharged in relaxed ZnO samples.
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Florio, Claudia, R. Aveta, G. Calvanese, and B. Naviglio. "Advanced diagnostics and innovative solutions for leather defects - 78: the problem of yellowing." Verein für Gerberei-Chemie und -Technik e. V, 2019. https://slub.qucosa.de/id/qucosa%3A34231.
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Providing peculiar enhanced features to leather items is a factor of primary importance for the marketing of high-end articles; although the tanning production is oriented to satisfy a wide market range, it is mainly in the 'high end' and 'premium luxury' categories that the quality properties of the material are more expressed, indeed, and where the main current challenges have been focalized, in terms of technological innovation, sustainability and product quality.
The light-coloured leathers belongs to the category of materials designed especially for the luxury market. For this type of articles, the uniformity of the colour and the agreeableness of the overall surface
appearance are crucial requirements for the most of international fashion and luxury brands. One of the most common and undesirable defects of this type of article is the alteration of the color, with
particular reference to the effects of yellowing of the surface of the material. There are several causes able to contribute to this type of defects, due to the complexity of the matrix and to the variability of traditional or innovative production processes used: from the fragility, photosensitivity and thermo-sensitivity of the finishing polymers, to the chemical instability of some finishing pigments, further than the presence of photosensitive additives, the migration of skin components or assembly components of the articles (fats, fillers, plasticizers, glues, etc.), up to the indirect contribution of environmental and thermo-climatic factors able to affect negatively the performance of the material.
SSIP, which has always been involved in research and consulting activities for the leather industry with regards to defect monitoring, through this work, would offer an overview of all the main tools for advanced diagnostics (with particular reference to Scanning Electronic Microscopy and to chromatographic and spectroscopic methods) aimed to the identification of the causes of yellowing, beside to explore innovative solutions for the development of strategies for the resolution and / or minimization of the problem of yellowing.
The technical solutions will include innovative tanning processes, innovative finishing methods, and leather surface treatments carried out in order to provide a sensible attenuation of surface absorption of IR
(infrared) and UV (ultraviolet)-visible radiation.
Take-Away:
Advanced Diagnostics and innovative solutions for leather yellowing
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Bastin, Dirk. "Reassignment of oxygen-related defects in CdTe and CdSe." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-172319.
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This thesis reassigns the OTe-VCd complex in CdTe and the OSe-VCd complex in CdSe to a sulfur-dioxygen complex SO2*, and the OCd defect in CdSe to a VCdH2 complex using Fourier transformed infrared absorption spectroscopy. The publications of the previous complexes were investigated by theoreticians who performed first-principle calculations of theses complexes. The theoreticians ruled out the assignments and proposed alternative defects, instead. The discrepancy between the experimentally obtained and theoretically proposed defects was the motivation of this work.
Two local vibrational modes located at 1096.8 (v1) and 1108.3 cm-1 (v2) previously assigned to an OTeV_Cd complex are detected in CdTe single crystals doped with CdSO4 powder. Five weaker additional absorption lines accompanying v1 and v2 could be detected. The relative intensities of the absorption lines match a sulfur-dioxygen complex SO2* having two configurations labeled v1 and v2. A binding energy difference of 0.5+-0.1meV between the two configurations and an energy barrier of 53+-4 meV separating the two configurations are determined. Uniaxial stress applied to the crystal leads to a splitting of the absorption lines which corresponds to an orthorhombic and monoclinic symmetry for v1 and v2, respectively.
In virgin and oxygen-doped CdSe single crystals, three local vibrational modes located at 1094.1 (gamma_1), 1107.5 (gamma_2), and 1126.3 cm-1 (gamma_3) previously attributed to an OSe-VCd complex could be observed. The signals are accompanied by five weaker additional absorption features in their vicinity. The additional absorption lines are identified as isotope satellites of a sulfur-dioxygen complex SO2* having three configurations gamma_1, gamma_2, and gamma_3. IR absorption measurements with uniaxial stress applied to the CdSe crystal yield a monoclinic C1h symmetry for gamma_1 and gamma_2. The SO2* complex is stable up to 600 C. This thesis assigns the v- lines in CdTe and gamma-lines in CdSe to local vibrational modes of a sulfur-dioxygen complex SO2*.
A hydrogen-doped CdSe single crystal exhibits two absorption lines at 1992 (SeH$_\\parallel$) and 2001 cm-1 (SeH$_\\perp$). Both signals show a red-shift in frequency to 1454 (SeD$_\\parallel$) and 1461 cm-1 (SeD$_\\perp$) when hydrogen is replaced by deuterium. This frequency shift in combination with the fine structure of the absorption lines arising from Se isotopes yields a VCdH2 defect giving rise to SeH$_\\parallel$ and SeH$_\\perp$. This contradicts the previously assignment of the two absorptions lines to an OCd defect. The SeH$_\\parallel$ and SeH$_\\perp$ vibrational modes are found to be aligned parallel and perpendicular to the c-axis of the crystal, respectively. The VCdH2 defect is stable up to a temperature of 525 C.
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Rönnqvist, Johannes, and Johannes Sjölund. "A Deep Learning Approach to Detection and Classification of Small Defects on Painted Surfaces : A Study Made on Volvo GTO, Umeå." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160194.
