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Research on national pattern innovation fusion and packaging design system based on efficient net image recognition and transfer learning algorithm

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GEOJOURNAL 2025年第2期90卷 1-18页

作者： Yuan, Weijing Ge, Yinglin Heilongjiang Inst Technol Coll Art & Design Harbin 150050 Peoples R China

In the context of globalization and digitalization, there are both opportunities and challenges for integrating ethnic patterns into packaging design, and traditional methods are inefficient and poor in creative integration due to the lack of advanced algorithms. In this study, the EfficientNet image recognition and transfer learning algorithms were used to construct a new packaging design system, and the accuracy of ethnic pattern recognition on a large-scale dataset exceeded 95%, far exceeding that of traditional algorithms. The system analyzes design needs, selects fusion patterns, and generates packaging designs with a modern aesthetic. Through transfer learning to achieve cross-cultural design innovation, the experimental display system can improve design efficiency by 30% while maintaining design innovation and cultural depth, effectively shortening the design cycle. This shows that the intelligent design system based on EfficientNet significantly improves the application effect of ethnic pattern packaging design, high recognition accuracy ensures accurate pattern application, cross-cultural innovation gives packaging culture inclusiveness, and efficiency improvement strongly supports the application of ethnic patterns in the field of modern design, promotes the development of national culture communication, and explores more technology integration in the future to continuously optimize system performance.

关键词： Efficient Net Image recognition transfer learning algorithm Traditional national culture

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Prediction of Cutting Force for Different Tools Based on transfer learning and Neural Networks

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2025年 1-20页

作者： Li, Zhengkang Ni, Chang Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Processing large and complex parts requires various tools, a number of which need to be replaced during the process. However, the variable dynamic characteristics of different cutting tools can significantly affect the accuracy of cutting force modeling and prediction. A data-driven approach utilizing neural networks and transfer learning is proposed for predicting milling forces across various tools. Initially, cutting data from milling experiments using different tools and machining parameters are collected to form a dataset. The source tool contains a comprehensive set of process parameters data, whereas the target tool includes a small number of labeled and test groups. Afterwards, the input data of the source and target tools are fed into an autoencoder with a maximum mean discrepancy loss function to reduce the marginal distribution discrepancy. Furthermore, affine transformation is performed to generate pseudo-labels, thereby augmenting the dataset and providing coarse data for the target tool. Finally, the TrAdaBoost.R2 algorithm is applied to establish the cutting force prediction model specific to the target tool. The training set of which includes a combination of pseudo-data and a small amount of target tool marked data, enabling accurate prediction of the cutting forces for the target tool's unlabeled data. Detailed experimental validation is performed on five-axis machine tools to verify the accuracy and effectiveness of the designed methodology. Comparison results show that prediction accuracy improved by more than 50%, 35%, and 65% compared with network trained directly with source domain data, models trained directly with TrAdaBoost.R2 algorithm, and network trained with small amounts of data from the target tool, respectively, which showcase the superiority of the proposed model.

关键词： Cutting force prediction Different cutting tools Neural network transfer learning algorithm

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Rolling bearing fault classification method based on data simulation collaborative drive and transfer learning 3

Rolling bearing fault classification method based on data si...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Xiaoyu, Tang Zihan, Wan Yiyang, Shen Nanjing Tech University Nanjing China

ISBN: (纸本)9798350341546

The simulation data is complete, but the measured data is unbalanced or missing, which makes it difficult to apply the data-driven model. In addition, there are many adjustable hyperparameters and artificially adjusted random factors in the data-driven model, which leads to the final result of the model. Insufficient precision. Therefore, this paper proposes a rolling bearing fault image recognition method based on hyperparameter optimization and transfer ***, build a simulation model to generate some samples, and then screen the network through noise resistance, select a network with strong robustness, and then input the network with strong noise resistance with simulation data. In this paper, AlexNet depth volume is selected. A neural network is integrated and the model is tuned through hyperparameter optimization techniques. Finally, this paper constructs a model by migrating the evaluation model from the simulation domain to the measured data domain through feature projection to make up for the scarcity of samples. It is verified by experiments that the actual measurement data of rolling bearing faults from Case Western Reserve University is used, and the overall accuracy of the model is improved by means of the hyperparameter optimization method. In addition, this paper also uses transfer learning and feature projection methods to solve the problem of scarcity of measured fault samples, and has a good transfer effect and high accuracy after transfer. The above results have verified the improvement effect and good noise immunity of the above method. © 2023 IEEE.

关键词： AlexNet neural network hyperparameter optimization rolling bearing transfer learning algorithm

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Aleaf: An Android-Based Phytotherapy Leaf Recognition Using Custom Vision Machine learning 7

Aleaf: An Android-Based Phytotherapy Leaf Recognition Using ...

