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Journal articles on the topic 'Defects classification'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

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 (inv

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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 softw

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3

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

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Du Rand, Francois, Malan Van Tonder, Andre Van Der Merwe, et al. "Powder Bed Defects Classification: An Industry Perspective." MATEC Web of Conferences 370 (2022): 06003. http://dx.doi.org/10.1051/matecconf/202237006003.

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The manufacture of defect-free parts has been a key discussion topic with the widespread adoption of additive manufacturing by industry. While significant research has been performed on the detection of powder bed defects, the focus has been on the classification of the defects according to defect type. However, when looking at creating a closed loop feedback system, it is important for the machine to make autonomous decisions regarding defects. The focus of this paper will be to create a defect severity classification matrix based on industry partner experience as well as published literature

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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 o

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Ivanov, S. A. "CLASSIFICATION DEFECTS IN THE LOWER LIP." Health and Ecology Issues, no. 2 (June 28, 2005): 89–92. http://dx.doi.org/10.51523/2708-6011.2005-2-2-18.

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The systematizing of defects of the lower lip following resection helps to choose the optimal choice of the cheiloplasty. We have analyzed the patients group including 144 reconstructions of lower lip. We have worked out the classification of the defects in the lower lip. It is adapted to the modern TNM-staging of malignant tumors. The defects following T1-tumor excision are corresponding to less than 1/3 of lip, the defects following T2-tumor excision are corresponding to 1/3-1/2 of lip, the defects following T1-tumor excision are corresponding to more then 1/2 of lip. There are included the

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7

Liu, Shujie. "Classification of Tobacco Defects based on Vgg16." Academic Journal of Science and Technology 12, no. 2 (2024): 26–27. http://dx.doi.org/10.54097/mmeyx924.

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In the existing cigarette packet defect detection using simple image processing methods, the defect detection capability is limited and the corresponding defects cannot be counted. This paper addresses this problem and proposes a vgg16-based defect classification method for cigarette packets, which can effectively detect and count the defects of cigarette packets. Experiments have proved that the detection accuracy can reach 100% under ideal conditions.

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Mo, Dongmei, and Wai Keung Wong. "Fabric Defect Classification based on Deep Hashing Learning." AATCC Journal of Research 8, no. 1_suppl (2021): 191–201. http://dx.doi.org/10.14504/ajr.8.s1.23.

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Classifying categories of fabric defects can greatly help to identify the source of causing fabric defects in the textile manufacturing process. Most existing artificial intelligence based methods focus on identifying and locating defective regions and do not analyze the categories of the defects. On the other hand, as current fabric defect detection methods depend on handcrafted features, they can only handle fabric with specific patterns or textures. In this paper, we propose a novel model which can learn high-level representation from the automatic observations of the input images that can

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Li, Zhenwei, Shihai Zhang, Chongnian Qu, Zimiao Zhang, and Feng Sun. "Research on multi-defects classification detection method for solar cells based on deep learning." PLOS ONE 19, no. 6 (2024): e0304819. http://dx.doi.org/10.1371/journal.pone.0304819.

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Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detecting defects (mismatch), general defects (bubble, glass-crack and cell-crack) and easy-detecting defects (glass-upside-down). Corresponding to different types of defects, the deep learning model with different optimization methods and a classification detection method based on multi-models fusion are proposed in the paper. In the proposed model, in

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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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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 th

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Yang, Wenjia, Youhang Zhou, Gaolei Meng, Yuze Li, and Tianyu Gong. "Improving the Efficiency of Steel Plate Surface Defect Classification by Reducing the Labelling Cost Using Deep Active Learning." Strojniški vestnik - Journal of Mechanical Engineering 70, no. 11-12 (2024): 554–68. http://dx.doi.org/10.5545/sv-jme.2023.900.

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Efficient surface defects classification is one of the research hotpots in steel plate defect recognition. Compared with traditional methods, deep learning methods have been effective in improving classification accuracy and efficiency, but require a large amount of labeled data, resulting in limited improvement of detection efficiency. To reduce the labeling effort under the premise of satisfying the classification accuracy, a deep active learning method is proposed for steel plate surface defects classification. Firstly, a lightweight convolutional neural network is designed, which speeds up

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12

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. The

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Su, Weihao, Yutu Yang, Chenxin Zhou, Zilong Zhuang, and Ying Liu. "Multiple Defect Classification Method for Green Plum Surfaces Based on Vision Transformer." Forests 14, no. 7 (2023): 1323. http://dx.doi.org/10.3390/f14071323.

