Automatic Pearl Classification Machine Based on a Multistream Convolutional Neural Network

Xuan, Qi; Fang, Binwei; Liu, Yi; Wang, Jinbao; Zhang, Jian; Zheng, Yayu; Bao, Guanjun

doi:10.1109/tie.2017.2784394

Cited by 81 publications

(33 citation statements)

References 25 publications

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“…As concluded from Table 3 , when the p value increases to 0.09 and 0.12, the accuracy of network prediction will decline to a very low level; however, the prediction accuracy of MS-CNN model constructed in this study can reach 96% when the p value is 0.12; the prediction accuracy drops by only 3.3% compared with the prediction accuracy when the labels are correct, which indicates that the MS-CNN model constructed in this study effectively reduces the negative impact of commodity image labeling errors and effectively improves the robustness of CIR, which is consistent with the research results of Xuan et al (2017) [ 27 ].…”

Section: Resultssupporting

confidence: 90%

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

2020

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In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology.

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Section: Resultssupporting

confidence: 90%

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

2020

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“…Extensive reviews can be found in work of Kadlec [2]. Among these methods, multivariate static techniques [3][4][5][6] have been widely used. However, these algorithms are relatively sensitive to measurement noise and commonly require a large number of samples to build the promising so sensor as well.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Hybrid Local Kernel Regression for Soft Sensor Modelling of Rubber-Mixing Process

et al. 2020

Advances in Polymer Technology

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Soft sensor techniques have been widely adopted in chemical industry to estimate important indices that cannot be online measured by hardware sensors. Unfortunately, due to the instinct time-variation, the small-sample condition and the uncertainty caused by the drifting of raw materials, it is exceedingly difficult to model the fed-batch processes, for instance, rubber internal mixing processing. Meanwhile, traditional global learning algorithms suffer from the outdated samples while online learning algorithms lack practicality since too many labelled samples of current batch are required to build the soft sensor. In this paper, semi-supervised hybrid local kernel regression (SHLKR) is presented to leverage both historical and online samples to semi-supervised model the soft sensor using proposed time-windows series. Moreover, the recursive formulas are deduced to improve its adaptability and feasibility. Additionally, the rubber Mooney soft sensor of internal mixing processing is implemented using real onsite data to validate proposed method. Compared with classical algorithms, the performance of SHLKR is evaluated and the contribution of unlabelled samples is discussed.

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“…In such a situation, it is not suitable to directly apply SPC methods to flooding prognosis. Recent popular deep‐learning methods, such as deep brief networks , and convolutional neural networks , , often require a large amount of labeled data, which may not be directly applied to flooding prognosis. Recently, a degree of steadiness (DOS)‐based flooding prognosis strategy was proposed .…”

Section: Introductionmentioning

confidence: 99%

Dynamic Profile Monitoring for Flooding Prognosis in Packed Columns

et al. 2019

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In the chemical industry, real-time flooding prognosis is a necessity for packedcolumn operation because the flooding phenomenon interferes with the performance of production systems. In this work, the profile monitoring technique is utilized to capture the dynamic behavior of pressure drop, which is an important indicator for the flooding phenomenon. In each moving window, the pressure drop signals are described by using an exponential generalized autoregressive conditional heteroskedastic model. The onset of the flooding phenomenon is then indicated by changes in model parameters. Moreover, to efficiently capture the process change, a nonparametric approach is utilized to establish a statistical control chart. Experimental and comparison results show the advantages of the proposed method.

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Automatic Pearl Classification Machine Based on a Multistream Convolutional Neural Network

Cited by 81 publications

References 25 publications

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Semi-Supervised Hybrid Local Kernel Regression for Soft Sensor Modelling of Rubber-Mixing Process

Dynamic Profile Monitoring for Flooding Prognosis in Packed Columns

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