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In this thesis we conclude that convolutional neural networks, together with phase-measuring deflectometry techniques, can be used to create models which can detect and classify defects on painted surfaces very well, even compared to experienced humans. Further, we show which preprocessing measures enhances the performance of the models. We see that standardisation does increase the classification accuracy of the models. We demonstrate that cleaning the data through relabelling and removing faulty images improves classification accuracy and especially the models' ability to distinguish between different types of defects. We show that oversampling might be a feasible method to improve accuracy through increasing and balancing the data set by augmenting existing observations. Lastly, we find that combining many images with different patterns heavily increases the classification accuracy of the models. Our proposed approach is demonstrated to work well in a real-time factory environment. An automated quality control of the painted surfaces of Volvo Truck cabins could give great benefits in cost and quality. The automated quality control could provide data for a root-cause analysis and a quick and efficient alarm system. This could significantly streamline production and at the same time reduce costs and errors in production. Corrections and optimisation of the processes could be made in earlier stages in time and with higher precision than today.I den här rapporten visar vi att modeller av typen convolutional neural networks, tillsammans med phase-measuring deflektometri, kan hitta och klassificera defekter på målade ytor med hög precision, även jämfört med erfarna operatörer. Vidare visar vi vilka databehandlingsåtgärder som ökar modellernas prestanda. Vi ser att standardisering ökar modellernas klassificeringsförmåga. Vi visar att städning av data genom ommärkning och borttagning av felaktiga bilder förbättrar klassificeringsförmågan och särskilt modellernas förmåga att särskilja mellan olika typer av defekter. Vi visar att översampling kan vara en metod för att förbättra precisionen genom att öka och balansera datamängden genom att förändra och duplicera befintliga observationer. Slutligen finner vi att kombinera flera bilder med olika mönster ökar modellernas klassificeringsförmåga väsentligt. Vårt föreslagna tillvägagångssätt har visat sig fungera bra i realtid inom en produktionsmiljö. En automatiserad kvalitetskontroll av de målade ytorna på Volvos lastbilshytter kan ge stora fördelar med avseende på kostnad och kvalitet. Den automatiska kvalitetskontrollen kan ge data för en rotorsaksanalys och ett snabbt och effektivt alarmsystem. Detta kan väsentligt effektivisera produktionen och samtidigt minska kostnader och fel i produktionen. Korrigeringar och optimering av processerna kan göras i tidigare skeden och med högre precision än idag.
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Rashetnikava, Alena, Alexander Germanov, Irina Valikova, and Andrei Nazarov. "Molecular dynamics simulation of atomic structure in the vicinity of point defects in FCC and BCC metals." Diffusion fundamentals 11 (2009) 52, S. 1-2, 2009. https://ul.qucosa.de/id/qucosa%3A14014.
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Reitemeier, Bernd, Michael Unger, Gert Richter, Barbara Ender, Ursula Range, and Jutta Markwardt. "Clinical Test of Masticatory Efficacy in Patients with Maxillary/Mandibular Defects Due to Tumors." Karger, 2012. https://tud.qucosa.de/id/qucosa%3A27602.
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Background: The goal of the study was to evaluate the masticatory efficacy in patients who had been provided with resection prostheses after tumor removal in the maxillary/ mandibular region. These patients complained of impairment of masticatory function. Patients and Methods: 3 groups of patients were compared under clinical-experimental conditions. A uniform chewing material was masticated by the participants under standardized conditions. A sieving procedure was used to evaluate the masticatory efficacy. Analysis of the particle sizes and particle masses obtained was performed with the aid of computers. Results: The results showed that the masticatory efficacy of the patients with resection prostheses was the lowest of the 3 groups compared. The number of existing supporting zones and the location of the defect were found to be important influencing factors. Recording of the dietary habits of all patients was performed using a standardized dietary questionnaire. These data were analyzed using the corresponding software of the German Nutrition Society. With regard to the patients with resection prostheses, it was revealed that they often switched to food that did not require mastication. Conclusions: A nutritional guideline for patients with resection prostheses was developed, which is available for downloading free of charge on the Internet.Hintergrund: Ziel der Untersuchung war die Prüfung der Kaueffektivität bei Patienten, die mit Resektionsprothesen nach Tumorentfernung im Kieferbereich versorgt worden waren. Diese Patienten klagten über eine Einschränkung der mastikatorischen Funktion. Patienten und Methoden: Unter klinisch xperimentellen Bedingungen erfolgte der Vergleich von 3 Patientengruppen. Unter standardisierten Bedingungen zerkleinerten die Patienten einheitliches Kaugut. Zur Bewertung der Kaueffektivität wurde ein Siebverfahren eingesetzt. Die Auswertung der ermittelten Partikelgrößen und Partikelmassen erfolgte computergestützt. Ergebnisse: Die Ergebnisse zeigten, dass im Vergleich der 3 Gruppen die Kaueffektivität der Patienten mit Resektionsprothesen am geringsten war. Die Zahl der vorhandenen Stützzonen des Restgebisses und die Defektlokalisation wurden als bedeutsame Einflussfaktoren ermittelt. Die Erfassung der Ernährungsgewohnheiten aller Patienten erfolgte mittels eines standardisierten Ernährungsfragebogens. Diese Daten wurden mit der zugehörigen Software der Deutschen Gesellschaft für Ernährung ausgewertet. Bei den Patienten mit Resektionsprothesen zeigte sich, dass diese auf Nahrungsmittel ausweichen, die kein Kauen erfordern. Schlussfolgerungen: Es wurde eine Ernährungsrichtlinie für Patienten mit Resektionsprothesen abgeleitet, die zum kostenfreien Herunterladen im Internet zur Verfügung steht.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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Kutsche, Kerstin, Walter Werner, Oliver Bartsch, der Wense Axel von, Peter Meinecke, and Andreas Gal. "Microphthalmia with linear skin defects syndrome (MLS): a male with a mosaic paracentric inversion of Xp." Karger, 2002. https://tud.qucosa.de/id/qucosa%3A27747.
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The microphthalmia with linear skin defects syndrome (MLS) is an X-linked dominant disorder with male lethality. In the majority of the patients reported, the MLS syndrome is caused by segmental monosomy of the Xp22.3 region. To date, five male patients with MLS and 46,XX karyotype (“XX males”) have been described. Here we report on the first male case with MLS and an XY complement. The patient showed agenesis of the corpus callosum, histiocytoid cardiomyopathy, and lactic acidosis but no microphthalmia, and carried a mosaic subtle inversion of the short arm of the X chromosome in 15% of his peripheral blood lymphocytes, 46,Y,inv(X)(p22.13∼22.2p22.32∼22.33)[49]/46,XY[271]. By fluorescence in situ hybridization (FISH), we showed that YAC 225H10 spans the breakpoint in Xp22.3. End-sequencing and database analysis revealed a YAC insert of at least 416 kb containing the genes HCCS and AMELX, and exons 2–16 of ARHGAP6. Molecular cytogenetic data suggest that the Xp22.3 inversion breakpoint is located in intron 1 of ARHGAP6, the gene encoding the Rho GTPase activating protein 6. Future molecular studies in karyotypically normal female MLS patients to detect submicroscopic rearrangements including the ARHGAP6 gene as well as mutation screening of ARHGAP6 in patients with no obvious chromosomal rearrangements will clarify the role of this gene in MLS syndrome.Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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Bernhardt, Anne, Birgit Paul, and Michael Gelinsky. "Biphasic Scaffolds from Marine Collagens for Regeneration of Osteochondral Defects." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234963.
