Thematische Bibliographien / Defects classification

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Auswahl der wissenschaftlichen Literatur zum Thema „Defects classification“

Autor: Grafiati
Veröffentlicht am 28. Juni 2021
Zuletzt aktualisiert am 13. Februar 2022

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Zeitschriftenartikel zum Thema "Defects classification"

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Huh, Sang Moo, and Woo-Je Kim. "The Derivation of Defect Priorities and Core Defects through Impact Relationship Analysis between Embedded Software Defects." Applied Sciences 10, no. 19 (2020): 6946. http://dx.doi.org/10.3390/app10196946.

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As embedded software is closely related to hardware equipment, any defect in embedded software can lead to major accidents. Thus, all defects must be collected, classified, and tested based on their severity. In the pure software field, a method of deriving core defects already exists, enabling the collection and classification of all possible defects. However, in the embedded software field, studies that have collected and categorized relevant defects into an integrated perspective are scarce, and none of them have identified core defects. Therefore, the present study collected embedded software defects worldwide and identified 12 types of embedded software defect classifications through iterative consensus processes with embedded software experts. The impact relation map of the defects was drawn using the decision-making trial and evaluation laboratory (DEMATEL) method, which analyzes the influence relationship between elements. As a result of analyzing the impact relation map, the following core embedded software defects were derived: hardware interrupt, external interface, timing error, device error, and task management. All defects can be tested using this defect classification. Moreover, knowing the correct test order of all defects can eliminate critical defects and improve the reliability of embedded systems.
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Nurlaelah, Azis, and Usman Sudjadi. "The Classification of Residential Defects (Case Study: Citra Garden Residence in Indonesia)." Applied Mechanics and Materials 507 (January 2014): 97–106. http://dx.doi.org/10.4028/www.scientific.net/amm.507.97.

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The classification of residential defects (case study: Citra Garden Residence in Indonesia) was studied. This study aims to more satisfied customers. The study begins with the literature review to formulate the classification of house defects. Then classify the defect of house into two, namely the classification of house defects based on period of post hand over, and the classification of house defects based on category of the defects. Further studies followed by dividing the classification of house defects based on period of post-hand over into three parts, namely before hand over period (inviting time), hand over period, and post-hand over period. The next step is to check the complaint report from the customer service in Citra Garden Residence in Indonesia to quantificate the defects of the house. The classification of house defects based on category of the defects divided into two, namely structural defect (minor, moderate, serious), and nonstructural defect (minor, moderate, serious). The next step is also to check the complaint report from the customer service in Citra Garden Residence in Indonesia to quantificate the defects of the house.The results show that complaint hand over in the level minor defect is the highest complaint. Complaint in the serious defect is the lowest complaint.
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Pond, R. C. "TEM studies of line defects in interfaces." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 586–87. http://dx.doi.org/10.1017/s0424820100104996.

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Line defects are ubiquitious features in interfaces, and have important structural and mechanistic role. Recently, a crystallographic theory of such defects has been presented which appears to offer a comprehensive framework for their classification. The object of the present paper is firstly to outline the characterisation and classification of defects according to this treatment. Secondly, we illustrate examples of defects in the distinctive classes observed using tern, and discuss the various imaging techniques which have been employed.In the absence of a rigorous treatment of line defects in single crystals and interfaces, which would require the development of a discrete field theory, approximate methods of defect characterisation are used. The most popular method involves mapping a contour, initially constructed around a defect of interest, into a reference space. For defeats in single crystals this Burgers circuit method, introduced by Frank, is very helpful, but suffers from certain procedural inconveniences in the case of interfacial defects.
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Cho, Du Hyung, and Seok Lyong Lee. "Defect Identification and Classification for Plasma Display Panels." Advanced Materials Research 694-697 (May 2013): 1197–201. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.1197.

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The defect inspection is a crucial process for the plasma display panel (PDP) production that significantly influences the quality of final products. In this paper, we propose a defect identification and classification method that extracts and classifies defects using various image analysis techniques. First, we identify defects through binarization of images using Gaussian filter. Then, those defects are classified into seven different types by analyzing geometric characteristics of defects and utilizing a support vector machine (SVM) classifier. The experimental results using separate sets of training and test PDP images obtained from production lines are quite promising. Our method identifies defects effectively enough to be used in the real environment. It also achieves a high correctness in classifying various types of defects.
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Kumaresh, Sakthi, and R. Baskaran. "Software Defect Prevention through Orthogonal Defect Classification (ODC)." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (2013): 2393–400. http://dx.doi.org/10.24297/ijct.v11i3.1166.

