Journal articles: '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 (October 4, 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 (October 15, 2013): 2393–400. http://dx.doi.org/10.24297/ijct.v11i3.1166.

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â€œQuality is never an accident; it is always the result of intelligent effortâ€ [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 â€œBest Practicesâ€ 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 (June 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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Danilov, E. O. "Legal Classification of Defects in Medical Care." Actual Problems of Russian Law 16, no. 5 (June 9, 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 (March 1, 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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Hua, Liang, Peng Xue, Jin Ping Tang, Hui Jin, and Qi Zhang. "Welding Defects Classification Based on Multi-Weights Neural Network." Advanced Materials Research 820 (September 2013): 130–33. http://dx.doi.org/10.4028/www.scientific.net/amr.820.130.

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Incomplete fusion and incomplete penetration are two types of damage serious welding defects. These two kinds of defects have the similarity in the features in X-ray imaging. Identifying the two kinds of defects automatically and accurately can improve the welding technology and improve the quality of welding effectively. The causes of defects and features of X-ray images are described in the paper. The welding defects calssification method based on multi-weights neural network is put forward in the paper. The multi-weights neural network based on graphic geometry theory is introduced, which uses the geometrical shape in high dimensional space to cover the same class defect samples via constructing multi-weights neural network. The experimental results proved the effectiveness of the algorithm.

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Han, Wan Jiang, Sun Yi, Li Yan, Wei Jian Li, Li Ye, Han Xiao, and Liu Chi. "Study on the Defect Classification Model." Applied Mechanics and Materials 513-517 (February 2014): 4008–11. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4008.

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This paper presents the concept of defect classification model, which is based on the technology of similarity. Defect classification model can analyze software defect more efficiently and provides the basis of solving problems quickly. This paper applies this model to GUI project and gives a GUI defect classification model based on large number of interface defects. Experiments show that the model is useful to improve the process of defect management and be used for test planning and implementation.

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Kerres, Karsten, Sylvia Gredigk-Hoffmann, Rüdiger Jathe, Stefan Orlik, Mustafa Sariyildiz, Torsten Schmidt, Klaus-Jochen Sympher, and Adrian Uhlenbroch. "Future approaches for sewer system condition assessment." Water Practice and Technology 15, no. 2 (April 8, 2020): 386–93. http://dx.doi.org/10.2166/wpt.2020.027.

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Abstract Different analytical approaches exist to describe the structural substance or wear reserve of sewer systems. The aim is to convert engineering assessments of often complex defect patterns into computational algorithms and determine a substance class for a sewer section or manhole. This analytically determined information is essential for strategic rehabilitation planning processes up to network level, as it corresponds to the most appropriate rehabilitation type and can thus provide decision-making support. Current calculation methods differ clearly from each other in parts, so that substance classes determined by the different approaches are only partially comparable with each other. The objective of the German R&D cooperation project ‘SubKanS’ is to develop a methodology for classifying the specific defect patterns resulting from the interaction of all the individual defects, and their severities and locations. The methodology takes into account the structural substance of sewer sections and manholes, based on real data and theoretical considerations analogous to the condition classification of individual defects. The result is a catalogue of defect patterns and characteristics, as well as associated structural substance classifications of sewer systems (substance classes). The methodology for sewer system substance classification is developed so that the classification of individual defects can be transferred into a substance class of the sewer section or manhole, eventually taking into account further information (e.g. pipe material, nominal diameter, etc.). The result is a validated methodology for automated sewer system substance classification.

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Muhtadan, Risanuri Hidayat, Widyawan, and Fahmi Amhar. "Weld Defect Classification in Radiographic Film Using Statistical Texture and Support Vector Machine." Advanced Materials Research 896 (February 2014): 695–700. http://dx.doi.org/10.4028/www.scientific.net/amr.896.695.

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Weld defect identification requires radiographic operator experience, so the interpretation of weld defect type could potentially bring subjectivity and human error factor. This paper proposes Statistical Texture and Support Vector Machine method for weld defect type classification in radiographic film. Digital image processing technique applied in this paper implements noise reduction using median filter, contrast stretching, and image sharpening using Laplacian filter. Statistical method feature extraction based on image histogram was proposed for describing weld defects texture characteristic of a radiographic film digital image. Multiclass Support Vector Machine (SVM) algorithm was used to perform classification of weld defects type. The result of classification testing shows that the proposed method can classify 83.3% correctly from 60 testing data of weld defects radiographic films.

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15

Santos, J. B., and F. Perdigão. "Automatic defects classification — a contribution." NDT & E International 34, no. 5 (July 2001): 313–18. http://dx.doi.org/10.1016/s0963-8695(00)00043-8.