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Background: Collagens of marine origin are applied increasingly as alternatives to mammalian collagens in tissue engineering. The aim of the present study was to develop a biphasic scaffold from exclusively marine collagens supporting both osteogenic and chondrogenic differentiation and to find a suitable setup for in vitro chondrogenic and osteogenic differentiation of human mesenchymal stroma cells (hMSC).
Methods: Biphasic scaffolds from biomimetically mineralized salmon collagen and fibrillized jellyfish collagen were fabricated by joint freeze-drying and crosslinking. Different experiments were performed to analyze the influence of cell density and TGF-β on osteogenic differentiation of the cells in the scaffolds. Gene expression analysis and analysis of cartilage extracellular matrix components were performed and activity of alkaline phosphatase was determined. Furthermore, histological sections of differentiated cells in the biphasic scaffolds were analyzed.
Results: Stable biphasic scaffolds from two different marine collagens were prepared. An in vitro setup for osteochondral differentiation was developed involving (1) different seeding densities in the phases; (2) additional application of alginate hydrogel in the chondral part; (3) pre-differentiation and sequential seeding of the scaffolds and (4) osteochondral medium. Spatially separated osteogenic and chondrogenic differentiation of hMSC was achieved in this setup, while osteochondral medium in combination with the biphasic scaffolds alone was not sufficient to reach this ambition.
Conclusions: Biphasic, but monolithic scaffolds from exclusively marine collagens are suitable for the development of osteochondral constructs.
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Somerson, Jeremy. "Mesenchymal Stem Cell Constructs for Repair of Focal Cartilage Defects in an Ovine Model." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-214417.
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Focal cartilage defects (FCD) of the knee joint remain a difficult area of treatment for orthopaedic surgeons, as they often progress to generalized osteoarthritis (OA). Osteochondral autograft transfer (OAT) to the damaged cartilage area has shown promise, but this has been associated with pain and bleeding at the site of graft harvest. The use of mesenchymal stem cells (MSCs) in a matrix to regenerate articular cartilage has been proposed. This work describes a prospective case-control series comparing OAT with a novel, MSC-seeded scaffold graft in the stifle joints of healthy merino sheep. The triphasic grafts were composed of a beta-tricalcium phosphate osseous phase, an intermediate activated plasma phase and a collagen I hydrogel cartilage phase.
The osseous and cartilage phases were seeded with autologous MSCs.
All sheep underwent creation of a full-thickness, 4.0 mm diameter FCD (n=20) followed by six weeks of unrestricted activity, allowing the defects to degenerate naturally. At six weeks, half of the lesions were treated with OAT and half with the triphasic engineered grafts.
At 6-month and 12-month follow-up, no significant differences were noted between groups with regard to overall histological scores. Macroscopic and biomechanical analysis at 12 months showed no significant differences between groups. In summary, autologous MSC-seeded implants showed comparable repair quality to OAT without the associated donor site morbidity.
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Bowes, David Hutchinson. "Factors affecting the performance of trainable models for software defect prediction." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/10978.
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Context. Reports suggest that defects in code cost the US in excess of $50billion per year to put right. Defect Prediction is an important part of Software Engineering. It allows developers to prioritise the code that needs to be inspected when trying to reduce the number of defects in code. A small change in the number of defects found will have a significant impact on the cost of producing software. Aims. The aim of this dissertation is to investigate the factors which a ect the performance of defect prediction models. Identifying the causes of variation in the way that variables are computed should help to improve the precision of defect prediction models and hence improve the cost e ectiveness of defect prediction. Methods. This dissertation is by published work. The first three papers examine variation in the independent variables (code metrics) and the dependent variable (number/location of defects). The fourth and fifth papers investigate the e ect that di erent learners and datasets have on the predictive performance of defect prediction models. The final paper investigates the reported use of di erent machine learning approaches in studies published between 2000 and 2010. Results. The first and second papers show that independent variables are sensitive to the measurement protocol used, this suggests that the way data is collected a ects the performance of defect prediction. The third paper shows that dependent variable data may be untrustworthy as there is no reliable method for labelling a unit of code as defective or not. The fourth and fifth papers show that the dataset and learner used when producing defect prediction models have an e ect on the performance of the models. The final paper shows that the approaches used by researchers to build defect prediction models is variable, with good practices being ignored in many papers. Conclusions. The measurement protocols for independent and dependent variables used for defect prediction need to be clearly described so that results can be compared like with like. It is possible that the predictive results of one research group have a higher performance value than another research group because of the way that they calculated the metrics rather than the method of building the model used to predict the defect prone modules. The machine learning approaches used by researchers need to be clearly reported in order to be able to improve the quality of defect prediction studies and allow a larger corpus of reliable results to be gathered.
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Markwardt, Jutta, Günther Pfeifer, Uwe Eckelt, and Bernd Reitemeier. "Analysis of Complications after Reconstruction of Bone Defects Involving Complete Mandibular Resection Using Finite Element Modelling." Karger, 2007. https://tud.qucosa.de/id/qucosa%3A27607.