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“Quality is never an accident; it is always the result of intelligent effort” [10]. In the process of making quality software product, it is necessary to have effective defect prevention process, which will minimize the risk of making defects /errors in software deliverables. An ideal approach would involve effective software development process with an integrated defect prevention process. This paper presents a Defect Prevention Model in which Defect Prevention Process(DPP) is integrated into software development life cycle to reduce the defects at early stages itself, thereby reducing the defect arrival rate as the project progresses to the subsequent stages. Orthogonal Defect Classification (ODC) scheme involving defect trigger, defect type etc. are discussed in this work to illustrate how ODC can be used in the defect prevention process. ODC can be used to measure development progress with respect to product quality and identify process problems, which will help to come out with “Best Practices” to be followed to eradicate the defects in the subsequent projects.
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Stoll, Claude, Denis Duboule, Lewis B. Holmes, and J�rgen Spranger. "Classification of limb defects." American Journal of Medical Genetics 77, no. 5 (1998): 439–41. http://dx.doi.org/10.1002/(sici)1096-8628(19980605)77:5<439::aid-ajmg16>3.0.co;2-j.

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7

Danilov, E. O. "Legal Classification of Defects in Medical Care." Actual Problems of Russian Law 16, no. 5 (2021): 123–38. http://dx.doi.org/10.17803/1994-1471.2021.126.5.123-138.

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The paper studies the legal nature of defects in medical care and defines criteria for their legal classification. A retrospective analysis of the development of the institution of legal responsibility for improper medical treatment is carried out. The concept of a defect in medical care and related categories, their natural ontological characteristics and classifying legal features are investigated, doctrinal approaches to the legal assessment of defects in medical care are considered. It is noted that, despite the noticeable evolution that the question of the responsibility of doctors has undergone in the history of law, there is still no single approach to understanding the legal nature of defects in medical care in jurisprudence. In modern Russian legislation, as in the criminal laws of most foreign countries, there are no special standards for such defects classification. At the same time, in the interests of optimal legal regulation of relations in the field of medicine, today it is the legal definition and systematization of basic concepts and criteria for the legal classification of various medical incidents that matters and not the introduction into the law of special articles establishing criminal liability for improper provision of medical care. Thus, by combining all adverse events in medical practice under the general term "medical incidents", one can use the concept of "medical care defect" to distinguish incidents caused by inappropriate provision of medical services. The author proposes his own classification of defects in medical care dividing them into medical torts (offenses) and medical incidents (accidents and medical errors) based on the nature of the attitude of the subject of medical activity to their professional duties. A conceptual approach to the legal classification of medical care defects has been formulated.
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Cho, Du Hyung, and Seok Lyong Lee. "Defect Classification Using Machine Learning Techniques for Flat Display Panels." Applied Mechanics and Materials 365-366 (August 2013): 720–24. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.720.

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Defect classification for a flat display panel (FDP) is the crucial process that identifies and classifies defects automatically during the final step of its manufacturing process. It plays an important role since it prevents possible malfunction by inspecting defects timely and reduces time for identifying inferior products. In this paper, we propose the defect classification methods for FDP using various machine learning techniques and provide the comparison among them for practical use in production environment. First, we identify defects through Gaussian filter and threshold technique. Then, those defects are classified into different types based on geometric characteristics of them using four machine learning techniques that are widely used. The experimental results using training and test sets of FDP images show considerable effectiveness in classifying defect types. We also believe that the comparison result might be quite useful when engineers determine methods for defect classification during FDP manufacturing.
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Agnelo, João, Nuno Laranjeiro, and Jorge Bernardino. "Using Orthogonal Defect Classification to characterize NoSQL database defects." Journal of Systems and Software 159 (January 2020): 110451. http://dx.doi.org/10.1016/j.jss.2019.110451.

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Pham, D. T., and S. Sagiroglu. "Neural network classification of defects in veneer boards." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 214, no. 3 (2000): 255–58. http://dx.doi.org/10.1243/0954405001517649.

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Learning vector quantization (LVQ) networks are known good neural classifiers which provide fast and accurate results for many applications. The aim of this work was to test if this network paradigm could be employed for the classification of wood sheet defects. Experiments conducted with LVQ networks have shown that they provide a high degree of discrimination between the different types of defects and potentially can perform defect classification in real time.
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Dissertationen zum Thema "Defects classification"

1

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.<br>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.<br>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.<br>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, et al. "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.<br>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.<br>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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Bücher zum Thema "Defects classification"

1

Papaelias, Mayorkinos, and Fausto Pedro Garca Mrquez. Fault Detection: Classification, Techniques and Role in Industrial Systems. Nova Science Publishers, Incorporated, 2014.