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16

Becker, A. E. "Classification of ventricular septal defects." Current Opinion in Cardiology 6, no. 1 (February 1991): 135–38. http://dx.doi.org/10.1097/00001573-199102000-00021.

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17

Sika, Robert, Michał Rogalewicz, Paweł Popielarski, Dorota Czarnecka-Komorowska, Damian Przestacki, Katarzyna Gawdzińska, and Paweł Szymański. "Decision Support System in the Field of Defects Assessment in the Metal Matrix Composites Castings." Materials 13, no. 16 (August 12, 2020): 3552. http://dx.doi.org/10.3390/ma13163552.

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This paper presented a new approach to decision making support of defects assessment in metal matrix composites (MMC). It is a continuation of the authors’ papers in terms of a uniform method of casting defects assessment. The idea of this paper was to design an open-access application (follow-up system called Open Atlas of Casting Defects (OACD)) in the area of industry and science. This a new solution makes it possible to quickly identify defect types considering the new classification of casting defects. This classification complements a classical approach by adding a casting defect group called structure defects, which is especially important for metal matrix composites. In the paper, an application structure, and the possibility of its use in casting defects assessment were introduced.

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Deng, Weiquan, Bo Ye, Jun Bao, Guoyong Huang, and Jiande Wu. "Classification and Quantitative Evaluation of Eddy Current Based on Kernel-PCA and ELM for Defects in Metal Component." Metals 9, no. 2 (February 1, 2019): 155. http://dx.doi.org/10.3390/met9020155.

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Eddy current testing technology is widely used in the defect detection of metal components and the integrity evaluation of critical components. However, at present, the evaluation and analysis of defect signals are still mostly based on artificial evaluation. Therefore, the evaluation of defects is often subjectively affected by human factors, which may lead to a lack in objectivity, accuracy, and reliability. In this paper, the feature extraction of non-linear signals is carried out. First, using the kernel-based principal component analysis (KPCA) algorithm. Secondly, based on the feature vectors of defects, the classification of an extreme learning machine (ELM) for different defects is studied. Compared with traditional classifiers, such as artificial neural network (ANN) and support vector machine (SVM), the accuracy and rapidity of ELM are more advantageous. Based on the accurate classification of defects, the linear least-squares fitting is used to further quantitatively evaluate the defects. Finally, the experimental results have verified the effectiveness of the proposed method, which involves automatic defect classification and quantitative analysis.

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Aljassmi, Hamad A., and Sangwon Han. "CLASSIFICATION AND OCCURRENCE OF DEFECTIVE ACTS IN RESIDENTIAL CONSTRUCTION PROJECTS." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 20, no. 2 (March 20, 2014): 175–85. http://dx.doi.org/10.3846/13923730.2013.801885.

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Defects can have a significant impact on construction performance. Numerous studies have attempted to identify their root causes, contending that the prevention of defects could be achieved by eliminating the root causes. Yet, their direct causes also need to be considered in order to identify the sequence of events leading to defects. This study aims to classify the defective acts that are directly linked to the occurrence of a defect, in order to provide insights about the nature and the impact of different types of direct causes. The study involves investigation into 272 defects from 81 disputes that occurred in the Dubai residential construction industry in 2009. Results from this study reveal that the majority of construction defects are driven by a violation of practices or workers’ lack of skill and competence. While it is difficult to prevent deliberate violations, increased effort toward growing skills and competencies would be effective in reducing defects, and therefore improving construction performance. Also, classification of defective acts is envisioned as a platform toward a more thorough causal tracking of construction defects, facilitating the identification of latent conditions resulting in defects.

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Mirolyubov, L. M. "Kazan version of the classification of congenital heart defects of John Kirklin." Rossiyskiy Vestnik Perinatologii i Pediatrii (Russian Bulletin of Perinatology and Pediatrics) 64, no. 5 (November 16, 2019): 246–49. http://dx.doi.org/10.21508/1027-4065-2019-64-5-246-249.

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The article is devoted to the analysis of classifications of congenital heart defects from a practical point of view. The researchers present their classification of congenital heart defects with the substantiation of optimal terms of surgical correction. The proposed classification allows us to predict possible critical hemodynamic conditions in children with heart defects both in the neonatal period and in other age groups. The classification creates the basis for choosing the treatment tactics of patients with congenital heart defects using the known stages of hemodynamic changes, it has been used by the cardiologists and cardiac surgeons in the Republic of Tatarstan for more than 15 years.

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Wu, Bao Hua, Lei Duan, Gui Hua Wang, Hai Yang Wang, and Jing Peng. "Gene Expression Programming Based Classification for Automated Birth Defects Detection." Applied Mechanics and Materials 197 (September 2012): 508–14. http://dx.doi.org/10.4028/www.scientific.net/amm.197.508.