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Background: In a retrospective study, risk factors for complications after the bridging of mandibular defects using reconstruction plates were reviewed. Especially the loosening of the plate-screw-mandible complex should be analyzed with a finite element model in order to reduce plate complications in future. Patients and Methods: We examined 60 patients who underwent a treatment with reconstruction plates after tumor resection during a period of 10 years. The problem of screw loosening was additionally reviewed by means of a finite element study, and a model for the loosening process was developed. Results: Our postoperative examination showed that 26 patients suffered from complications that required an early removal of the plate. These complications were oral or extraoral plate exposures, the looseness of screws with or without plate displacement, and plate fractures. Thereby, we noticed that maxillary and mandibular areas of opposing teeth, the size of the mandible defect, and the crossing of the orofacial midline are all risk factors for plate complications. On the basis of the finite element model, a modified arrangement of the screws was derived. Hence, a new type of resection plate was established. Conclusions: By repositioning the screw holes along the long axis of the plate, the transition from tensile force to torque force of the screws in the screw-plate-bone complex can be minimized. Thereby, the complication of screw loosening will be considerably reduced.Hintergrund: In einer retrospektiven Studie wurden Risikofaktoren für Komplikationen nach Überbrückung von Unterkieferdefekten mit Rekonstruktionsplatten geprüft. Insbesondere die Lockerungsvorgänge des Schrauben- Platten-Unterkiefer-Verbundes sollten mit einer Finite- Elemente-Modellierung analysiert werden, um in Zukunft eine Reduzierung der Plattenkomplikationen erreichen zu können. Patienten und Methoden: Es wurden 60 Patienten untersucht, welche im Zeitraum von 10 Jahren im Rahmen von Tumoroperationen mit Rekonstruktionsplatten versorgt wurden. Das Problem der Lockerung der Plattenschrauben wurde zusätzlich mittels einer Finite-Elemente-Studie überprüft und ein Modell für den Lockerungsvorgang erarbeitet. Ergebnisse: Die Nachuntersuchungen ergaben, dass bei 26 Patienten die Platte wegen Komplikationen vorzeitig entfernt werden musste. Die Komplikationen traten als orale und extraorale Plattenfreilage, als Schraubenlockerung ohne oder mit Plattendislokationen und als Plattenbrüche auf. Dabei konnte festgestellt werden, dass bestehende Stützzonen des körpereigenen Restgebisses, die Größe des Unterkieferdefektes und dessen Mittellinienüberschreitung Risikofaktoren für Plattenkomplikationen darstellen. Anhand der Finite-Elemente-Modellierung wurde eine veränderte Schraubenanordnung abgeleitet. Daraus resultiert eine neue Form der Resektionsplatte. Schlussfolgerungen: Durch die Verschiebung der Schraubenlöcher aus der Längsachse der Platte kann der Übergang von der Zugbelastung zur Drehmomentbelastung der Schrauben im Schrauben-Platten-Knochen-Verbund der Platte minimiert werden. Dadurch werden Schraubenlockerungen als Komplikationen wesentlich seltener auftreten.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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Di, Pietro Riccardo, Tim Erdmann, Naixiang Wang, Xuhai Liu, David Gräfe, Johannes Lenz, Josef Brandt, et al. "The impact of molecular weight, air exposure and molecular doping on the charge transport properties and electronic defects in dithienyldiketopyrrolopyrrole- thieno[3,2-b]thiophene copolymers." Royal Society of Chemistry, 2016. https://tud.qucosa.de/id/qucosa%3A36273.
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We performed an in-depth study of high molecular weight poly[3,6-(dithiophene-2-yl)-2,5-di(2-octyldodecyl)-pyrrolo[3,4-c]pyrrole-1,4-dione-alt-thieno[3,2-b]thiophene] P(DPP2OD-TT) synthesized through the Stille coupling polycondensation in order to understand the correlation between molecular weight, processing conditions and charge transport. We observed a rapid increase in its aggregation in solution with increasing molecular weight which strongly limits the solubility and processability for weight average molecular weights beyond 200 kg mol⁻¹. This results in severe limitation in the charge transport properties of the polymer. We further observe the presence of bulk electronic defects in all different polymer batches that severely limit the current flow and manifest themselves in organic field effect transistors as apparent charge density dependence of the mobility. These defects are passivated by exposure to an ambient atmosphere, as confirmed by an increase in current and mobility that is no more charge density dependent. This is further confirmed by the result of chemical doping using 2,2-(perfluoronaphthalene-2,6-diylidene)dimalononitrile, F₆TCNNQ, which leads to the filling of the trap states and a higher charge density independent mobility of up to 1 cm2 V⁻¹ s⁻¹.
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Kaden, Thomas. "Temperatur- und injektionsabhängige Photospannungsmessungen zur Defektcharakterisierung in kristallinem Silizium." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2014. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-150612.
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Mit wellenlängenabhängigen Messungen der Oberflächenphotospannung (Surface Photovoltage, SPV) lässt sich die Diffusionslänge von Ladungsträgern im Volumen von Siliziumproben messen. Das Ziel der Arbeit war es, mit Hilfe temperatur- und injektionsabhängiger Messungen der Diffusionslänge die Natur rekombinationsaktiver Defekte in kristallinem Silizium zu untersuchen. Im Rahmen der Arbeit wurde eine zu diesem Zwecke geeignete Messanlage sowie die nötigen Mess- und Auswerteprozeduren entwickelt. Die Möglichkeiten und Grenzen der aufgebauten Anlage wurden durch Messungen an gezielt mit Eisen, Kupfer oder Chrom verunreinigten mono- und multikristallinen Siliziumproben bewertet. Es zeigt sich, dass die SPV-Methode in einem jeweils begrenzten Temperatur- und Injektionsbereich bei Vorhandensein dominanter Defekte zur Defekt-Spektroskopie einsetzbar ist. Eine Anwendung fand das Verfahren an industriell relevantem, aufbereitetem metallurgischen Silizium (umg-Si).
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Wilhelm, Maximilian Felix, Uwe Füssel, Thomas Richter, Matthias Riemer, and Martin Foerster. "Analysis of the shear-out failure mode for CFRP connections joined by forming." Sage, 2015. https://tud.qucosa.de/id/qucosa%3A35782.
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In this paper, we look at the shear-out failure of carbon fiber reinforced plastics connections in the automotive industry. Contrary to the aircraft industry, the boundary conditions of automotive applications favor this failure mode strongly. Moreover, the use of other joining technologies than that used in the aircraft industry, such as joining by forming, leads to new challenges. The different influences, typical for joining by forming, on ultimate shear-out strength were first investigated separately and then transferred and validated on connections related to praxis by an analytical model. Special attention was given to effects that resulted from oversized pre-holes, acting clamping forces, and the reduced quality of the laminates in the immediate vicinity of the joint due to the joining process.
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Müller, Rainer, Andreas Höhlein, Annette Wolf, Jutta Markwardt, Matthias C. Schulz, Ursula Range, and Bernd Reitemeier. "Evaluation of Selected Speech Parameters after Prosthesis Supply in Patients with Maxillary or Mandibular Defects." Karger, 2013. https://tud.qucosa.de/id/qucosa%3A71635.