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Lamari, Foudil, and Jean-Marie Saudubray. Disorders of Complex Lipids Synthesis and Remodeling. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199972135.003.0066.

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Defective lipid catabolic pathways are involved in numerous inherited metabolic diseases such as lysosomal storage diseases and peroxisome biogenesis disorders. We recently described a new classification of a rapidly growing group of inherited metabolic disorders involving biosynthesis and remodeling of complex lipids including phospholipids and sphingolipids. The remarkable progress achieved over the last decade in high throughput gene sequencing and in lipid analysis technologies have enabled the description of more than 40 diseases linked to defects in enzymes involved in these pathways. Some of these defects present in infancy or childhood but most of them are diagnosed in adolescence or adulthood. In this review we focus on those with adult presentation.
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Uffman, Joshua C. Neuronal Ceroid Lipofuscinoses (Batten Disease). Edited by Kirk Lalwani, Ira Todd Cohen, Ellen Y. Choi, and Vidya T. Raman. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190685157.003.0042.

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Neuronal ceroid lipofuscinoses (NCL) are a group of autosomal recessive genetic disorders which represent the most common form of childhood neurodegenerative disease. Classically the disease was described according to the age of diagnosis resulting in four common phenotypes: (i) infantile or Santavuori-Haltia, (ii) late infantile or Jansky-Bielschowsky, (iii) juvenile or Spielmeyer-Vogt, and (iv) adult or Kufs. With advances in genetic mutational analysis techniques and improved understanding of NCL disease as a whole, disease classification now focuses on which of the known genetic defects is responsible for the disease. Regardless of genetic defect or age of onset, patients typically present with language delay, seizures, blindness, and ataxia. The term “Batten disease” is used to refer to the group as a whole in addition to specifically referring to the juvenile form. Anesthetic implications focus on disease symptoms at presentation, with special attention to maintaining normorthermia and the possibility of bradycardia.
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Kuwabara, Satoshi. Neuromuscular junction disorders. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199658602.003.0014.

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Ten seminal papers on disorders of the neuromuscular junction are described, covering historical aspects, recent advances in immunological, biological, and genetic researches, and future perspectives. Early descriptions of myasthenia gravis (MG) date back to the seventeenth century, and MG acquired its name in the nineteenth century. The first symptomatic treatment with cholinesterase inhibitors was reported in 1934, leading to the development of modern immunological therapies. Following the discovery of anti-MuSK (muscle-specific tyrosine kinase) antibody in 2001, MG is currently classified into three categories: AChR-positive, MuSK-positive, and dual-seronegative. Lambert-Eaton myasthenic syndrome was recognized in 1956, followed by the discovery of antibodies to voltage-gated calcium channels in the pre-synaptic membrane, facilitating diagnosis and improving the understanding of the pathophysiological mechanisms. Since the late twentieth century, many types of congenital myasthenic syndromes with pre-synaptic, synaptic, and post-synaptic defects have been identified, and a classification based on molecular genetics is in evolution.
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Karapapa, Stavroula. Defences to Copyright Infringement. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198795636.001.0001.

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Defences to copyright infringement have gained increased significance over the past twenty years. The fourth industrial revolution emerged with the development of innovative copy-reliant services and business models, transforming the way in which copyright works can be used, from digital learning methods to mass digitization initiatives, media monitoring services, image transformation tools, and content mining technologies. The lawfulness of such innovative services and business methods, which arguably have the potential to enhance public welfare, is dubious and challenges copyright law. EU copyright contains specifically enumerated, narrowly drafted, and strictly interpreted defensive rules, often taking the form of the so-called exceptions and limitations to copyright. Because the fourth industrial revolution promises innovation and business growth—stated objectives of EU copyright—it invites an examination of defensive rules as a whole. The book adopts a holistic approach in its exploration of the limits of permissibility under EU copyright, including legislatively mentioned exceptions and limitations, doctrinal principles, and rules external to copyright, with a view to unveiling possible gaps and overlaps, offering a novel classification of defensive rules, and evaluating the adaptability of the law towards technological change. Discussing recent legislative developments, such as the provisions of the Digital Single Market Directive, Court of Justice of the European Union case law, and insights from national laws and cases, the book tells the story of copyright from the perspective of copyright defences, offering positivist and normative insights into law and doctrine and arguing towards a principle-based understanding of the scope of defences that could inform future law and policy making.
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Federico, Antonio, and Silvia Palmeri. Oligosaccharidoses. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199972135.003.0057.

