Modified UDP-Based Semi-supervised Learning for Fruit Internal Quality Detection

2012 ◽  
pp. 571-579
Author(s):  
Peiyi Zhu ◽  
Benlian Xu ◽  
Jue Gao
Keyword(s):  
Supervised Learning ◽  
Internal Quality ◽  
Quality Detection

scholarly journals Assembly Quality Detection Based on Class-Imbalanced Semi-Supervised Learning

2021 ◽  
Vol 11 (21) ◽  
pp. 10373
Author(s):  
Zichen Lu ◽  
Jiabin Jiang ◽  
Pin Cao ◽  
Yongying Yang
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Class Imbalance ◽  
Loss Functions ◽  
Quality Inspection ◽  
Data Annotation ◽  
Quality Detection ◽  
High Classification Accuracy ◽  
Total Data ◽  
The Mean

Due to the imperfect assembly process, the unqualified assembly of a missing gasket or lead seal will affect the product’s performance and possibly cause safety accidents. Machine vision method based on deep learning has been widely used in quality inspection. Semi-supervised learning (SSL) has been applied in training deep learning models to reduce the burden of data annotation. The dataset obtained from the production line tends to be class-imbalanced because the assemblies are qualified in most cases. However, most SSL methods suffer from lower performance in class-imbalanced datasets. Therefore, we propose a new semi-supervised algorithm that achieves high classification accuracy on the class-imbalanced assembly dataset with limited labeled data. Based on the mean teacher algorithm, the proposed algorithm uses certainty to select reliable teacher predictions for student learning dynamically, and loss functions are modified to improve the model’s robustness against class imbalance. Results show that when only 10% of the total data are labeled, and the imbalance rate is 5.3, the proposed method can improve the accuracy from 85.34% to 93.67% compared to supervised learning. When the amount of annotated data accounts for 20%, the accuracy can reach 98.83%.

scholarly journals Rapid Nondestructive Detection of Water Content and Granulation in Postharvest “Shatian” Pomelo Using Visible/Near-Infrared Spectroscopy

Biosensors ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 41
Author(s):  
Sai Xu ◽  
Huazhong Lu ◽  
Christopher Ference ◽  
Guangjun Qiu ◽  
Xin Liang
Keyword(s):  
Feature Extraction ◽  
Data Processing ◽  
Water Content ◽  
Near Infrared ◽  
Internal Quality ◽  
Nondestructive Detection ◽  
Information Mining ◽  
Quality Detection ◽  
Spectral Signal ◽  
Validation Set

Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small, thin-peeled fruit, though less so for large, thick-peeled fruit due to a weak spectral signal resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. “Shatian” pomelo is a traditional Chinese large, thick-peeled fruit, and granulation and water loss are two major internal quality factors that influence its storage quality. However, there is no efficient, nondestructive detection method for measuring these factors. Thus, the VIS/NIR spectral signal detection of 120 pomelo samples during storage was performed. Information mining (singular sample elimination, data processing, feature extraction) and modeling were performed in different ways to construct the optimal method for achieving an accurate detection. Our results showed that the water content of postharvest pomelo was optimally detected using the Savitzky–Golay method (SG) plus the multiplicative scatter correction method (MSC) for data processing, genetic algorithm (GA) for feature extraction, and partial least squares regression (PLSR) for modeling (the coefficient of determination and root mean squared error of the validation set were 0.712 and 0.0488, respectively). Granulation degree was best detected using SG for data processing and PLSR for modeling (the detection accuracy of the validation set was 100%). Additionally, our research showed a weak relationship between the pomelo water content and granulation degree, which provided a reference for the existing debates. Therefore, our results demonstrated that VIS/NIR combined with optimal information mining and modeling methodswas feasible for determining the water content and granulation degree of postharvest pomelo, and for providing references for the nondestructive internal quality detection of other large, thick-peeled fruits.

scholarly journals An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 80
Author(s):  
Sai Xu ◽  
Huazhong Lu ◽  
Christopher Ference ◽  
Qianqian Zhang
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Data Processing ◽  
Water Content ◽  
Information Fusion ◽  
Nir Spectroscopy ◽  
Internal Quality ◽  
Source Information ◽  
Accuracy Improvement ◽  
Quality Detection

The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling

An IoT Device Identification Method based on Semi-supervised Learning

2020 ◽  
Author(s):  
Linna Fan ◽  
Shize Zhang ◽  
Yichao Wu ◽  
Zhiliang Wang ◽  
Chenxin Duan ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Device Identification ◽  
Identification Method
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