Full textAbstract:
Background: Ablative surgery of oropharyngeal tumors frequently leads to defects in the speech organs, resulting in impairment of speech up to the point of unintelligibility. The aim of the present study was the assessment of selected parameters of speech with and without resection prostheses. Patients and Methods: The speech sounds of 22 patients suffering from maxillary and mandibular defects were recorded using a digital audio tape (DAT) recorder with and without resection prostheses. Evaluation of the resonance and the production of the sounds /s/, /sch/, and /ch/ was performed by 2 experienced speech therapists. Additionally, the patients completed a non-standardized questionnaire containing a linguistic self-assessment. Results: After prosthesis supply, the number of patients with rhinophonia aperta decreased from 7 to 2 while the number of patients with intelligible speech increased from 2 to 20. Correct production of the sounds /s/, /sch/, and /ch/ increased from 2 to 13 patients. A significant improvement of the evaluated parameters could be observed only in patients with maxillary defects. The linguistic self-assessment showed a higher satisfaction in patients with maxillary defects. Conclusion: In patients with maxillary defects due to ablative tumor surgery, an increase in speech performance and intelligibility is possible by supplying resection prostheses.
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Zobelli, Alberto. "Electron beam generation and structure of defects in carbon and boron nitride nanotubes." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2007. http://nbn-resolving.de/urn:nbn:de:swb:14-1197988167199-13274.
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The nature and role of defects is of primary importance to understand the physical properties of C and BN single walled nanotubes. Transmission electron microscopy (TEM) is a well known powerful tool to study the structure of defects in materials. However, in the case of SWNTs, the electron irradiation of the TEM may knock out atoms. This effect may alter the native structure of the tube, and has also been proposed as a potential tool for nanoengineering of nanotubular structures. Here we develop a theoretical description of the irradiation mechanism. First, the anisotropy of the emission energy threshold is obtained via density functional based calculations. Then, we numerically derive the total Mott cross section for different emission sites of carbon and boron nitride nanotubes with different chiralities. Using a dedicated STEM microscope with experimental conditions optimised on the basis of derived cross-sections, we are able to control the generation of defects in nanotubular systems. Either point or line defects can be obtained with a spatial resolution of a few nanometers. The structure, energetics and electronics of point and line defects in BN systems have been investigated. Stability of mono- and di- vacancy defects in hexagonal boron nitride layers is investigated, and their activation energies and reaction paths for diffusion have been derived using the nudged elastic band method (NEB) combined with density functional based techniques. We demonstrate that the appearance of extended linear defects under electron irradiation is more favorable than a random distribution of point defects and this is due to the existence of preferential sites for atom emission in the presence of pre-existing defects, rather than thermal vacancy nucleation and migration.
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Benda, Jan. "Klasifikace vad." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442537.
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The thesis deals with a concept and creation of classifiers of defects found on continuous production lines. The first part presents an overview of methods used for image classification and a analysis of defects. The main part of the thesis consist of a description of created classifier interface and graphical user interface for classifier. The last part sums up reliability of each implemented classifer.
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Buchwald, Rajko. "Optoelektrische Magnetfelduntersuchungen an Solarsilizium." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2010. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-39445.
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Es werden die Ergebnisse der Untersuchungen von Stromverteilungen an Solarzellen und Solarmaterial durch magnetische, elektrische und optische Messmethoden gezeigt. Die neue magnetfeldtopographische Messmethode CAIC wird hierbei erläutert und deren Stromverteilungen mit Ergebnissen der IR-Durchlichtmikroskopie, der LBIC- und der LIT-Methode verglichen und ausgewertet. Auf Basis der durchgeführten Untersuchungen und der Annahme des Vorhandenseins einer Korngrenzendekoration werden die Stromverlaufsmodelle einer elektrisch aktiven Korngrenze für eine Probe mit pn-Übergang sowie für eine Probe ohne pn-Übergang gezeigt. Anhand von CAIC-Messungen können die Position und die Orientierung leitfähiger und oberflächennaher SiC- und Si3N4-Ausscheidungen in mc-Silizium ohne pn-Übergang ermittelt werden. Hierfür wird ein Stromverlaufsmodell gezeigt. Weiterhin werden Zellmikrobrüche, Fehler in der Kontaktstruktur und Layoutunterschiede der Kontaktstruktur durch CAIC-Messungen an Solarzellen eindeutig nachgewiesen.
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Wong, Boon Kwei. "Automatic surface defect recognition and classification." Thesis, University of Sunderland, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283762.
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Johnson, Jay Tillay. "Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry." Thesis, Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37234.
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Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.
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Ngendangenzwa, Blaise. "Defect detection and classification on painted specular surfaces." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-146063.
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The Volvo Trucks cab plant in Umea is one of the northern Sweden’s largestengineering industries. The plant manufactures only cabs for trucks and is one of the most modern production plants in the world. Despite a highly automated and computerized system among many processes, the paint quality inspection process is still mainly performed manually. A real-time automated and intelligent quality inspection for painted cabs is highly desired to decrease the costs and at the same time to increase both the production efficiency and the product quality. This project is one step forward to the automation of paint quality control. Two different issues were treated during this project, namely defect detection and defect classification. These problems were solved by feeding four statistical approaches such as support vector machine, random forests, k-nearest neighbors and neural networks with extracted histogram of oriented gradients features from the captured images. The results revealed that support vector machine and random forests outperformed their contenders in terms of accuracy to both detect and to classify the defects.Volvokoncernens hyttfabrik i Umeå är en av Norrlands största verkstadsindustrier.Hyttfabriken tillverkar bara hytter för lastbilar och tillhör en av världens modernaste produktionsanläggningar. Trots ett hög automatiserat och datoriserat system bland många processer så är kvalitetsinspektionen av målade hytter fortfarande utförd manuellt. En smart och automatiserad kvalitetskontroll kan leda till lägre kostnader, högre kvalitet samt högre produktions effektivitet. Den här studien är ett steg framåt mot en automatiserad kvalitetskontroll. Två slagsproblem undersöktes närmare i den här studien nämligen defekt inspektion och defekt klassificering. Dessa problem åtgärdades genom att förse fyra statistiskametoder, support vector machine, random forests, k-nearest neighbors och neuralnetworks, med extraherade HOG egenskaper från tagna bilder. Resultaten visade att support vector machine och random forests presterade bättre än dess konkurrenter i förhållande till förmågan att både inspektera och klassificera defekter.
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Janošík, Zdeněk. "Klasifikace detekovaných vad." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221300.
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In this master thesis is described how to design and implement classifier of defects detected during the final stage of production nonwovens. The beginning of the thesis is devoted to the analysis of options for image processing and classification. Followed by the part, where is described process of image segmentation and extraction of feature vector. Description of classifier implementation and table of achieved results of classification on real images of detected defects.