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Green plums have produced significant economic benefits because of their nutritional and medicinal value. However, green plums are affected by factors such as plant diseases and insect pests during their growth, picking, transportation, and storage, which seriously affect the quality of green plums and their products, reducing their economic and nutritional value. At present, in the detection of green plum defects, some researchers have applied deep learning to identify their surface defects. However, the recognition rate is not high, the types of defects identified are singular, and the class

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Liu, Jinxin, and Kexin Li. "Intelligent Metal Welding Defect Detection Model on Improved FAST-PNN." Coatings 12, no. 10 (2022): 1523. http://dx.doi.org/10.3390/coatings12101523.

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In order to solve the problem of accurate and efficient detection of welding defects in the process of batch welding of metal parts, an improved Probabilistic Neural Network (PNN) algorithm was proposed to build an automatic identification model of welding defects. Combined with the characteristics of the PNN model, the structure and algorithm flow of the FAST-PNN algorithm model are proposed. Extraction of welding defect image texture features of metal welded parts by a Gray Level Co-occurrence Matrix (GLCM) screens out the characteristic indicators that can effectively characterize welding d

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15

Altmann, Mika León, Thiemo Benthien, Nils Ellendt, and Anastasiya Toenjes. "Defect Classification for Additive Manufacturing with Machine Learning." Materials 16, no. 18 (2023): 6242. http://dx.doi.org/10.3390/ma16186242.

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Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies depending on the specific defect type, its size, and morphology. Furthermore, a single component may exhibit various defect types due to the manufacturing process. To investigate these occurrences with regard to other target variables, this study prese

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16

Sika, Robert, Michał Rogalewicz, Paweł Popielarski, et al. "Decision Support System in the Field of Defects Assessment in the Metal Matrix Composites Castings." Materials 13, no. 16 (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 c

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17

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 characterist

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18

Han, Ying, Xingkun Li, Gongxiang Cui, Jie Song, Fengyu Zhou, and Yugang Wang. "Multi-defect detection and classification for aluminum alloys with enhanced YOLOv8." PLOS ONE 20, no. 3 (2025): e0316817. https://doi.org/10.1371/journal.pone.0316817.

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With the increasing application of aluminum alloys in the industrial field, the defect of aluminum alloys significantly impacts the structural integrity and safety of products. However, state-of-the-art material defect detection methods have low detection accuracy and inaccurate defect target frame problems. Therefore, an enhanced YOLOv8-ALGP (aluminum, Ghost, P2) defect detection and classification method for 13 defects is proposed in this paper. Firstly, based on the AliCloud Tianchi dataset, 3 defects are added and an enhancement strategy is implemented to increase the diversity of the trai

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19

Kerres, Karsten, Sylvia Gredigk-Hoffmann, Rüdiger Jathe, et al. "Future approaches for sewer system condition assessment." Water Practice and Technology 15, no. 2 (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 s

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20

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 u

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21

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 un

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22

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 (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 ve

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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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Ivanov, P. S., O. F. Lesun, M. M. Bukin, et al. "CLASSIFICATION OF RAIL DEFECTS." Science and Transport Progress, no. 8 (September 25, 2005): 156–60. http://dx.doi.org/10.15802/stp2005/20185.

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An, Jinke, Li Yang, Zhongyu Hao, Gongfa Chen, and Longjian Li. "Investigation on road underground defect classification and localization based on ground penetrating radar and Swin transformer." International Journal for Simulation and Multidisciplinary Design Optimization 15 (2024): 7. http://dx.doi.org/10.1051/smdo/2023023.

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In response to the low detection efficiency and accuracy of traditional manual methods for detecting road underground defects, this paper proposes an intelligent detection method based on ground penetrating radar (GPR). This method integrates the detection, classification, and localization of road underground defects. The approach uses Swin Transformer as a feature extraction network and utilizes the YOLOX object detection algorithm as a road underground defect detection model. It enables the detection of defect regions in three types of defect images: voids, non-compact areas, and underground

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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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27

Odion, Philip O., and Kabiru B. Ishola. "Multi-Class Road Defects Detection and Classification System Using Transfer Learning-Based Deep Convolutional Neural Networks." International Journal of Science for Global Sustainability 10, no. 2 (2024): 102–14. http://dx.doi.org/10.57233/ijsgs.v10i2.653.

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The road’s infrastructure is crucial for growth, development, and forming the backbone of any country's economy. The accurate detection and classification of road defects for optimal road maintenance is a challenging task due to the varied types of road defects of different severity. This paper presents a transfer learning model for the detection and classification of road defects based on types of defects (cracks and potholes) and severity. A new local dataset was introduced consisting of road surface images (defects and non-defects) of Kaduna metropolis, Nigeria. The types and severity of th

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Han, Wan Jiang, Sun Yi, Li Yan, et al. "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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Sharma, Mansi, Jongtae Lim, and Hansung Lee. "The Amalgamation of the Object Detection and Semantic Segmentation for Steel Surface Defect Detection." Applied Sciences 12, no. 12 (2022): 6004. http://dx.doi.org/10.3390/app12126004.