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With the rapid development of digital medicine, improving the diagnostic accuracy for birth defects (BD) by using data mining techniques has been paid more attentions by researchers. In this paper, an automated classification technique based on Gene Expression Programming (GEP) to detect the defect infants, named Birth Defects Detection based on Gene Expression Programming (BDD-GEP) is proposed. The main contributions of this paper include: (1) proposing two contrast inequalities (CIs) for birth defects detection: the defection contrasts to normal and the normal contrasts to defection, (2) designing a new fitness function to mine the normal and defect CIs by GEP, (3) presenting a method to select useful CIs for classification, (4) implementing the BDD-GEP algorithm through combining the proposed CIs with k-Nearest Neighbor algorithm. In order to evaluate the proposed classification method, 11,897 infant samples from national center for birth defects monitoring of China were used, and the method was compared with several existing classification methods. The experimental results show that the overall detection accuracy of BDD-GEP was as high as 87.8%. Specifically, the F-measure of the detect samples was about 70.2%, and the F-measure of the normal samples was about 92.3%.

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Liu, Zixi, Zhengliang Hu, Longxiang Wang, Tianshi Zhou, Jintao Chen, Zhenyu Zhu, Hao Sui, Hongna Zhu, and Guangming Li. "Effective detection of metal surface defects based on double-line laser ultrasonic with convolutional neural networks." Modern Physics Letters B 35, no. 15 (April 15, 2021): 2150263. http://dx.doi.org/10.1142/s0217984921502638.

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The time–frequency analysis by smooth Pseudo-Wigner-Ville distribution (SPWVD) is utilized for the double-line laser ultrasonic signal processing, and the effective detection of the metal surface defect is achieved. The double-line source laser is adopted for achieving more defects information. The simulation model by using finite element method is established in a steel plate with three typical metal surface defects (i.e. crack, air hole and surface scratch) in detail. Besides, in order to improve the time resolution and frequency resolution of the signal, the SPWVD method is mainly used. In addition, the deep learning defect classification model based on VGG convolutional neural network (CNN) is set up, also, the data enhancement method is adopted to extend training data and improve the defects detection properties. The results show that, for different types of metal surface defects with sub-millimeter size, the classification accuracy of crack, air holes and scratch surface are 94.6%, 94% and 94.6%, respectively. The SPWVD and CNN algorithm for processing the laser ultrasonic signal and defects classification supplies a useful way to get the defect information, which is helpful for the ultrasonic signal processing and material evaluation.

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Czimmermann, Tamás, Gastone Ciuti, Mario Milazzo, Marcello Chiurazzi, Stefano Roccella, Calogero Maria Oddo, and Paolo Dario. "Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY." Sensors 20, no. 5 (March 6, 2020): 1459. http://dx.doi.org/10.3390/s20051459.

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This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

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Bolotin, М. V., A. M. Mudunov, V. Yu Sobolevsky, А. А. Akhundov, I. M. Gelfand, and S. V. Sapromadze. "Microsurgical reconstruction of the hard palate after resections for malignant tumors." Head and Neck Tumors (HNT) 10, no. 4 (January 16, 2021): 25–31. http://dx.doi.org/10.17650/2222-1468-2020-10-4-25-31.