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Oligosaccharidoses are a group of lysosomal diseases, also called glycoproteinoses, biochemically characterized by storage of protein-bound oligosaccharides within lysosomes and excretion with urine of corresponding sugars. Storage of oligosaccharides results from absence or defective function of a specific lysosomal enzyme. Classification includes α‎ and β‎ mannosidosis, fucosidosis, sialidosis types I and II, Schindler disease, and aspartylglycosaminuria. Galactosialidosis characterized by deficiency of β‎-galactosidase and α‎-neuraminidase with presence in patient urine of oligosaccharides has been included among oligosaccharidoses but may be better classified as a lysosomal enzyme protection defect disease in relation to its primary defect of cathepsine A-protective protein. The clinical spectrum of the diseases vary widely, as is common in lysosomal storage disorders. Patients frequently have neurological symptoms, but in rare cases presenting in adulthood symptoms may be very subtle. Psychiatric presentations have been described in adults. For adult cases, no treatments are available except for supportive care.
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Sprague, Stuart M., and James M. Pullman. Spectrum of bone pathologies in chronic kidney disease. Edited by David J. Goldsmith. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0122.

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Histologic bone abnormalities begin very early in the course of chronic kidney disease. The KDIGO guidelines recommend that bone disease in patients with chronic kidney disease should be diagnosed on the basis of bone biopsy examination, with bone histomorphometry. They have also proposed a new classification system (TMV), using three key features of bone histology—turnover, mineralization, and volume—to describe bone disease in these patients. However, bone biopsy is still rarely performed today, as it involves an invasive procedure and highly specialized laboratory techniques. High-turnover bone disease (osteitis fibrosa cystica) is mainly related to secondary hyperparathyroidism and is characterized by increased rates of both bone formation and resorption, with extensive osteoclast and osteoblast activity, and a progressive increase in peritrabecular marrow space fibrosis. On the other hand, low-turnover (adynamic) bone disease involves a decline in osteoblast and osteoclast activities, reduced new bone formation and mineralization, and endosteal fibrosis. The pathophysiological mechanisms of adynamic bone include vitamin D deficiency, hyperphosphataemia, metabolic acidosis, inflammation, low oestrogen and testosterone levels, bone resistance to parathyroid hormone, and high serum fibroblast growth factor 23. Mixed uraemic osteodystrophy describes a combination of osteitis fibrosa and mineralization defect. In the past few decades, an increase in the prevalence of mixed uraemic osteodystrophy and adynamic bone disease has been observed.
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Buchteile zum Thema "Defects classification"

1

Engh, Gerard A. "Classification of Bone Defects." In Surgical Techniques in Total Knee Arthroplasty. Springer New York, 2002. http://dx.doi.org/10.1007/0-387-21714-2_53.

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Krautkrämer, Josef, and Herbert Krautkrämer. "Detection and Classification of Defects." In Ultrasonic Testing of Materials. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-10680-8_20.

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Kleman, Maurice. "The Topological Classification of Defects." In Formation and Interactions of Topological Defects. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1883-9_2.

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Cordeiro, Peter G. "Classification System for Mandibulectomy Defects." In Atlas of Mandibular and Maxillary Reconstruction with the Fibula Flap. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10684-3_1.

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Cordeiro, Peter G. "Classification System for Maxillectomy Defects." In Atlas of Mandibular and Maxillary Reconstruction with the Fibula Flap. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10684-3_2.

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Anderson, R. H., and F. J. Macartney now deceased. "Classification and Nomenclature of Congenital Heart Defects." In Surgery for Congenital Heart Defects. John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470093188.ch1.

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Béland, M. J., R. C. Franklin, V. D. Aiello, L. Houyel, P. M. Weinberg, and R. H. Anderson. "Nomenclature and Classification of Cardiac Defects." In Pediatric and Congenital Cardiology, Cardiac Surgery and Intensive Care. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-4999-6_251-1.

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Béland, M. J., R. C. Franklin, V. D. Aiello, L. Houyel, P. M. Weinberg, and R. H. Anderson. "Nomenclature and Classification of Cardiac Defects." In Pediatric and Congenital Cardiology, Cardiac Surgery and Intensive Care. Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-4999-6_251-2.

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Trebin, H. R. "Classification of Defects in Liquid Crystals." In Defects in Liquid Crystals: Computer Simulations, Theory and Experiments. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0512-8_1.

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10

Engh, Gerard A. "Classification of Bone Defects Femur and Tibia." In Knee Arthroplasty Handbook. Springer New York, 2006. http://dx.doi.org/10.1007/0-387-33531-5_9.