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Steel surface defect detection is challenging because it contains various atypical defects. Many studies have attempted to detect metal surface defects using deep learning and had success in applying deep learning. Despite many previous studies to solve the steel surface defect detection, it remains a difficult problem. To resolve the atypical defects problem, we introduce a hierarchical approach for the classification and detection of defects on the steel surface. The proposed approach has a hierarchical structure of the binary classifier at the first stage and the object detection and semant

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Liu, Zixi, Zhengliang Hu, Longxiang Wang, et al. "Effective detection of metal surface defects based on double-line laser ultrasonic with convolutional neural networks." Modern Physics Letters B 35, no. 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

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Chen, Yuan, Shaonan Liang, Zhongyang Wang, et al. "Automatic Classification of Weld Defects From Ultrasonic Signals Using WPEE-KPCA Feature Extraction and an ABC-SVM Approach." Insight - Non-Destructive Testing and Condition Monitoring 65, no. 5 (2023): 262–69. http://dx.doi.org/10.1784/insi.2023.65.5.262.

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The classification of weld defects is very important for the safety assessment of welded structures and feature extraction of ultrasonic defect signals is vital for defect classification. A novel approach based on wavelet packet energy entropy (WPEE) and kernel principal component analysis (KPCA) feature extraction and an artificial bee colony optimisation support vector machine (ABC-SVM) classifier is proposed in this paper. Firstly, the WPEE method is adopted to extract ultrasonic signal features of weld defects and KPCA is used for feature selection. Secondly, an ABC-SVM classifier is emplo

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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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Piao, Guanyu, Jiatong Ling, and Jiaoyang Li. "Classification and characterization of coexisting defects from magnetic flux leakage data using deep learning method." AIP Advances 13, no. 1 (2023): 015029. http://dx.doi.org/10.1063/9.0000451.

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Ferromagnetic materials are widely used in infrastructure, such as steam generators, storage tanks, and gas pipelines. During their service time, ferromagnetic materials are subject to deterioration and defects are prone to generate which could damage infrastructures and cause catastrophic accidents. Magnetic flux leakage (MFL) is one of the widely used nondestructive evaluation (NDE) methods to detect and characterize defects in ferromagnetic materials to ensure infrastructure safety. However, many research works have been carried out on the modeling, classification, and characterization of a

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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 (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 be

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Shaprynskyi, Ye V., D. V. Myrhorodskyi, and D. V. Mikhurinskyi. "COMPARATIVE ANALYSIS OF EXISTING CLASSIFICATIONS OF SOFT TISSUE DEFECTS OF THE LOWER EXTREMITIES FOR OPTIMAL METHODS OF THEIR CLOSURE." Kharkiv Surgical School, no. 1 (March 20, 2024): 9–14. http://dx.doi.org/10.37699/2308-7005.1.2024.02.

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Abstract. Aim. To compare the existing classifications of soft tissue defects of the lower extremities and analyze them taking into account metric characteristics, localization of defects and the degree of tissue change in the area of defects. Materials and methods. During the two-year period of the war, a great experience has been gained in the treatment of soft tissue defects of different structures, localization, sizes, and volume, especially of the lower extremities, since this is the predominant injury. The great variety of defects made it necessary to revise their classifications and cal

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Lopes, Fábio, João Agnelo, A. Teixeira César, Nuno Laranjeiro, and Jorge Bernardino. "Automating orthogonal defect classification using machine learning algorithms." Future Generation Computer Systems 102 (January 31, 2020): 932–47. https://doi.org/10.1016/j.future.2019.09.009.

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Software systems are increasingly being used in business or mission critical scenarios, where the presence of certain types of software defects, i.e., bugs, may result in catastrophic consequences (e.g., financial losses or even the loss of human lives). To deploy systems in which we can rely on, it is vital to understand the types of defects that tend to affect such systems. This allows developers to take proper action, such as adapting the development process or redirecting testing efforts (e.g., using a certain set of testing techniques, or focusing on certain parts of the system). Orthogon

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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 (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 dis

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Wang, Haibin, Zhichao Fan, Xuedong Chen, et al. "Automated Classification of Pipeline Defects from Ultrasonic Phased Array Total Focusing Method Imaging." Energies 15, no. 21 (2022): 8272. http://dx.doi.org/10.3390/en15218272.

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The defects in the welds of energy pipelines have significantly influenced their safe operation. The inefficient and inaccurate detection of the defects may give rise to catastrophic accidents. Ultrasonic phased array inspection is an important means of ensuring pipeline safety. The total focusing method (TFM), using ultrasonic phased arrays, has become widely used in recent years in non-destructive evaluation (NDE). However, manual defect recognition of TFM images is seen to lack accuracy and robustness, arising from deficiency of practical experience. In this paper, the automated classificat

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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 (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

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Li, Yanfeng, Pengyu Gao, Yongbiao Luo, et al. "Automatic Detection and Classification of Natural Weld Defects Using Alternating Magneto-Optical Imaging and ResNet50." Sensors 24, no. 23 (2024): 7649. https://doi.org/10.3390/s24237649.