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Rønsted, Nina. "Towards a natural classification of Plantago : chemical and molecular systematics /." Cph. : Royal Danish School of Pharmacy, Department of Medicinal Chemistry, 2002. http://www.dfh.dk/phd/defences/Nina%20Ronsted.html.
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Yang, Xuezhi, and 楊學志. "Discriminative fabric defect detection and classification using adaptive wavelet." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29913408.
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Turcu, Mircea Cassian. "Defect energies, band alignments, and charge carrier recombination in polycrystalline Cu(In,Ga)(Se,S)2 alloys." Doctoral thesis, Technische Universität Dresden, 2003. https://tud.qucosa.de/id/qucosa%3A24342.
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This work investigates the defect energies, band alignments, and charge carrier recombination in polycrystalline Cu(In1-xGax)(Se1-ySy)2 chalcopyrite thin films and the interrelationship with the alloy composition. Photoluminescence spectroscopy of investigated Cu-poor Cu(In,Ga)(Se,S)2 layers generally shows broad emission lines with the corresponding maxima shifting towards higher energies under decreasing temperature or under increasing excitation power. Admittance spectroscopy of Cu-poor ZnO/CdS/Cu(In,Ga)(Se,S)2 chalcopyrite devices shows that the activation energies of the dominant defect distributions involving donors at the CdS/absorber interface and deep acceptors in the chalcopyrite bulk, increase upon alloying CuInSe2 with S. The band alignments within the Cu(In1-xGax)(Se1-ySy)2 system are determined using the energy position of the bulk acceptor state as a reference. The band gap enlargement under Ga alloying is accommodated almost exclusively in the rise of the conduction band edge, whereas the increase of band gap upon alloying with S is shared between comparable valence and conduction band offsets. The extrapolated band discontinuities [delta]EV(CuInSe2/CuInS2) = -0.23 eV, [delta]EC(CuInSe2/CuInS2) = 0.21 eV, [delta]EV(CuInSe2/CuGaSe2) = 0.036 eV, and [delta]EC(CuInSe2/CuGaSe2) = 0.7 eV are in good agreement with theoretical predictions. Current-voltage analysis of Cu-poor ZnO/CdS/Cu(In,Ga)(Se,S)2 devices reveals recombination barriers which follow the band gap energy of the absorber irrespective of alloy composition, as expected for dominant recombination in the chalcopyrite bulk. In turn, the recombination at the active junction interface prevails in Cu-rich devices which display substantially smaller barriers when compared to the band gap energy of the absorber. The result indicates that the Cu-stoichiometry is the driving compositional parameter for the charge carrier recombination in the chalcopyrite heterojunctions under investigations.
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Huang, Hsu-Yi, and 黃旭儀. "Automatic Classification of Color Filter Defects by Data Mining." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/03004494570636724713.
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碩士中原大學
資訊工程研究所
101
Nowadays, life cycles of electronic products, such as televisions, tablet computers, and smart phones, have become shorter and shorter, due to the renewed and fast developed information technologies. Therefore, it is important to control the productivity and quality of these products. The industry of TFT LCD plays an important role in the supply chain of these IT products. Therefore, it is a challenge to keep high productivity and high quality of TFT LCD for the flat display industry. In the quality control process, the last checking of color filter (CF) of TFT LCD are still mainly performed manually. However, there are uncertain factors in the manual checking process, so it is needed to be replaced by an automatic checking process. In this research, we developed a method of automatic defect classification of CF by image processing and data mining techniques. In the stage of image processing, we processed the transparent-light images and reflective-light images, detecting the defect areas, extracting the image features, and then formed a feature vector of the defect. In the stage of automatic classification, based on data mining techniques, we discovered the feature vectors of the 16 defect classes from training images, then we calculated the similarity of the feature vectors of testing images to get the results of classification. Finally, we evaluated the method from the experiment results by calculating the precision and recall. The experiment results indicate that our method has average high precision and high recall.
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Wu, Ching-Ming, and 吳璟旻. "A neural-network approach for wafer defects pattern classification." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/44586708557044604231.
Full textAbstract:
碩士國立清華大學
工業工程與工程管理學系
93
Nowadays, the procedures of semiconductor manufacturing have become more and more sophisticated. Though highly automated facilities are used to process the complex manufacturing steps in the near particle free environment, the yield loss is still unavoidable. Manufacturers must develop a method that enables them to improve yield. Recognizing the existence of a systematic defect provides a clue to identifying the equipment or process abnormality responsible for the defect. However, the process of defect classification is time-consuming, monotonous and costly and causes fatigue and eye-strain, which in turn cause errors in classification. For these reasons, this research intends to propose a two-phases defect pattern recognition system. The first phase is to use the masks and thresholds to eliminate the wafers with random defects and identify the existence of the systematic defects. At the same time, the features extracted from systematic defects are the inputs for constructing the neural network in the second phase. After training three supervised learning neural networks, this research compares these two neural networks by MSE of training and testing samples, and selects the better neural network. The developed methodology is verified with industrial data from a famous semiconductor company. The existing neural-network approaches for recognizing the defect patterns on the wafer are limited by the size and the orientation of defect patterns. The experimental results demonstrate that the proposed methodology can not only solve this problem by extracting features, but also effectively identify the defect patterns on the wafer.
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WU, CHEN-HAO, and 巫承昊. "The Recognition and Classification of Defects of Lace Fabrics." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/75011375293469599467.
Full textAbstract:
碩士國立臺北科技大學
有機高分子研究所
90
The inspection of fabric defects is the most important process for ensuring the quality of final products. Traditionally, manual inspections are not only time wasted but also labour intensive. To solve these problems, lots of researches apply updated computer visual technologies to the “Automatic Inspection of Fabric Defects System”. However, most of those researches focus on the inspection of woven fabrics or nonwoven fabrics. The researches and applications for the inspections of lace fabrics, jacquard fabrics, print fabrics or others are rare. Therefore, this study concentrates on improving the classification and recognizing accuracy of lace fabric defects by employing an image processing technology with an artificial neural network theory to solve the defect-recognition difficulty of lace fabrics caused by a slightly shift or a rotation of fabrics during a dynamic inspection process. The approach of this study is to compare the defect-recognition results between theoretical lace fabric images and real lace fabric images. Firstly, Area-Scan CCD camera is applied to acquire theoretical and real images of lace fabrics defects. Secondly, the theoretical image of lace fabric is then shifted and rotated by the computer to simulate the variances of lace fabric images generated by a dynamic acquisition process. Then, the binary technology is employed to distinguish the differences between the images of lace fabrics and background. In order to tell the feature values of lace fabrics defects, this study utilizes the image processing analysis technologies of “moment invariants” and “total black pixel”. All the feature values of defect images are consisted into feature vectors as the input vectors of a neural network to classify the defects. Finally, the real images of lace fabrics can be acquired to verify the system. From the results, it tells that the accuracy of theoretical defect images classification of lace fabrics is 100% but the real defect images classification of lace fabrics is 93.33%. Therefore, it can be suggested that this application of acquiring features values of defect images is suitable for the “Automatic Inspection of Lace Fabric Defects System” to improve product quality and reduce the cost of lace fabric processing and inspection.
