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学者姓名：许婉妮
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Background: Symmetry is a special kind of balance. This study aims to systematically explore and apply the role of balanced composition in aesthetic judgments by focusing on balanced composition features and employing research methods from computational aesthetics and neuroaesthetics. Methods: First, experimental materials were classified by quantifying balanced composition using several indices, including symmetry, center of gravity, and negative space. An EEG experiment was conducted with 18 participants, who were asked to respond dichotomously to the same stimuli under different judgment tasks (balance and aesthetics), with both behavioral and EEG data being recorded and analyzed. Subsequently, participants' data were combined with balanced composition indices to construct and analyze various SVM classification models. Results: Participants largely used balanced composition as a criterion for aesthetic evaluation. ERP data indicated that from 300-500 ms post-stimulus, brain activation was more significant in the aesthetic task, with unbeautiful and imbalanced stimuli eliciting larger frontal negative waves and occipital positive waves. From 600-1000 ms, beautiful stimuli caused smaller negative waves in the PZ channel. The results of the SVM models indicated that the model incorporating aesthetic subject data (ACC = 0.9989) outperforms the model using only balanced composition parameters of the aesthetic object (ACC = 0.7074). Conclusions: Balanced composition is a crucial indicator in aesthetics, with similar early processing stages in both balance and aesthetic judgments. Multi-modal data models validated the advantage of including human factors in aesthetic evaluation systems. This interdisciplinary approach not only enhances our understanding of the cognitive and emotional processes involved in aesthetic judgments but also enables the construction of more reasonable machine learning models to simulate and predict human aesthetic preferences.
Keyword ：
aesthetic aesthetic balance composition balance composition computational aesthetics computational aesthetics EEG EEG ERPs ERPs neuroaesthetics neuroaesthetics SVM SVM
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| GB/T 7714 | Lin, Fangfu , Song, Wu , Li, Yan et al. Investigating the Relationship between Balanced Composition and Aesthetic Judgment through Computational Aesthetics and Neuroaesthetic Approaches [J]. | SYMMETRY-BASEL , 2024 , 16 (9) . |
| MLA | Lin, Fangfu et al. "Investigating the Relationship between Balanced Composition and Aesthetic Judgment through Computational Aesthetics and Neuroaesthetic Approaches" . | SYMMETRY-BASEL 16 . 9 (2024) . |
| APA | Lin, Fangfu , Song, Wu , Li, Yan , Xu, Wanni . Investigating the Relationship between Balanced Composition and Aesthetic Judgment through Computational Aesthetics and Neuroaesthetic Approaches . | SYMMETRY-BASEL , 2024 , 16 (9) . |
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Background: In recent years, computational aesthetics and neuroaesthetics have provided novel insights into understanding beauty. Building upon the findings of traditional aesthetics, this study aims to combine these two research methods to explore an interdisciplinary approach to studying aesthetics. Method: Abstract artworks were used as experimental materials. Based on traditional aesthetics and in combination, features of composition, tone, and texture were selected. Computational aesthetic methods were then employed to correspond these features to physical quantities: blank space, gray histogram, Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor filters. An electroencephalogram (EEG) experiment was carried out, in which participants conducted aesthetic evaluations of the experimental materials in different contexts (genuine, fake), and their EEG data were recorded to analyze the impact of various feature classes in the aesthetic evaluation process. Finally, a Support Vector Machines (SVMs) was utilized to model the feature data, Event-Related Potentials (ERPs), context data, and subjective aesthetic evaluation data. Result: Behavioral data revealed higher aesthetic ratings in the genuine context. ERP data indicated that genuine contexts elicited more negative deflections in the prefrontal lobes between 200 and 1000 ms. Class II compositions demonstrated more positive deflections in the parietal lobes at 50-120 ms, while Class I tones evoked more positive amplitudes in the occipital lobes at 200-300 ms. Gabor features showed significant variations in the parieto-occipital area at an early stage. Class II LBP elicited a prefrontal negative wave with a larger amplitude. The results of the SVM models indicated that the model incorporating aesthetic subject and context data (ACC = 0.76866) outperforms the model using only parameters of the aesthetic object (ACC = 0.68657). Conclusion: A positive context tends to provide participants with a more positive aesthetic experience, but abstract artworks may not respond to this positivity. During aesthetic evaluation, the ERP data activated by different features show a trend from global to local. The SVM model based on multimodal data fusion effectively predicts aesthetics, further demonstrating the feasibility of the combined research approach of computational aesthetics and neuroaesthetics.