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Background. The main aims of hard palate reconstruction include separation of the nasal and oral cavities, restoration of chewing, swallowing, speech, ensuring good aesthetic results, and preparation for dental rehabilitation. The choice of reconstruction method is determined by such factors as the nature and location of the defect, surgeon’s experience in certain reconstruction methods, cancer prognosis, and patient’s preference. The study objective is to analyze the results of microsurgical reconstruction of hard palate defects using different types of flaps. Materials and methods. Forty-one (41) patients underwent microsurgical reconstruction of defects of the hard palate, soft palate, and alveolar process between 2014 and 2020. Defects of the anterior portion of the hard palate (grade I, IIc, IId according to the classification of J.S. Brown; grade IB, II, III according to the classification of D.J. Okay) were formed in 13 cases; all of them involved the alveolar margin of the maxilla to some extent. To repair these defects, we used flaps containing revascularized bone (n = 10; scapular tip flaps in 8 patients and fibular flaps in 2 patients) and fasciocutaneous or musculocutaneous flaps (n = 3; radial fasciocutaneous flaps in 2 patients and musculocutaneous flap from the anterior surface of the thigh in 1 patient). Defects of the posterior portion of the hard palate (grade Ib according to the classification of J.S. Brown; grade Ib according to the classification of D.J. Okay) were formed in 18 patients. To repair these defects, we used radial fasciocutaneous flaps (n = 17) and fibular autologous graft containing skin, muscles, and bone (n = 1). Soft palate resection was performed in 10 patients; all surgeries were combination, since the lateral oropharyngeal wall was included in the block of removed tissues. None of the patients had the opposite side affected. These defects were repaired using radial fasciocutaneous flaps.Results. Six patients (15 %) developed total flap necrosis due to venous thrombosis on days 2, 3, and 6 postoperatively; two patients developed flap necrosis due to arterial thrombosis 2 days postoperatively. Good speech quality was achieved in 33 patients (80 %), while 6 patients (15 %) had satisfactory speech; rhinolalia was observed in 2 patients (5 %). All patients with defects of the posterior hard palate and of the soft palate had excellent aesthetic results. Among participants with defects of the anterior hard palate and alveolar process, 10 patients had excellent aesthetic results, while 5 individuals had good results. Three patients had unsatisfactory results due to scarring in the middle portion of the face.Conclusion. Patients with subtotal defects of the hard palate and defects of its anterior portion (grade I, IIb, IIc according to the classification of J.S. Brown; grade II, III according to the classification of D.J. Okay) require repair of the alveolar margin of the maxilla; flaps containing revascularized bone are preferable in this case. The method of choice is defect repair using musculoskeletal scapular tip flap. In patients with short defects, defects located posteriorly, minimal or no defect of the alveolar margin of the maxilla (grade Ia, IB according to the classification of J.S. Brown; grade Ia, Ib according to the classification of D.J. Okay; grade V according to the classification of M.A. Aramany), soft palate defects, radial fasciocutaneous flaps should be used.

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Benzahioul, Samia, Abderrezak Metatla, Adlen Kerboua, Dimitri Lefebvre, and Riad Bendib. "Use of Support Vector Machines for Classification of Defects in the Induction Motor." Acta Universitatis Sapientiae, Electrical and Mechanical Engineering 11, no. 1 (December 1, 2019): 1–21. http://dx.doi.org/10.2478/auseme-2019-0001.

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Abstract The classification and detection of defects play an important role in different disciplines. Research is oriented towards the development of approaches for the early detection and classification of defects in electrical drive systems. This paper, proposes a new approach for the classification of induction motor defects based on image processing and pattern recognition. The proposed defect classification approach was carried out in four distinct stages. In the first step, the stator currents were represented in the 3D space and projected onto the 2D space. In the second step, the projections obtained were transformed into images. In the third step, extraction of features whereas the Histogram of Oriented Gradient (HOG) is used to construct a descriptor based on several sizes of cells. In the fourth step, a method of classifying the induction motor defects based on the Support Vector Machine (SVM) was applied. The evaluation results of the developed approach show the efficiency and the precision of classification of the proposed approach.

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Liu, Fen, Yuxuan Liu, and Hongqiang Sang. "Multi-Classifier Decision-Level Fusion Classification of Workpiece Surface Defects Based on a Convolutional Neural Network." Symmetry 12, no. 5 (May 25, 2020): 867. http://dx.doi.org/10.3390/sym12050867.

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Various defects are formed on the workpiece surface during the production process. Workpiece surface defects are classified according to various characteristics, which includes a bumped surface, scratched surface and pit surface. Suppliers analyze the cause of workpiece surface defects through the defect types and thus determines the subsequent processing. Therefore, the correct classification is essential regarding workpiece surface defects. In this paper, a multi-classifier decision-level fusion classification model for workpiece surface defects based on a convolutional neural network (CNN) was proposed. In the proposed model, the histogram of oriented gradient (HOG) was used to extract the features of the second fully connected layer of the CNN, and the features of the HOG were further extracted by using the local binary patterns (LBP), which was called the HOG–LBP feature extraction. Finally, this paper designed a symmetry ensemble classifier, which was used to classify the features of the last fully connected layer of the CNN and the features of the HOG–LBP. The comprehensive decision was made by fusing the classification results of the symmetry structure channels. The experiments were carried out, and the results showed that the proposed model could improve the accuracy of the workpiece surface defect classification.

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Lu, Manhuai, and Chin-Ling Chen. "Detection and Classification of Bearing Surface Defects Based on Machine Vision." Applied Sciences 11, no. 4 (February 18, 2021): 1825. http://dx.doi.org/10.3390/app11041825.