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陳毅均. "Recognition and Classification of Fabric Defects by Fuzzy-Neural Systems." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/07126465283023212303.
Full textAbstract:
碩士國立臺灣科技大學
纖維及高分子工程研究所
87
Due to rapid development and increasingly rigorous quality requirement in the textile industry, the automation is definitely prerequisite. However, human operators usually inspect fabric defects, which is subjective, tedious, and time—consuming. The inspection results are greatly influenced by the mental and physical condition of an inspector. In this study, fabric defects are detected and classified using fuzzy neural network based on their features. The conjunction of fuzzy logic and neural network algorithms can enhance the fabric defect recognition system to be more robust and adaptive. A total of forty-five samples, each defect with five samples, are obtained for training and testing, respectively. For each sample, the feature vector including the ratio of projection lengths of the defect area in the x and y axes, gray level mean, gray level standard deviation, and large number emphasis (LNE) based on Neighboring gray level dependence statistics (NGLDM) method. The feature vectors are normalized by fuzzification, and then back-propagation learning algorithm is used for training computations. The fabric defects include normal, broken end, broken pick, double ends, double picks, hole in fabric, light filling bar, cobweb, and oil stain. The test results show that the fabric defects can be identified with high accuracy. The mean squared errors are within 0.0001. We also find that fuzzy neural network is superior to conventional neural network.
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Chen, Chia Fa, and 陳加發. "Using Neural Networks for Surface Defects Classification - A Pilot Study." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/50909392206083002781.
Full textAbstract:
碩士中原大學
工業工程研究所
82
The machine vision system apply in the production line is the solution for the dificulty of the automatic production process. The merchandize of the machine vision system is used statistics paptern recognition as classifier method. For statistics and structure ayalysis method need a complex analysis for each pattern of data. So, in this study, we will use neural network to classify the defects of the oil-lid. In this study, image substraction, image matrix transfer and neural network are employed to model the machine vision system. Adaptive Resonance Theory(ART) and Back-Pagation(BP) network are used which learning and training through five defect patterns and a good one images, then used such neural networks to classify the oil-lid which is a good production or one of the five kind defect of production. The result of the experiment is: First, without any refined method: CGNN:65%, ART1: 80%, ART2:87.3%, BP:90.67%. Second, used one refined method: CGNN:75%, BP:95%. Through refined method, the BP network has been proved that machine vision system has a good result in the pattern recognition. The major dificulties in refining process of the classfication system. First, the choice of refining system methods depend on the result of the experiment. Second, it would not know the reason after the result of refined is bad. Third, it would not know to stop refining under the unknow refined value. In this study, it is fucus to solve the troubles of using refined methods, we can make a bettre result for neural network in the pattern recognition.
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Chen, Ching, and 陳靚. "Applying Image Recognition on Solar Cell Defects Inspection and Classification." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/q628wv.
Full textAbstract:
碩士正修科技大學
資訊管理研究所
107
The natural energy is so important so that the solar power industry and light-electricity conversion devices are developed fast during these days. For all products in this field, solar cell is the most important device which generates electricity by drawing sunlight and sometimes store the electricity to a normal battery. The faults on the surface of solar cell leads to the light-electric conversion efficiency decreased of the solar cell and to eventually shorten the battery life. Traditional manual detection in manufacturing solar cell consumes labor and manpower since it is applied by visible checking. There are some disadvantages of manual detection so that we developed a two-process model for automatic identification and classification to for checking the faults on the surface of solar cells in order to reduce the number of images necessary for manual detection. The first process of this model is responsible for identifying the obvious faulty image of the solar cell and the second one is responsible for classifying normal cell. Both processes will be trained by the classified training set of images. While the suitably classifying model of these two processes is established, the testing data set will be applied for verification of their classification accuracy. In the research, OpenCV's image processing function is applied to implement preprocess before classification on the image sets. SimpleBlobDetector, one of OpenCV's spot detection methods, is applied in this research to identify and classify the features in image sets. In order to improve the precision of the detection on faulty images, we used Genetic Algorithms to optimize the parameters for the image feature detection of SimpleBlobDetector. The first process can classify obvious faulty image entirely. The identification of the normal image through the second process achieve 96. We can conclude that this two-process model can classify most image correctly and leave only small amount of images necessary for manual detection，thus it reduced the consumption of manpower.
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Wu, Tsung-Lin, and 吳昌林. "A Computer Vision System for Detection and Classification of Defects." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/66308090046378263068.
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Wang, Yu-min, and 王裕民. "PCB Golden Finger Defects Classification Research Under Small Sample Size Situation." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/10196244075714032045.
Full textAbstract:
碩士元智大學
工業工程研究所
87
PCB Golden Finger Defects Classification Research Under Small-Sample Size Situation Student:Yu-Min Wang Advisor:Dr.B.C.Jiang Department of Industrial Engineering YUAN-ZE University ABSTRACT Generally, using machine vision to inspect surface defects of manufacturing parts in industry, the classification of the defects can provide important information on how to improve quality in manufacturing processes. However, current available classification schemes are not able to handle small sample situation which is sometimes occurs in industry. It is proposed to use bootstrap technique to deal with the small sample classification problem. Then a tree analysis method is used to classify different PCB surface defects. In this research, there are 19 training samples and 25 test samples. Using the real 19 samples generates 2000 "bootstraped" samples and using the 25 test samples segmented into 470 test samples.The results showed that using bootstrap and tree-classifier to solve small sample size classification problem, it can achieve 97.87% accuracy rate. Keywords: Bootstrap, Machine Vision, Golden Finger, Defect inspect, classifier
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Wang, Chien-Chih, and 王建智. "A Study of Defects Classification Based on Automatic Visual Inspection System." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/28916858456650309403.