Keyword ：
aesthetics aesthetics computational aesthetics computational aesthetics EEG EEG ERP ERP neuroaesthetics neuroaesthetics SVM SVM
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| GB/T 7714 | Lin, Fangfu , Xu, Wanni , Li, Yan et al. Exploring the Influence of Object, Subject, and Context on Aesthetic Evaluation through Computational Aesthetics and Neuroaesthetics [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (16) . |
| MLA | Lin, Fangfu et al. "Exploring the Influence of Object, Subject, and Context on Aesthetic Evaluation through Computational Aesthetics and Neuroaesthetics" . | APPLIED SCIENCES-BASEL 14 . 16 (2024) . |
| APA | Lin, Fangfu , Xu, Wanni , Li, Yan , Song, Wu . Exploring the Influence of Object, Subject, and Context on Aesthetic Evaluation through Computational Aesthetics and Neuroaesthetics . | APPLIED SCIENCES-BASEL , 2024 , 14 (16) . |
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To reduce environmental pollution and resource waste in manufacturing, it is important to discuss the increase in consumers' willingness to buy sustainable clothing and their satisfaction with it, as well as ideas on product design optimization and marketing strategies of environmental protection clothing enterprises as well as business and logistics operations. The approach used in this study to obtain the overall rank of criteria is a combination of the Decision Making Trial and Evaluation Laboratory (DEMATEL) and Decision-making Trial and Evaluation Laboratory based on ANP (DANP). Using Muji brand clothing as the empirical research object, this paper seeks to investigate the important factors and consumer preferences in purchasing sustainable clothing. We collected 140 criterion-importance questionnaires and 161 IPA questionnaires. Through literature review and the Delphi method, this study provides an important criterion framework for sustainable clothing, and it used the DEMATEL-based Analytical Network Process (ANP) method to determine the causal relationships among the purchasing factors of sustainable clothing and the importance ranking of these factors. According to the findings, the criterion of "sustainable material" is the most important factor in consumers' decisions to purchase sustainable clothing, and the "environmental protection brand image" and "value expression" are key factors that should be optimized. The evaluation model can be widely used in clothing design, marketing, questionnaire making, evaluation indicators, enterprise development, business operations, and other fields. Such a study has strong implications for industry 4.0 research in the future.