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Surface defects on bearings can directly affect the service life and reduce the performance of equipment. At present, the detection of bearing surface defects is mostly done manually, which is labor-intensive and results in poor stability. To improve the inspection speed and the defect recognition rate, we proposed a bearing surface defect detection and classification method using machine vision technology. The method makes two main contributions. It proposes a local multi-neural network (Lc-MNN) image segmentation algorithm with the wavelet transform as the classification feature. The precision segmentation of the defect image is accomplished in three steps: wavelet feature extraction, Lc-MNN region division, and Lc-MNN classification. It also proposes a feature selection algorithm (SCV) that makes comprehensive use of scalar feature selection, correlation analysis, and vector feature selection to first remove similar features through correlation analysis, further screen the results with a scalar feature selection algorithm, and finally select the classification features using a feature vector selection algorithm. Using 600 test samples with three types of defect in the experiment, an identification rate of 99.5% was achieved without the need for large-scale calculation. The comparison tests indicated that the proposed method can achieve efficient feature selection and defect classification.

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Kumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (March 23, 2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.

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Early detection of software defects is very important to decrease the software cost and subsequently increase the software quality. Success of software industries not only depends on gaining knowledge about software defects, but largely reflects from the manner in which information about defect is collected and used. In software industries, individuals at different levels from customers to engineers apply diverse mechanisms to detect the allocation of defects to a particular class. Categorizing bugs based on their characteristics helps the Software Development team take appropriate actions to reduce similar defects that might get reported in future releases. Classification, if performed manually, will consume more time and effort. Human resource having expert testing skills & domain knowledge will be required for labeling the data. Therefore, the need of automatic classification of software defect is high.This work attempts to categorize defects by proposing an algorithm called Software Defect CLustering (SDCL). It aims at mining the existing online bug repositories like Eclipse, Bugzilla and JIRA for analyzing the defect description and its categorization. The proposed algorithm is designed by using text clustering and works with three major modules to find out the class to which the defect should be assigned. Software bug repositories hold software defect data with attributes like defect description, status, defect open and close date. Defect extraction module extracts the defect description from various bug repositories and converts it into unified format for further processing. Unnecessary and irrelevant texts are removed from defect data using data preprocessing module. Finally grouping of defect data into clusters of similar defect is done using clustering technique. The algorithm provides classification accuracy more than 80% in all of the three above mentioned repositories.

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Feng, Xinglong, Xianwen Gao, and Ling Luo. "A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel." Mathematics 9, no. 19 (September 23, 2021): 2359. http://dx.doi.org/10.3390/math9192359.

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Hot-rolled strip steel is widely used in automotive manufacturing, chemical and home appliance industries, and its surface quality has a great impact on the quality of the final product. In the manufacturing process of strip steel, due to the rolling process and many other reasons, the surface of hot rolled strip steel will inevitably produce slag, scratches and other surface defects. These defects not only affect the quality of the product, but may even lead to broken strips in the subsequent process, seriously affecting the continuation of production. Therefore, it is important to study the surface defects of strip steel and identify the types of defects in strip steel. In this paper, a scheme based on ResNet50 with the addition of FcaNet and Convolutional Block Attention Module (CBAM) is proposed for strip defect classification and validated on the X-SDD strip defect dataset. Our solution achieves a classification accuracy of 94.11%, higher than more than a dozen other compared deep learning models. Moreover, to adress the problem of low accuracy of the algorithm in classifying individual defects, we use ensemble learning to optimize. By integrating the original solution with VGG16 and SqueezeNet, the recognition rate of oxide scale of plate system defects improved by 21.05 percentage points, and the overall defect classification accuracy improved to 94.85%.

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Antonov, O. V., G. P. Filippov, and Ye V. Bogachyova. "On the problem of terminology and classification in birth developmental defects and morphogenetic variants." Bulletin of Siberian Medicine 10, no. 4 (August 28, 2011): 179–82. http://dx.doi.org/10.20538/1682-0363-2011-4-179-182.

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Based on his own results and the latest literature evidence the author raises a point concerning the birth defects nosological forms and birth morphogenetic readings registration. Living hitherto in the literature conception differences regarding terms «birth developmental defects» and «birth morphogenetic variants» stipulate for the significance of varied approach in definition birth defect as developmental defects or as developmental anomaly taking into consideration the insight of distinction in these states. The author proposes the addition to the subsist in native literature term «birth developmental defects» Registration of birth defects according to the latest International Classification of Diseases with the register organization of all birth defects apart from the register of birth anomalies is specificated in the paper.

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Zhu, Jinsong, and Jinbo Song. "An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges." Applied Sciences 10, no. 3 (February 2, 2020): 972. http://dx.doi.org/10.3390/app10030972.