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It is difficult to detect and identify natural defects in welded components. To solve this problem, according to the Faraday magneto-optical (MO) effect, a nondestructive testing system for MO imaging, excited by an alternating magnetic field, is established. For the acquired MO images of crack, pit, lack of penetration, gas pore, and no defect, Gaussian filtering, bilateral filtering, and median filtering are applied for image preprocessing. The effectiveness of these filtering methods is evaluated using metrics such as peak signal–noise ratio (PSNR) and mean squared error. Principal componen

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Li, Zhong, Chen Wu, Qi Han, Mingyang Hou, Guorong Chen, and Tengfei Weng. "CASI-Net: A Novel and Effect Steel Surface Defect Classification Method Based on Coordinate Attention and Self-Interaction Mechanism." Mathematics 10, no. 6 (2022): 963. http://dx.doi.org/10.3390/math10060963.

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The surface defects of a hot-rolled strip will adversely affect the appearance and quality of industrial products. Therefore, the timely identification of hot-rolled strip surface defects is of great significance. In order to improve the efficiency and accuracy of surface defect detection, a lightweight network based on coordinate attention and self-interaction (CASI-Net), which integrates channel domain, spatial information, and a self-interaction module, is proposed to automatically identify six kinds of hot-rolled steel strip surface defects. In this paper, we use coordinate attention to em

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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 (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 proje

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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 (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)

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Antonova, I. V., O. V. Antonov, and D. V. Listkova. "STATE AND PROBLEMS OF THE SYSTEM FOR REGISTRATION AND ACCOUNTING OF CONGENITAL DEVELOPMENTAL DEFECTS IN CHILDREN AND FETUSES." Transbaikalian Medical Bulletin, no. 4 (February 25, 2025): 54–63. https://doi.org/10.52485/19986173_2024_4_54.

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The literature review presents an analysis of the current state of the existing system for monitoring congenital malformations in children and fetuses. The birth rate of children with developmental defects in different countries and on the territory of the Russian Federation varies over a wide range, both over time and across observation areas. Using the example of defects and anomalies of the urinary system organs, the issues of defining the terms “congenital defect” and «developmental anomaly» are discussed. The problem remains that researchers have used various classifications over the year

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Jie, Luyang, Yilong Guo, Yiming Yao, and Yongkang Liu. "Unsupervised Wafer Defect Classification Model Based On Joint Reconstruction And Clustering." Advances in Engineering Technology Research 9, no. 1 (2023): 182. http://dx.doi.org/10.56028/aetr.9.1.182.2024.

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Defect detection of unpatterned wafer is very important for determining the causes of wafer defects, and it is also a significant way to improve production yield. At present, the defect detection model based on the deep learning method has been widely used and has shown promising performance. However, the labor cost of supervised learning method based on labeled samples is very expensive, so the wafer defect detection model based on unsupervised learning is a future research direction. In this paper, we propose an unsupervised wafer defect classification model(UWDDM), in which the reconstructi

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Mathew, Dennise, and N. C. Brintha. "Deep-GD: Deep Learning based Automatic Garment Defect Detection and Type Classification." International Journal of Electrical and Electronics Research 12, no. 1 (2024): 41–47. http://dx.doi.org/10.37391/ijeer.120107.

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Garment defect detection has been successfully implemented in the quality quick response system for textile manufacturing automation. Defects in the production of textiles waste a lot of resources and reduce the quality of the finished goods. It is challenging to detect garment defects automatically because of the complexity of images and variety of patterns in textiles. This study presented a novel deep learning-based Garment defect detection framework named as Deep-GD model for sequentially identifying image defects in patterned garments and classify the defect types. Initially, the images a

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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 (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

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Kumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (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

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Jiang, Zhaolin, Xueyuan Hu, and Sunxin Wang. "Image Classification of Car Paint Defect Detection Based on Convolutional Neural Networks." Journal of Physics: Conference Series 2456, no. 1 (2023): 012037. http://dx.doi.org/10.1088/1742-6596/2456/1/012037.

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Abstract In the study of using images to display car paint defects, the current need is to use deep Convolutional Neural Networks (CNN) to identify and classify different types of car paint defects, so as to give full play to the application of image processing in the field of automatic car paint defect detection. Using the collected car paint defect images, the car paint defects dataset is established. The preprocessing process of original data and the application of three image classification models based on CNN are visually displayed. First, the dataset of 7 types of car body defects includ

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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) des

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