Keyword ：
Apparel manufacturing Apparel manufacturing Business and logistics Business and logistics Delphi Delphi DEMATEL-based analytical network process DEMATEL-based analytical network process Importance Performance Analysis Importance Performance Analysis Product design Product design
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| GB/T 7714 | Xu, Wanni , Wang, Lu , Zhuang, Qingbin et al. Management of products in the apparel manufacturing industry using DEMATEL-based analytical network process technique [J]. | OPERATIONS MANAGEMENT RESEARCH , 2024 . |
| MLA | Xu, Wanni et al. "Management of products in the apparel manufacturing industry using DEMATEL-based analytical network process technique" . | OPERATIONS MANAGEMENT RESEARCH (2024) . |
| APA | Xu, Wanni , Wang, Lu , Zhuang, Qingbin , Yu, Na , Guan, Minghua , Tian, Zhibo et al. Management of products in the apparel manufacturing industry using DEMATEL-based analytical network process technique . | OPERATIONS MANAGEMENT RESEARCH , 2024 . |
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Objective: Deep learning-based CNN networks have recently been investigated to solve the problem of body posture recognition based on surface electromyographic signals (sEMG). Influenced by these studies, to develop a combined approach of sEMG and CNNs in the study of human-product interactions and the impact of body comfort, and to compare the advantages and disadvantages of various CNNs networks.Methods: In this study, sEMG measurements were carried out by building a prototype usability experiment, and the data were divided into four categories, with two types of datasets: training and testing. Four CNNs, LeNet-5, VGGNet-11, InceptionNet V4, and DenseNet, were used for the recognition of sEMG images.Results: DenseNet is another type of convolutional neural network with deep layers, which has a unique advantage over other algorithms. unique advantages over other algorithms. DenseNet has fewer layers and better accuracy than InceptionNet V4, but not only does it bypass enhanced feature reuse, but its network is easier to train and has some regularization effects, while also mitigating the problems of gradient disappearance and model degradation.Conclusion: These findings could lead to a more appropriate CNN model and a useful tool for developing comfort judgments of surface EMG signals, furthering the development of products that come into contact with the human body without the need for routine retraining.
Keyword ：
Convolutional neural network Convolutional neural network Health informatics Health informatics Prototype comfort Prototype comfort sEMG imaging sEMG imaging Sternocleidomastoid Sternocleidomastoid
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| GB/T 7714 | Fu, You-Lei , Song, Wu , Xu, Wanni et al. Feature recognition in multiple CNNs using sEMG images from a prototype comfort test [J]. | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2023 , 243 . |
| MLA | Fu, You-Lei et al. "Feature recognition in multiple CNNs using sEMG images from a prototype comfort test" . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 243 (2023) . |
| APA | Fu, You-Lei , Song, Wu , Xu, Wanni , Lin, Jie , Nian, Xuchao . Feature recognition in multiple CNNs using sEMG images from a prototype comfort test . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2023 , 243 . |
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Background and objective: Deep learning, a novel approach and subset of machine learning, has drawn a growing amount of attention from computer vision researchers in recent years. This method has drawn a lot of interest because of its extraordinary ability to interpret medical pictures, especially when combined with residual neural networks, which have helped to progress the field. Methods: In this paper, the following research is carried out on the residual network. First, the research status of ResNet in the medical field is introduced. The fundamental idea behind the residual neural network is then explained, along with the residual unit, its many structures, and the network architecture. Second, four aspects of the widespread use of residual neural networks in medical image processing are discussed: lung tumor, diagnosis of skin diseases, diagnosis of breast diseases, and diagnosis of diseases of the brain. Finally, the main issues and ResNet's future development in the area of processing medical images are discussed. Results: In the area of medical graph processing, residual neural networks have made strides and have had success in the clinical auxiliary diagnosis of serious illnesses such as lung tumors, breast cancer, skin conditions, and cardiovascular and cerebrovascular diseases. Conclusion: We thoroughly sorted out the most recent developments in residual neural network research and their use in medical image processing, which serves as a crucial point of reference for this field of study. It offers a helpful reference for further promoting the application and research of the ResNet model in the field of medical image processing by summarising the application status and issues of the ResNet model in the field of medical image processing and putting forwards some future development directions.& COPY; 2023 Elsevier B.V. All rights reserved.