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This paper mainly improves the visual geometry group network-16 (VGG-16), which is a classic convolutional neural network (CNN), to classify the surface defects on cement concrete bridges in an accurate manner. Specifically, the number of fully connected layers was reduced by one, and the Softmax classifier was replaced with a Softmax classification layer with seven defect tags. The weight parameters of convolutional and pooling layers were shared in the pre-trained model, and the rectified linear unit (ReLU) function was taken as the activation function. The original images were collected by a road inspection vehicle driving across bridges on national and provincial highways in Jiangxi Province, China. The images on surface defects of cement concrete bridges were selected, and divided into a training set and a test set, and preprocessed through morphology-based weight adaptive denoising. To verify its performance, the improved VGG-16 was compared with traditional shallow neural networks (NNs) like the backpropagation neural network (BPNN), support vector machine (SVM), and deep CNNs like AlexNet, GoogLeNet, and ResNet on the same sample dataset of surface defects on cement concrete bridges. Judging by mean detection accuracy and top-5 accuracy, our model outperformed all the contrastive methods, and accurately differentiated between images with seven classes of defects such as normal, cracks, fracturing, plate fracturing, corner rupturing, edge/corner exfoliation, skeleton exposure, and repairs. The results indicate that our model can effectively extract the multi-layer features from surface defect images, which highlights the edges and textures. The research findings shed important new light on the detection of surface defects and classification of defect images.

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Zhou, Haiyan, Zilong Zhuang, Ying Liu, Yang Liu, and Xiao Zhang. "Defect Classification of Green Plums Based on Deep Learning." Sensors 20, no. 23 (December 7, 2020): 6993. http://dx.doi.org/10.3390/s20236993.

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The green plum is rich in amino acids, lipids, inorganic salts, vitamins, and trace elements. It has high nutritional value and medicinal value and is very popular among Chinese people. However, green plums are susceptible to collisions and pests during growth, picking, storage, and transportation, causing surface defects, affecting the quality of green plums and their products and reducing their economic value. In China, defect detection and grading of green plum products are still performed manually. Traditional manual classification has low accuracy and high cost, which is far from meeting the production needs of green plum products. In order to improve the economic value of green plums and their products and improve the automation and intelligence level of the product production process, this study adopted deep learning methods based on a convolutional neural network and cost-effective computer vision technology to achieve efficient classification of green plum defects. First, a camera and LEDs were used to collect 1240 green plum images of RGB, and the green plum experimental classification standard was formulated and divided into five categories, namely, rot, spot, scar, crack, and normal. Images were randomly divided into a training set and test set, and the number of images of the training set was expanded. Then, the stochastic weight averaging (SWA) optimizer and w-softmax loss function were used to improve the VGG network, which was trained and tested to generate a green plum defect detection network model. The average recognition accuracy of green plum defects was 93.8%, the test time for each picture was 84.69 ms, the recognition rate of decay defect was 99.25%, and the recognition rate of normal green plum was 95.65%. The results were compared with the source VGG network, resnet18 network, and green lemon network. The results show that for the classification of green plum defects, the recognition accuracy of the green plum defect detection network increased by 9.8% and 16.6%, and the test speed is increased by 1.87 and 6.21 ms, respectively, which has certain advantages.

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Singh, Karanveer, and Jaspreet Kaleka. "IDENTIFICATION AND CLASSIFICATION OF FABRIC DEFECTS." International Journal of Advanced Research 4, no. 8 (August 31, 2016): 1137–41. http://dx.doi.org/10.21474/ijar01/1314.

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Tanteles, George A., and Mohnish Suri. "Classification and aetiology of birth defects." Paediatrics and Child Health 17, no. 6 (June 2007): 233–43. http://dx.doi.org/10.1016/j.paed.2007.03.005.

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Huang, Chang-Chiun, and I.-Chun Chen. "Neural-Fuzzy Classification for Fabric Defects." Textile Research Journal 71, no. 3 (March 2001): 220–24. http://dx.doi.org/10.1177/004051750107100306.

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Peskind, Steven, and Fred Stucker. "Classification and Etiology of Nasal Defects." Facial Plastic Surgery 10, no. 04 (October 1994): 313–16. http://dx.doi.org/10.1055/s-2008-1064581.

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Nakanishi, Hiizu, Kiyoshi Hayashi, and Hiroyuki Mori. "Topological classification of unknotted ring defects." Communications in Mathematical Physics 117, no. 2 (June 1988): 203–13. http://dx.doi.org/10.1007/bf01223590.

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Song, Zong-Ming, Yan-Juan Sheng, Xiao-Ying Fu, An-Quan Xue, Fan Lu, Qin-Mei Wang, and Jia Qu. "Proposed classification of lens capsule defects." Graefe's Archive for Clinical and Experimental Ophthalmology 245, no. 11 (June 12, 2007): 1653–58. http://dx.doi.org/10.1007/s00417-007-0614-5.

