On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

Ma, Yue; Xu, Yongde; Yan, Hui; Zhang, Guozheng

doi:10.1177/0967033521999745

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…e average image of all the learned templates is the displayed template. When the number of learned template images reaches the specified number, the learning is stopped, and the learning process of the template images ends at this time [19,20].…”

Section: Algorithm Processmentioning

confidence: 99%

Integration of Artistic Visual Elements in Packaging Design Based upon Pattern Recognition Technology

2022

Mobile Information Systems

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Under the development trend of today’s society, any brand attaches great importance to its outer packaging, and the research and analysis of the design effect of outer packaging has always troubled many brands. The purpose of this paper is to conduct a comprehensive study on the integration of artistic visual elements in the design effect of product packaging through the use of pattern recognition technology. Therefore, this paper proposes an improvement to the feature analysis algorithm of image recognition in pattern recognition technology and understands the specific principle of pattern recognition and improves its specific recognition method. At the same time, in the experiment and analysis part, this paper conducts research experiments on the improved algorithm and explores the performance of the algorithm and the adaptability of the algorithm in the actual packaging design recognition process. The experimental results of this paper show that the improved pattern recognition improves the accuracy of text recognition in packaging by 18% and the accuracy of image noise point recognition by nearly 30%. It can be detected very efficiently in the artistic visual fusion process of packaging design.

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Section: Algorithm Processmentioning

confidence: 99%

Integration of Artistic Visual Elements in Packaging Design Based upon Pattern Recognition Technology

2022

Mobile Information Systems

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“…These techniques include magnetic resonance imaging (MRI) [8], X-ray imaging [9], hyper-spectral imaging (HSI) [10,11], near-infrared (NIR) spectroscopy [12], and image recognition [13]. Among these, NIR is frequently used for sex determination based on differences in morphology, gonadal traits such as eggs, and water content between male and female pupae [14][15][16][17][18]. Having addressed sericulture challenges in areas like Shandong, China, Zhu et al developed an NIR system with 97.5% accuracy and a sorting rate of 7.7 pupae per second [19].…”

Section: Introductionmentioning

confidence: 99%

Integrated Analysis of Machine Learning and Deep Learning in Silkworm Pupae (Bombyx mori) Species and Sex Identification

He,

Zhu,

Shen

et al. 2023

Animals

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Hybrid pairing of the corresponding silkworm species is a pivotal link in sericulture, ensuring egg quality and directly influencing silk quantity and quality. Considering the potential of image recognition and the impact of varying pupal postures, this study used machine learning and deep learning for global modeling to identify pupae species and sex separately or simultaneously. The performance of traditional feature-based approaches, deep learning feature-based approaches, and their fusion approaches were compared. First, 3600 images of the back, abdomen, and side postures of 5 species of male and female pupae were captured. Next, six traditional descriptors, including the histogram of oriented gradients (HOG), and six deep learning descriptors, including ConvNeXt-S, were utilized to extract significant species and sex features. Finally, classification models were constructed using the multilayer perceptron (MLP), support vector machine, and random forest. The results indicate that the {HOG + ConvNeXt-S + MLP} model excelled, achieving 99.09% accuracy for separate species and sex recognition and 98.40% for simultaneous recognition, with precision–recall and receiver operating characteristic curves ranging from 0.984 to 1.0 and 0.996 to 1.0, respectively. In conclusion, it can capture subtle distinctions between pupal species and sexes and shows promise for extensive application in sericulture.

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“…Line loss identification work as an important link in line loss management, can effectively guarantee the normal operation of the power grid lines. By effectively identifying the area of line loss in the power grid, the aging line can be replaced or repaired in a timely manner, thus improving the production efficiency of power grid operation and providing protection for the good operation of power supply enterprises [1]. At this stage, there are mainly two means for the identification of power grid line loss, one is the conventional identification method based on manual determination; the other is the intelligent identification method based on mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Intelligent identification of segmental line losses in low-voltage networks under graphical consistency line variation relationship analysis

Li,

Zhao,

Wei

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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The current conventional line loss identification method mainly constructs the similarity matrix by calculating the similarity of line loss data, which leads to poor identification effect due to the lack of effective extraction of line loss data features. In this regard, the intelligent identification method of low-voltage grid segmentation under the analysis of line variation relationship of mapping consistency is proposed. The similarity of voltage time series mapping of low-voltage power grid is calculated to extract line loss feature data, and the EM algorithm is used to cluster and analyze the power operation data of line loss abnormal stations, and the vector features of line loss data under different Gaussian distribution states are calculated by constructing GMM model to realize line loss identification. In the experiments, the proposed method is verified for recognition accuracy. The experimental results show that the algorithm has a high recall rate and possesses a more desirable recognition accuracy when the proposed method is used for line loss data recognition.

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On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

Cited by 9 publications

References 24 publications

Integration of Artistic Visual Elements in Packaging Design Based upon Pattern Recognition Technology

Integration of Artistic Visual Elements in Packaging Design Based upon Pattern Recognition Technology

Integrated Analysis of Machine Learning and Deep Learning in Silkworm Pupae (Bombyx mori) Species and Sex Identification

Intelligent identification of segmental line losses in low-voltage networks under graphical consistency line variation relationship analysis

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