A convolutional neural network model for marble quality classification

Karaali, İdris; Eminağaoğlu, Mete

doi:10.1007/s42452-020-03520-5

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The correlation between the quality of the nut and the tapping process was analyzed by using various regression trees and learning methods. Karaali [ 38 ] proposed to use convolution neural network model for multiclassification of marble quality. Manimala [ 39 ] proposed a data selection method based on fuzzy c-means clustering for power quality event classification.…”

Section: Related Workmentioning

confidence: 99%

Product Processing Quality Classification Model for Small-Sample and Imbalanced Data Environment

Liu

Dai

2022

Computational Intelligence and Neuroscience

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With the rapid development of machine learning technology, how to use machine learning technology to empower the manufacturing industry has become a research hotspot. In order to solve the problem of product quality classification in a small sample data and imbalanced data environment, this paper proposes a data generation model called MSMOTE-GAN, which is based on Mahalanobis Synthetic Minority Oversampling Technology (MSMOTE) and Generative Adversarial Network (GAN). Among them, MSMOTE is proposed to solve the problem of the sample biased to the majority class expanded by methods such as GAN in a sample imbalanced environment. Based on the traditional SMOTE method, the sample distance measurement method is modified from Euclidean distance to Mahalanobis distance, taking into account the correlation between attributes and the influence of dimensions on the sample distance. In the data generation model, MSMOTE is used to balance the positive and negative samples in the data. GAN generates fake data with the same distribution as the original data based on a balanced data set and expands the sample size to solve the problems of overfitting and insufficient model expression ability that occur when the sample size is too small. The quality classification framework of water heater liner based on the data generation model and Random Forest is constructed, and the process of the quality classification of water heater liner under the environment of small sample data and imbalanced data is fully described. This paper compares the MSMOTE-GAN model, Bootstrap, and tableGAN on the water heater liner production line data set and the public data set. The experimental result shows that the expanded data set of the MSMOTE-GAN model can effectively improve the performance of the classification model.

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Section: Related Workmentioning

confidence: 99%

Product Processing Quality Classification Model for Small-Sample and Imbalanced Data Environment

Liu

Dai

2022

Computational Intelligence and Neuroscience

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“…The authors of Ref. 8 have implemented there own CNN architecture and good bring out an accuracy of around 96.1% in marble quality classification. The authors of Ref.…”

Section: Previous Workmentioning

confidence: 99%

Granite classification using machine learning and edge computing

Karanam

Nagaraju

et al. 2022

F1000Res

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Background: The outlook and the aura of any place are highly dependent on how a place is decorated and what materials are used in designing it. Granite is such a kind of rock which is vastly used for this purpose. Granite flooring and counters have a major influence on the interior d ́ecor which is essential to set the mood and ambience of a house. A system is needed to help the end users differentiate between granites, which enhance the grandeur of their house and also check the frauds of different color granite being sent by the merchant as compared to what was selected by the end user. Several models have been developed for this cause using CNN and other image processing techniques. However, a solution for this purpose must be precise and computationally efficient. Methods: For this purpose,researchers in this work developed a machine learning based granite classifier using Edge Computing and a website to help users in choosing which granite would go well with their d ́ecor is also built. The developed system consists of a color sensor [TCS3200] integrated with an ESP8266 board. The data pertaining to RGB contrasts of different rocks is acquired by using the color sensor from a dealership.This data is used to train a Machine Learning algorithm to classify the rock into different granite types from a granite dealer and yield the category prediction. Results: The proposed system yields a result of 94% accuracy when classified using Random Forest Algorithm. Conclusion: Thus, this system provides an upper hand for the end users in differentiating between different types of granites.

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“…Derin öğrenme ile belirtilen alanlarda; nesne algılama, yüz tanıma, görüntü rekonstrüksiyonu, makine çevirisi, görüntü açıklaması, kötü amaçlı yazılım algılama, spam algılama, bilgisayar destekli görüntü yorumlama ve analizi uygulamaları gerçekleştirilmektedir. Özellikle bilgisayarlı görü ve görüntü işlemede CNN mimarisine sahip algoritmalar ile daha iyi performans ve başarılı sonuçlar elde edilmektedir [41,42].…”

Section: Derin öğRenme (Deep Learning)unclassified

Toz yatak füzyon birleştirme eklemeli imalatta kusur tespiti için öğrenme aktarımı kullanan derin öğrenme tabanlı bir yaklaşım

Duman¹,

Özsoy

2021

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Günümüzde toz yatak füzyon birleştirme (TYB) metal eklemeli imalat, karmaşık geometrili parça imalatında sıklıkla tercih edilmesine rağmen, parça imalat süreçlerinin gerçek zamanlı izlenmesi yeterli düzeyde değildir. Bu nedenle makine kontrol sistemi büyük ölçüde açık döngü olarak kalmaktadır. Bazı metal eklemeli imalat makineleri toz yatağının izlenmesini görüntülerle sunarken, toz yatağı katmanında oluşabilecek kusurların otomatik tespiti ve kontrol sistemini uyarıcı yeteneğinin olduğuna rastlanmamıştır. Çalışmada, herhangi bir TYB metal eklemeli imalat makinesinde gerçek zamanlı kontrol sisteminin bir bileşeni olma potansiyeline sahip toz yatağı görüntülerinin yerinde izlenmesi ve kusurların tespiti için makine öğrenmesi temelli örnek bir yaklaşım sunulmuştur. Makine öğrenmesinin alt alanlarından olan derin öğrenme yöntemi kullanılarak, işlemin bir katmanının oluşturulmasında meydana gelebilecek kusurları tespitine yönelik sınıflandırma yapılmıştır. Kusurları algılama ve sınıflandırma işlemi evrişimli sinir ağları modeli kullanılarak yerine getirilmiştir. Modelin eğitimi ve performansı için veri seti, EOS M290 makinesinde imal edilmiş örnek bir üç boyutlu yapının fotoğrafları ile oluşturulmuştur. VGG-16, InceptionV3 ve DenseNet ön öğrenmeli modellerinden transfer öğrenimi yapılarak en iyi performans %86 doğruluk değeri ile VGG-16 modelinde elde edilmiştir.

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A convolutional neural network model for marble quality classification

Cited by 6 publications

References 15 publications

Product Processing Quality Classification Model for Small-Sample and Imbalanced Data Environment

Product Processing Quality Classification Model for Small-Sample and Imbalanced Data Environment

Granite classification using machine learning and edge computing

Toz yatak füzyon birleştirme eklemeli imalatta kusur tespiti için öğrenme aktarımı kullanan derin öğrenme tabanlı bir yaklaşım

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