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Jiang, Qingsheng, Dapeng Tan, Yanbiao Li, Shiming Ji, Chaopeng Cai, and Qiming Zheng. "Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning." Applied Sciences 10, no. 1 (December 20, 2019): 87. http://dx.doi.org/10.3390/app10010087.

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Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection.

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Ben Salem, Yassine, and Mohamed Naceur Abdelkrim. "Texture classification of fabric defects using machine learning." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 4390. http://dx.doi.org/10.11591/ijece.v10i4.pp4390-4399.

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In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

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Shih, Po-Chou, Chun-Chin Hsu, and Fang-Chih Tien. "Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks." Symmetry 12, no. 5 (May 2, 2020): 705. http://dx.doi.org/10.3390/sym12050705.

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Silicon wafer is the most crucial material in the semiconductor manufacturing industry. Owing to limited resources, the reclamation of monitor and dummy wafers for reuse can dramatically lower the cost, and become a competitive edge in this industry. However, defects such as void, scratches, particles, and contamination are found on the surfaces of the reclaimed wafers. Most of the reclaimed wafers with the asymmetric distribution of the defects, known as the “good (G)” reclaimed wafers, can be re-polished if their defects are not irreversible and if their thicknesses are sufficient for re-polishing. Currently, the “no good (NG)” reclaimed wafers must be first screened by experienced human inspectors to determine their re-usability through defect mapping. This screening task is tedious, time-consuming, and unreliable. This study presents a deep-learning-based reclaimed wafers defect classification approach. Three neural networks, multilayer perceptron (MLP), convolutional neural network (CNN) and Residual Network (ResNet), are adopted and compared for classification. These networks analyze the pattern of defect mapping and determine not only the reclaimed wafers are suitable for re-polishing but also where the defect categories belong. The open source TensorFlow library was used to train the MLP, CNN, and ResNet networks using collected wafer images as input data. Based on the experimental results, we found that the system applying CNN networks with a proper design of kernels and structures gave fast and superior performance in identifying defective wafers owing to its deep learning capability, and the ResNet averagely exhibited excellent accuracy, while the large-scale MLP networks also acquired good results with proper network structures.

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Jian, Chuan Xia, Jian Gao, and Xin Chen. "A Review of TFT-LCD Panel Defect Detection Methods." Advanced Materials Research 734-737 (August 2013): 2898–902. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.2898.

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TFT-LCD panel defect detection has been one of the difficulties in this field because of fuzzy defect boundary, low contrast between defects and background, and low detection speed. The structure of TFT-LCD panels and classification are introduced. Through the analysis of panel defect features, current detection methods for the TFT-LCD panel defects are reviewed. The key technologies of feature extraction and defect classification are analyzed in the defect image recognition of TFT-LCD panel. Meanwhile the methods of fuzzy boundary defect segmentation, image subtraction and image filtering are also discussed. Finally, the characteristics and advantages of these detection methods are concluded, and several key issues for the TFT-LCD defect detection have been proposed for future development.

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Wang, An Na, Chao Hu, Chang Liang Xue, and Hong Rui Zhang. "Recognition and Classification of Hot Strip Surface Defect Based on Binary Tree SVM." Advanced Materials Research 538-541 (June 2012): 427–30. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.427.

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The paper presents a new method which uses Binary Tree SVM in the automatic classification of surface defects for hot strip. Two types of Binary Tree SVMs are applied in defect classification. Compared with BP neural network and one-against-one SVM, the algorithm adopted in the paper greatly improved the accuracy of classification and decreased the classification time.

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Niles, S. N., S. Fernando, and W. D. G. Lanerolle. "A System for Analysis, Categorisation and Grading of Fabric Defects using Computer Vision." Research Journal of Textile and Apparel 19, no. 1 (February 1, 2015): 59–64. http://dx.doi.org/10.1108/rjta-19-01-2015-b006.

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Inspection of fabrics is a major consideration in fabric manufacture, as well as in manufacture of garments and other fabric-based goods. In this research, a computer-based system for objective assessment of fabric defects was designed with emphasis placed on fabric defects occurring in the Sri Lankan industry. Image processing techniques were used to analyse scanned images of the test fabric, compare it with an ideal sample, and identify defects according to pre-learnt rules. The information gathered was then used to grade the fabric, either by determining the frequency of defect occurrence or assigning points. A new classification method for common defects was designed, thereby facilitating grading according to commonly used grading systems. A coding system for defects was also designed to help report defects to the user. The fabric defects were classified and stored according to the developed classification method and coding system.

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Chien, Jong-Chih, Ming-Tao Wu, and Jiann-Der Lee. "Inspection and Classification of Semiconductor Wafer Surface Defects Using CNN Deep Learning Networks." Applied Sciences 10, no. 15 (August 2, 2020): 5340. http://dx.doi.org/10.3390/app10155340.

