Improved deep residual shrinkage network on near infrared spectroscopy for tobacco qualitative analysis

Qin, Yuhua; Liu, Xinpeng; Zhang, Fengmei; Shan, Qiufu; Zhang, Min

doi:10.1016/j.infrared.2023.104575

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…This module effectively extracts useful features from noisy signals. The DRSN consists of the input layer, convolutional layer, stacked residual shrinkage building unit (RSBU), batch normalization (BN), activation function, global average pooling (GAP), and fully connected layer [21], as depicted in Figure 4. The DRSN offers several key advantages in terms of feature extraction in this model: (1).…”

Section: Deep Residual Shrinkage Network (Drsn)mentioning

confidence: 99%

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Liu,

Wang,

Zhang

et al. 2024

Symmetry

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Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data collection, data pre-processing, and model interpretation. In this context, a quality anomaly recognition and diagnosis model for the complex product manufacturing process is constructed based on a deep residual network, support vector machine (SVM), and Shapley additive explanation (SHAP). Given the numerous complex product quality characteristic indexes and unpredictable accidental factors in the production process, it is necessary to mine the deep relationship between quality characteristic data and quality state. This mining is achieved by utilizing the strong feature extraction ability of the deep residual shrinkage network (DRSN) through self-learning. The symmetry of the data within the model has also been taken into account to ensure a more balanced and comprehensive analysis. The excellent binary classification ability of the support vector machine is combined with the DRSN to identify the quality anomaly state. The SHAP interpretable model is employed to diagnose the quality anomaly problem of a single product and to identify and diagnose quality anomalies in the manufacturing process of complex products. The effectiveness of the model is validated through case analysis. The accuracy of the DRSN-SVM quality anomaly recognition model reaches 99%, as demonstrated by example analysis, and the model exhibits faster convergence and significantly higher accuracy compared with the naive Bayesian model classification and support vector machine classification models.

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Section: Deep Residual Shrinkage Network (Drsn)mentioning

confidence: 99%

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Liu,

Wang,

Zhang

et al. 2024

Symmetry

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“…Qin, Y. et al used an improved deep residual contraction network to optimize the accuracy of tobacco NIR spectra and used the Gram's Angle summation field to transform the tobacco spectra into a twodimensional image. Moreover, the attention mechanism was introduced into the network, which improved the network's attention to tobacco spectra and enhanced the accuracy of tobacco NIR qualitative analysis [11].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based intelligent control of moisture at the exit of blade charging process in cigarette production

Rui,

Qiu,

Hou

et al. 2024

Applied Mathematics and Nonlinear Sciences

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Currently, in the production of cigarettes in the blade, charging export moisture control means is relatively single and can not effectively guarantee the excellent quality of cigarette filament. In this paper, first of all, the working principle of the tobacco blade charging machine is introduced, and the moisture of the tobacco leaf for the charging machine is dynamically analyzed, and the influence of the return air temperature control of the charging machine on the export moisture of the blade charging process is explored. Secondly, based on the traditional PID controller, an adaptive fuzzy PID controller is established by combining adaptive fuzzy rules, and then the stacked noise-reducing self-encoder in deep learning is combined with the adaptive fuzzy PID control to design the intelligent control structure of export moisture of leaf charging process. Finally, the effectiveness of export moisture intelligence control, process capability index, and the effect before and after application were analyzed in controlled experiments, respectively. The results show that the difference between the predicted value and the real value of blade export moisture in this paper’s method is only 0.5%, and the process capability index of this paper’s method is improved by 1.48 compared with the PID controller, and it can control the temperature of the return air of the charging machine in the range of 56.86℃~57.21℃. The intelligent control method of export moisture introduced by deep learning can accurately control the export moisture of the leaf dosing process, which effectively ensures the quality of tobacco filament making.

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“…Hence, intelligent tobacco grading will be the trend in the future development of the tobacco industry. At present, the research on intelligent tobacco grading technology has achieved significant growth, mainly including three aspects: The first way is using infrared 12 and hyperspectral techniques, combined with stoichiometry to construct a classification model to achieve rapid and nondestructive tobacco classification 13 – 15 . However, the near-infrared equipment cost is high, and the spectrum it scans is more sensitive to environments (such as temperature and humidity), making classification results inaccurate.…”

Section: Introductionmentioning

confidence: 99%

“…(1) The first way is using infrared 12 and hyperspectral techniques, combined with stoichiometry to construct a classification model to achieve rapid and nondestructive tobacco classification [13][14][15] . However, the nearinfrared equipment cost is high, and the spectrum it scans is more sensitive to environments (such as temperature and humidity), making classification results inaccurate.…”

mentioning

confidence: 99%

Intelligent large-scale flue-cured tobacco grading based on deep densely convolutional network

Xin

Gong

et al. 2023

Sci Rep

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Flue-cured tobacco grading plays a crucial role in tobacco leaf purchase and the formulation of tobacco leaf groups. However, the traditional flue-cured tobacco grading mode is usually manual, which is time-consuming, laborious, and subjective. Hence, it is essential to research more efficient and intelligent flue-cured tobacco grading methods. Most existing methods suffer from the more classes less accuracy problem. Meanwhile, limited by different industry applications, the flue-cured tobacco datasets are hard to be obtained publicly. The existing methods employ relatively small and lower resolution tobacco data that are hard to apply in practice. Therefore, aiming at the insufficiency of feature extraction ability and the inadaptability to multiple flue-cured tobacco grades, we collected the largest and highest resolution dataset and proposed an efficient flue-cured tobacco grading method based on deep densely convolutional network (DenseNet). Diverging from other approaches, our method has a unique connectivity pattern of convolutional neural network that concatenates preceding tobacco feature data. This mode connects all previous layers to the subsequent layer directly for tobacco feature transmission. This idea can better extract depth tobacco image information features and transmit each layer’s data, thereby reducing the information loss and encouraging tobacco feature reuse. Then, we designed the whole data pre-processing process and experimented with traditional and deep learning algorithms to verify our dataset usability. The experimental results showed that DenseNet could be easily adapted by changing the output of the fully connected layers. With an accuracy of 0.997, significantly higher than the other intelligent tobacco grading methods, DenseNet came to the best model for solving our flue-cured tobacco grading problem.

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Improved deep residual shrinkage network on near infrared spectroscopy for tobacco qualitative analysis

Cited by 12 publications

References 21 publications

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Deep learning-based intelligent control of moisture at the exit of blade charging process in cigarette production

Intelligent large-scale flue-cured tobacco grading based on deep densely convolutional network

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