Full textAbstract:
博士元智大學
工業工程研究所
89
This research is based an automatic visual system to develop a new classification model and to apply it to the printed circuit board (PCB) defects classification. The feature selection and classifier design are key factors for the classification. To address these two aspects, this research proposed a new algorithm and procedure, and using the solder joints for a PCB from a monitor manufacturer in Taiwan and a set of OCR data to verify these methods. Two new algorithms for feature selection were studied： (I) Single Feature Selection：The feature selection index (FSI) was designed to measure the overlapped region for two different groups of data. Under the normal distribution assumption, it was proved that the relationship between two different overlapped region and the FSI values, and it could used to select the optimal single feature variable. Furthermore, the-larger-the-better SN ratio was used to calculate the multiple groups’ FSI to get the optimal feature variable. (II) Multiple Features Selection：To integrate DOE and MANOVA techniques to select the optimal multiple feature variables. First, the single feature variable selection algorithm was adopted to eliminate poor discrimination feature variables. Then the Plackett-Burman (PB) resolution III design was constructed for the selection of remaining feature variables. Using MANOVA technique, calculate Pillai statistic as the response for the PB design of experiment. Finally, execute statistical analysis to obtain the optimal multiple feature variables for multiple groups. Two new algorithms of classification were studied： (I) Parametric Classification Algorithm：An adaptive Bayesian classification procedure was adopted to solve high classification error rate problem when utilizing the normal Bayesian classification method under non-normal distributed data. (II) Nonparametric Classification Algorithm：The tree classification procedure was modified and was proposed as a new classification algorithm which consider the overlapped region for various groups. The simulation results showed that the nonparametric classifier of the proposed method was better than the Bayesian classifier. According to the research, we integrated two-classification systems：(I) Combined the optimal single feature selection algorithm and the order tree classifier; (II) Combined the optimal multiple features selection algorithm and the adaptive Bayesian classifier. And, the developed classification system (I) was applied to the solder joints defects of a PCB. The confidence interval of classification correctness is (98.5%, 100%), which was better than those of the optimal Bayesian classifier and the discriminate function classifier. On the application of the system II, the average classification correctness rate reached 94%, which was better than those of the Bayesian classifier, the discriminate function classifier, and the Nearest Neighbor classifier. In addition, applied both system to a set of OCR data from literature, the classification correctness rate is higher than that reported in the literature.
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Hsu, Yu-Nan, and 許友南. "Machine Vision Based Inspection and Classification for PCB Solder Joints Defects." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/66648719378723402615.
Full textAbstract:
碩士元智大學
工業工程研究所
88
To inspect the quality of solder joints defects needs some special illumination arrangement, such as LED、structural light, or some special instrument, such as X-ray、ultrasonic images. Because these equipments are expensive and can’t inspect solder joints defects effectively, the application for these illumination techniques is limited. The focus of this study is to provide a solder joints inspection framework based on machine vision. After capturing images which need inspection by a CCD camera, it utilizes the image of copper region on the PCB bare board to apply a minimum-filter, and it can segment all solder joints regions. The selected features of solder joints are calculated for the region. Then the features are separated into two parts: part one is based on the binary images、part two is based on gray-value images. To classify solder joints is using classification tree. In this study, it defines three types of solder joints defects: Open、No solder、Short and regular solder joints. The experiment results showed that using classification tree determined by the distance between groups and box plots, the classification correctness reached 97.2%. And utilizing the method proposed by Clark and Pregibon(1992), the classified correctness of regular solder joints is 100%, but it would classify Short to regular solder joints.
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Chuang, Fuji, and 莊富傑. "The Detection and Classification of Lead Frame Defects Using Neural Networks." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/44883327211863894990.
Full textAbstract:
碩士中華大學
機械與航太工程研究所
87
As the pitch getting finer and the lead number getting higher, the inspection of IC lead frame using bare eyes becomes more difficult. To lessen the workload of human inspectors, an effective method for the detection and classification of defects is presented. First, the proposed method uses image-processing techniques such as image enhancement, image segmentation, edge detection, morphological operation, and labeling to locate defects. Next, feature extraction techniques are applied to measure such features as perimeter, moments, area, eccentricity, compactness, roughness, and standard deviation of gray level. Finally, by inputting the extracted features of each defect to a pre-trained feedforward backpropagation network, the defect can be classified into pinhole, scratch, or contamination. To automate the inspection process, image processing, feature extraction, artificial neural network, as well as stage and light source control techniques have been integrated into an effective defect detection and classification system. The experimental results show that on an average, the system can finish inspecting an image in 0.4 second and the recognition rate is 99.22%. In summary, the developed system not only can replace the convention inspection method, but also increase the accuracy and efficiency of lead frame inspection.
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Fernandes, Pedro Miguel Pinto da Cunha. "Detection of production defects using Machine Learning based Image Classification Algorithms." Dissertação, 2020. https://hdl.handle.net/10216/129867.
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Fernandes, Pedro Miguel Pinto da Cunha. "Detection of production defects using Machine Learning based Image Classification Algorithms." Master's thesis, 2020. https://hdl.handle.net/10216/129867.
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Chang, Chih-Wei, and 張智崴. "Automatic optical inspection for classification on the defects of carbon nanofibers production." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/zd63qt.
Full textAbstract:
碩士中原大學
電子工程研究所
106
In recent years, mechanical vision image detection has become more and more developed. With image analysis to make the product in the production line more clearly, quality management can be more smoothly and save more time. Therefore, in order to realize fully automated equipment in the factory, the development of product inspection machines has become an important indicator of quality control. In this paper, we use OpenCV as the development environment, and use carbon nanofiber products as the research object. In the production process, we aim to distinguish some of the defective classes, so that the production quality is regulated. Because the production of the carbon nanofiber is not perfect, if bad products are put into the end equipment, the production process will inevitably encounter unexpected errors and degrade products. We use image processing techniques (such as Gaussian Blur filter to remove noise from the captured image) to detect whether the carbon fiber in the image is a normal product or not; we aim to solve that detection problem by using computer algorithms instead of a human check. We define four undesirable conditions for quality management analysis: bifurcation defects, burr defects, large-width defects and small-width defects. These four categories are likely to cause unpredictable errors in the production process that hinder normal production. If these defects can be eliminated before the subsequent production stage, the production quality will be increased. With this detection algorithm for various kinds of defects, the current state of production quality can be under control.