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Due to advances in semiconductor processing technologies, each slice of a semiconductor is becoming denser and more complex, which can increase the number of surface defects. These defects should be caught early and correctly classified in order help identify the causes of these defects in the process and eventually help to improve the yield. In today’s semiconductor industry, visible surface defects are still being inspected manually, which may result in erroneous classification when the inspectors become tired or lose objectivity. This paper presents a vision-based machine-learning-based method to classify visible surface defects on semiconductor wafers. The proposed method uses deep learning convolutional neural networks to identify and classify four types of surface defects: center, local, random, and scrape. Experiments were performed to determine its accuracy. The experimental results showed that this method alone, without additional refinement, could reach a top accuracy in the range of 98% to 99%. Its performance in wafer-defect classification shows superior performance compared to other machine-learning methods investigated in the experiments.

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Wang, Shuai, Xiaojun Xia, Lanqing Ye, and Binbin Yang. "Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks." Metals 11, no. 3 (February 26, 2021): 388. http://dx.doi.org/10.3390/met11030388.

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Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models.

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Shankar, N. G., Z. W. Zhong, and N. Ravi. "Classification of Defects on Semiconductor Wafers Using Priority Rules." Defect and Diffusion Forum 230-232 (November 2004): 135–48. http://dx.doi.org/10.4028/www.scientific.net/ddf.230-232.135.

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This paper presents a template-based vision system to detect and classify the nonuniformaties that appear on the semiconductor wafer surfaces. Design goals include detection of flaws and correlation of defect features based on semiconductor industry expert’s knowledge. The die pattern is generated and kept as the reference beforehand from the experts in the semiconductor industry. The system is capable of identifying the defects on the wafers after die sawing. Each unique defect structure is defined as an object. Objects are grouped into user-defined categories such as chipping, metallization peel off, silicon dust contamination, etc., after die sawing and micro-crack, scratch, ink dot being washed off, bridging, etc., from the wafer. This paper also describes the vision system in terms of its hardware modules, as well as the image processing algorithms utilized to perform the functions.

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Zhang, Jing Wei, Shuai Wang, and Hui Xuan Huang. "Study on Classification and Regulation for Defects on Railway Tunnel." Applied Mechanics and Materials 580-583 (July 2014): 1207–11. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.1207.

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Quality defects and diseases will happen to railway tunnel after it in operation four to five years later, such as lining split, water leakage and landslide of second lining. Based on the case of Goujiagou tunnel located at Ⅳ surrounding rock with mudstone and sandstone, the regulation for this tunnel is put forward; the causes and characteristics of all kinds of defects are analyzed by studying on the particularity of railway tunnel construction. Based on different extend of injury, the classification of the same type defect is proposed. Research results indicate that the regulation design is feasible and the treating technique is appropriate by using new technology, methods and materials. The results will provide reference to design for the same type tunnel regulation.

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Boikov, Aleksei, Vladimir Payor, Roman Savelev, and Alexandr Kolesnikov. "Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning." Symmetry 13, no. 7 (June 29, 2021): 1176. http://dx.doi.org/10.3390/sym13071176.

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The paper presents a methodology for training neural networks for vision tasks on synthesized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.

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Mahdi, Nada Saleh, and Hussein Reza Mahdi Reza Mahdi. "A Case Study in the Transformers and Household Appliances Factory." Iraqi Administrative Sciences Journal 1, no. 2 (June 30, 2017): 591–608. http://dx.doi.org/10.33013/iqasj.v1n2y2017.pp591-608.

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The research aims to identify the best way to evaluate the final product quality level, and through the comparison between the results of the value of demerits by using demerit system that depending on quadrant classification accredited by the researchers for defects with the method that used at the factory, that depending on three-classification of defects, and used cause and effect Diagram on the defects of the category (A) to identify the main and Secondary reasons that occurrence of this defect, Use the method case study to reach the goals of the research and was selected factory of transformers and household appliances Which represents the one of the formations of the General company for Electronic Industries spatial limits of the study, as were selected reflective product sample for the application of this study as it consists of 87 part which increases the probability of the emergence defects that different influence the reflective product quality, Search results showed that the use of demerit system, which depends on the quad classification for defects, that will lead to a devaluation of demerit value and thus improve the quality product reflective, In addition to identifying the main reasons and secondary that lead to emergence defects of class (A), It was identified four causes is (Touching the wires of body, Presence scratch in the connecting wires, Poor quality of connecting wires, A defect in the insulation material).

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Journal articles on the topic 'Defects classification'

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