Search Results - defect classification technique

Feature extraction and selection for defect classification of pulsed eddy current NDT by Chen, Tianlu, Tian, Gui Yun, Sophian, Ali, Que, Pei Wen

“…Various feature selection and integrations are proposed for defect classification. Experimental studies, including blind tests, show the validation of the new features and combination of selected features in defect classification. …”
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Neural network paradigm for classification of defects on PCB by Heriansyah, Rudi, Syed Al-Attas, Syed Abdul Rahman, Zabidi, Muhammad Mun'im Ahmad

“…A defective PCB image is used to ensure the function of the proposed technique.…”
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Software defect prediction framework based on hybrid metaheuristic optimization methods by Wahono, Romi Satria

“…The classification algorithm is a popular machine learning approach for software defect prediction. …”
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An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images by Ibrahim, I., Ibrahim, Z., Khalil, K., Mokji, M.M., Abu Bakar, S.A.R.S., Mokhtar, N., Ahmad, W.K.W.

“…The defect classification algorithm is improved by incorporating proper image registration and thresholding techniques to solve the alignment and uneven illumination problem. …”
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Evaluation Of Texture Feature Based On Basic Local Binary Pattern For Wood Defect Classification by Ibrahim, Eihab Abdelkariem Bashir, Hashim, Ummi Rabaah, Salahuddin, Lizawati, Ismail, Nor Haslinda, Ngo, Hea Choon, Kanchymalay, Kasturi, Zabri @ Suhaimi, Siti Normi

“…It indicates that the proposed technique works moderately well to classify wood defects. …”
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Automated mold defects classification in paintings: a comparison of machine learning and rule-based techniques. by Mohamad Hilman, Nordin *, Bushroa, Abdul Razak, Norrima, Mokhtar, Mohd Fadzil, Jamaludin, Adeel, Mehmood

“…Subsequently, these regions are classified as mold defects using either morphological filtering or machine learning models such as Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). …”
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Mold defects detection in painting using artificial intelligence / Mohamad Hilman Nordin by Mohamad Hilman , Nordin

“…The third technique is a machine learning classification that utilizes the feature extraction strength of the DLT. …”
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Classification Analysis Of High Frequency Stress Wave For Autonomous Detection Of Defect In Steel Tubes by Abd Halim, Zakiah, Jamaludin, Nordin, Junaidi, Syarif, Syed Yahya, Syed Yusaini

“…Interpretation of propagated high frequency stress wave signals in steel tubes is noteworthy for defect identification.This paper demonstrated a successful new approach for autonomous defect detection in steel tubes using classification analysis of high frequency stress waves.Classification analysis using Principal Component Analysis (PCA) algorithm involved feature extraction to reduce the dimensionality of the complex stress waves propagation path.Two defective tubes containing a slot defect of different orientation and a reference tube are inspected using Vibration Impact Acoustic Emission (VIAE) technique.The tubes are externally excited using impact hammer.The variation of stress wave transmission path are captured by high frequency Acoustic Emission sensor.The propagated stress waves in the steel tubes are classified using PCA algorithm.Classification results are graphically illustrated using a dendrogram that demonstrated the arrangement of the natural clusters of the stress wave signals.The inspection of steel tubes showed good recognition of defect in circumferential and longitudinal orientation.This approach successfully classified stress wave signals from VIAE testing and provide fast and accurate defect identification of defective steel tubes from non-defective tubes. …”
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Identification Of Wood Defect Using Pattern Recognition Technique by Teo, Hong Chun, Ahmad, Sabrina, Hashim, Ummi Rabaah, Ngo, Hea Choon, Kanchymalay, Kasturi, Ismail, Nor Haslinda, Salahuddin, Lizawati

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Internal defect detection and reconstruction framework for laminated glass fibre reinforced polymer composite materials by Ng, Sok Choo

“…Therefore, by removmg the low frequency signals, the intemal defect detectability can be improved. Moreover, the classification of an input pattem based on the closest neighbours of the point of interest provides more accurate defect detection in comparison with the classification based on experience data as the defect pattems vary on circumstances in ultrasonic NDE problems.…”
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Noise reduction techniques for partial discharge classification in XPLE cable joint / Noratika Mohd Zin by Noratika, Mohd Zin

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An attention-augmented convolutional neural network with focal loss for mixed-type wafer defect classification by Batool, Uzma, Mohd Ibrahim, Shapiai, Mostafa, Salama A., Mohd Zamri, Ibrahim

“…Although deep learning methods have been successful in single-defect wafer classification, the increasing complexity of the fabrication process has introduced the challenge of multiple defects on wafers, which requires more robust feature learning and classification techniques. …”
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Vision Based Identification And Classification Of Weld Defects In Welding Environment: A Review by Hairol Nizam, Mohd Shah, Mohd Zamzuri, Ab Rashid, Marizan, Sulaiman, Ahmad Zaki, Shukor

“…This paper is a review for the identification and classification of weld defects in welding environments based on vision. …”
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Electroluminescence Images for Solar Cell Fault Detection Using Deep Learning for Binary and Multiclass Classification by Almashhadani R.A.I., Hock G.C., Nordin F.H.B., Abdulrazzak H.N.

“…They adopted two classification strategies: binary classification (defective or non-defective) and multiclass classification; the class names are 0%, 33%, 67%, and 100% (here, % represents the percentage of defectiveness), which represents the defect likelihood. …”

Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques by Mansour, Diaa-Eldin A., Taha, Ibrahim B.m., Farade, Rizwan A., Abdul Wahab, Noor Izzri

“…The PD diagnosis of various defect types is implemented using five optimized machine learning classification techniques: decision tree classification, ensemble methods, k-nearest neighbouring, Discriminant analysis, and Naïve Bayes classification. …”
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The formulation of a transfer learning pipeline for the classification of the wafer defects by Lim, Shi Xuen

“…Thus far, there are still limited studies that investigate the classification of wafer defects using TL combined with a classical Machine learning (ML) pipeline. …”
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Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects by Zuwairie, Ibrahim, Tan, Shing Chiang, Watada, Junzo, Marzuki, Khalid

“…Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. …”
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Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models by Mohd Yazed, Muhammad Syukri, Mohd Yunus, Mohd Amin, Ahmad Shaubari, Ezak Fadzrin, Abdul Hamid, Nor Aziati, Amzah, Azmale, Md Ali, Zulhelmi

“…The results demonstrate that these models achieve high precision in defect classification and detection of defects by more than 80%. …”
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A feature extraction technique based on principal component analysis for pulsed Eddy current {NDT} by Sophian, Ali, Tian, Gui Yun, Taylor, David, Rudlin, John

“…A comparative test carried out shows that the introduced technique has performed better than the conventional technique in the classification of defects. …”
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Wood defect detection and classification using deep learning / Yap Yi Ren by Yap, Yi Ren

“…To reduce the human mistakes, this study focuses on designing a wood defect detection and classification by using the artificial intelligence technique of Convolutional Neural Network (CNN) in MATLAB. …”
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