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7th International Conference on Business and Industrial Research (ICBIR)

作者： Leonida, Althani Miguel G. Caballero, Arlene R. Lyceum Philippines Univ Coll Technol Manila Philippines

ISBN: (纸本)9781665494755

Phytotherapy is the area of medicine which deals with the practice of medicinal plants as remedies for illnesses or as therapeutic agents. Due to its budget-friendliness, high accessibility, and long account of effectivity, plant- based therapy as complementary and alternative medicine (CAM) is widespread in the first world while remains the primary health care for the third world. The more traditional use of plant-based therapy for medicinal purposes is to preserve the original properties of the plant;vitiated components are minimum. Even with its popularity, information regarding phytotherapy and the benefits of herbs and plants is nil. Even with its utilization among people, especially with the guidance of the elderlies, the knowledge is still lacking. This may be due to the fact that the practice of phytotherapy is usually based on experience and information is passed down through verbal transmission. The main objective of the study is to implement an Android application that acts as a leaf identification system, capable of detecting whether a plant has medicinal properties or not through the captured leaf image and its patterns. It displays their therapeutic benefits to the human body, its preparation, administration, dosage and frequency and duration of usage.

关键词： Phytotherapy Machine learning Custom Vision Leaf Recognition transfer learning algorithm

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Deep Analysis of EIT Dataset to Classify Apnea and Non-Apnea Cases in Neonatal Patients

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IEEE ACCESS 2021年 9卷 25131-25139页

作者： Vahabi, Nafiseh Yerworth, Rebecca Miedema, Martijn van Kaam, Anton Bayford, Richard Demosthenous, Andreas UCL Dept Elect & Elect Engn London WC1E 6BT England UCL Dept Med Phys & Biomed Engn London WC1E 6BT England Univ Amsterdam Emma Childrens Hosp Amsterdam UMC NL-1012 WX Amsterdam Netherlands Middlesex Univ Dept Nat Sci London NW4 4BT England

Electrical impedance tomography (EIT) is a non-invasive imaging modality that can provide information about dynamic volume changes in the lung. This type of image does not represent structural lung information but provides changes in regions over time. EIT raw datasets or boundary voltages are comprised of two components, termed real and imaginary parts, due to the nature of cell membranes of the lung tissue. In this paper, we present the first use of EIT boundary voltage data obtained from infants for the automatic detection of apnea using machine learning, and investigate which components contain the main features of apnea events. We selected 15 premature neonates with an episode of apnea in their breathing pattern and applied a hybrid classification model that combines two established methods;a pre-trained transfer learning method with a convolutional neural network with 50 layers deep (ResNet50) architecture, and a support vector machine (SVM) classifier. ResNet50 training was undertaken using an ImageNet dataset. The learnt parameters were fed into the SVM classifier to identify apnea and non-apnea cases from neonates' EIT datasets. The performance of our classification approach on the real part, the imaginary part and the absolute value of EIT boundary voltage datasets were investigated. We discovered that the imaginary component contained a larger proportion of apnea features.

关键词： Tomography Pediatrics Image reconstruction Lung Electrodes Support vector machines Training Apnea classification algorithm EIT data analysis electrical impedance tomography pre-trained ResNet50 transfer learning algorithm

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Chameleon-like intelligent camouflage metasurface

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MATERIALS & DESIGN 2023年 235卷

作者： Zhu, Ruichao Wang, Jiafu Sui, Sai Han, Yajuan Chu, Zuntian Jia, Yuxiang Li, Yuxi Qiu, Tianshuo Wang, Jun Qu, Shaobo Air Force Engn Univ Shaanxi Key Lab Artificially Struct Funct Mat & De Xian 710051 Shaanxi Peoples R China

Humans have been long dreaming of obtaining the ability of changing electromagnetic (EM) attributes intelli-gently according to the environment so as to camouflage just as chameleons do. Thanks to the unprecedented high degree of freedom in manipulating EM waves, metasurfaces can mimic EM characteristics of various en-vironments vividly and have pushed a giant step towards this long-desired dream. Nevertheless, for most met-asurfaces, the EM characteristics are fixed and thus cannot be altered according to changing environments, which hinders their applications in intelligent scenarios such as moving target camouflage. With the aim to achieve intelligent camouflage, in this work we propose a chameleon-like framework for intelligent camouflage based on EM metasurface, which integrates transfer-learning-empowered perceptron into a reconfigurable metasurface. As a demonstrative example, a chameleon-like intelligent camouflage metasurface (CLICM) system with robust extendibility was developed and measured in laboratory. The system can dynamically control the reflection spectrum in 5.1-6.5 GHz according to what it sees. Both the simulation and experiment results verify this framework. This work provides a perceptron-algorithm-metasurface framework with robust extendibility for intelligent EM manipulation and may find wide applications in camouflage and wireless communication.

关键词： Reconfigurable metasurface Perceptron-integration transfer learning algorithm Intelligent camouflage

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