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Konferenzberichte zum Thema "Defects classification"

1

Bień, Jan, and Małgorzata Gładysz-Bień. "Multi-level Classification of Bridge Defects in Asset Management." In IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management. International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/guimaraes.2019.1100.

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&lt;p&gt;In the paper a proposal of the unified multi-level classification of bridge defects declining condition of bridges is presented as one of the most important elements of asset management. General scheme of bridge degradation process is described with classifications of basic mechanisms of bridge degradation as well as stimulators of degradation mechanisms. Criteria of defects classification are proposed taking into account effects of activities of bridge degradation mechanisms. The proposed general conception of classification methodology of bridge defects is addressed to all types of bridge structures and all types of structural materials. Examples of hierarchical three-level classifications of defects are presented for concrete, steel and masonry bridge structures.&lt;/p&gt;
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Wolfe, Scott, and Steve McGarvey. "Scanning electron microscope automatic defect classification of process induced defects." In SPIE Advanced Lithography, edited by Martha I. Sanchez and Vladimir A. Ukraintsev. SPIE, 2017. http://dx.doi.org/10.1117/12.2258122.

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Nelson, Jeffrey E., Wing Chiu Tam, and R. D. Blanton. "Automatic classification of bridge defects." In 2010 IEEE International Test Conference (ITC). IEEE, 2010. http://dx.doi.org/10.1109/test.2010.5699231.

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Murino, V., M. Bicego, and I. A. Rossi. "Statistical classification of raw textile defects." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333765.

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Jeong, Daun, Dongyeop Kang, and Sangchul Won. "Feature selection for steel defects classification." In 2010 International Conference on Control, Automation and Systems (ICCAS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccas.2010.5670192.

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Boettiger, Tom, Peter Buck, Sankaranarayanan Paninjath, et al. "Automatic classification of blank substrate defects." In SPIE Photomask Technology, edited by Paul W. Ackmann and Naoya Hayashi. SPIE, 2014. http://dx.doi.org/10.1117/12.2069678.

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Rigaill, Denis M., Henry Roussel-Dupre, Michel Tissier, and Yann Guerin. "Automatic defects classification for photolithographics reticles." In 13th Annual BACUS Symposium on Photomask Technology and Management, edited by Edward C. Grady and Jack P. Moneta. SPIE, 1994. http://dx.doi.org/10.1117/12.167264.

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8

Wu, Mingtao, Vir V. Phoha, Young B. Moon, and Amith K. Belman. "Detecting Malicious Defects in 3D Printing Process Using Machine Learning and Image Classification." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67641.

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3D printing, or additive manufacturing, is a key technology for future manufacturing systems. However, 3D printing systems have unique vulnerabilities presented by the ability to affect the infill without affecting the exterior. In order to detect malicious infill defects in 3D printing process, this paper proposes the following: 1) investigate malicious defects in the 3D printing process, 2) extract features based on simulated 3D printing process images, and 3) an experiment of image classification with one group of non-defect infill image and the other group of defect infill training image from 3D printing process. The images are captured layer by layer from the top view of software simulation preview. The data extracted from images is input to two machine learning algorithms, Naive Bayes Classifier and J48 Decision Trees. The result shows Naive Bayes Classifier has an accuracy of 85.26% and J48 Decision Trees has an accuracy of 95.51% for classification.
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Chen, Zhao-he. "New Classification Assessment Rule for Defects Fitness." In ASME 2015 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/pvp2015-45147.

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Based upon three typical materials and metrology stipulated in GB/T 19624, a new classification assessment rule for defects which exceed the acceptance criteria in construction code is given, which includes three levels depending upon the information acquired during assessment. Following its implementation into in-service inspection projects, the compliance with normal assessment can be attained without much effort.
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Jakubec, Jaroslav. "Role of defects in rock mass classification." In Seventh International Symposium on Ground Support in Mining and Underground Construction. Australian Centre for Geomechanics, Perth, 2013. http://dx.doi.org/10.36487/acg_rep/1304_21_jakubec.

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Berichte der Organisationen zum Thema "Defects classification"

1

Dr. Gabe V. Garcia. Automated Diagnosis and Classification of Steam Generator Tube Defects. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/833464.

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Gabe V. Garcia. Eddy Current Signature Classification of Steam Generator Tube Defects Using A Learning Vector Quantization Neural Network. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/836575.

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Gleason, S., and A. Kulkarni. Semiconductor yield improvements through automatic defect classification. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/508162.

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