Keyword ：
Disease diagnosis Disease diagnosis Medical image Medical image Residual connection Residual connection Residual neural network (ResNet) Residual neural network (ResNet) Residual units Residual units
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| GB/T 7714 | Xu, Wanni , Fu, You-Lei , Zhu, Dongmei . ResNet and its application to medical image processing: Research progress and challenges [J]. | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2023 , 240 . |
| MLA | Xu, Wanni et al. "ResNet and its application to medical image processing: Research progress and challenges" . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 240 (2023) . |
| APA | Xu, Wanni , Fu, You-Lei , Zhu, Dongmei . ResNet and its application to medical image processing: Research progress and challenges . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2023 , 240 . |
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In order to restore the original colours of ancient relics more accurately and to reduce the burden of manual restoration, we developed a novel colour-restoration technique based on the DenseNet algorithm, which was used in a case study involving restoration of Dunhuang mural images and is based on deep learning. In recent years, deep learning-based methods have been an important direction for research into virtual restoration of image colours. Typical, damaged murals were generated from 60 mural datasets as input for the system, and these were enhanced by DenseNet, based on the interactive, digital mural-restoration system. We compared execution time, peak signal-to-noise ratio and structural similarities to evaluate DenseNet, SegNet, Deeplab and ResNet algorithms. In terms of time efficiency, the DenseNet algorithm was 44.62% faster than the SegNet algorithm. Regarding structural similarity (SSIM) values for the four models, DenseNet was the lowest: 1.289% lower than SegNet, 2.442% lower than Deeplab v3 and 1.288% lower than ResNet. In terms of the overall comparison, the repair effect for DenseNet was the best. Our method is highly reliable for mural restoration and not only saves time but also produces better virtual restoration results than other methods.
Keyword ：
Algorithm optimization Algorithm optimization Ancient relics Ancient relics Colour image segmentation Colour image segmentation Deep learning Deep learning Image restoration Image restoration
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| GB/T 7714 | Xu, Wanni , Fu, Youlei . Deep learning algorithm in ancient relics image colour restoration technology [J]. | MULTIMEDIA TOOLS AND APPLICATIONS , 2022 . |
| MLA | Xu, Wanni et al. "Deep learning algorithm in ancient relics image colour restoration technology" . | MULTIMEDIA TOOLS AND APPLICATIONS (2022) . |
| APA | Xu, Wanni , Fu, Youlei . Deep learning algorithm in ancient relics image colour restoration technology . | MULTIMEDIA TOOLS AND APPLICATIONS , 2022 . |
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Objective: The traditional ICM is widely used in applications, such as image edge detection and image segmentation. However, several model parameters must be set, which tend to lead to reduced accuracy and increased cost. As medical images have more complex edges, contours and details, more suitable combinatorial algorithms are needed to handle the pathological diagnosis of multiple cerebral infarcts and acute strokes, resulting in the findings being more applicable, as well as having good clinical value.Methods: To better solve the medical image fusion and diagnosis problems, this paper introduces the im-age fusion algorithm based on the combination of NSCT and improved ICM and proposes low-frequency, sub-band fusion rules and high-frequency sub-band fusion rules. The above method is applied to the fusion of CT/MRI images, subsequently, three other fusion algorithms, including NSCT-SF-PCNN, NSCT-SR-PCNN and Adaptive-PCNN are compared, and the simulation results of image fusion are analyzed and validated. Results: According to the experimental findings, the suggested algorithm performs better than other fu-sion algorithms in terms of five objecti ve evaluation metrics or subjective evaluation. The NSCT transform and the improved ICM were combined, and the outcomes were evaluated against those of other fusion algorithms. The CT/MRI medical images of healthy brain tissue, numerous cerebral infarcts and acute strokes were combined using this technique.Conclusion: Medical image fusion using Adaptive-PCNN produces satisfactory results, not only in relation to improved image clarity but also in terms of outstanding edge information, high contrast and bright-ness.(c) 2022 Published by Elsevier B.V.
Keyword ：
Intersecting cortical model (ICM) Intersecting cortical model (ICM) Medical image fusion Medical image fusion Non-subsampled contourlet transform Non-subsampled contourlet transform (NSCT) (NSCT) Pulse coupled neural network (PCNN) Pulse coupled neural network (PCNN)
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| GB/T 7714 | Xu, Wanni , Fu, You-Lei , Xu, Huasen et al. Medical image fusion using enhanced cross-visual cortex model based on artificial selection and impulse-coupled neural network [J]. | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2022 , 229 . |
| MLA | Xu, Wanni et al. "Medical image fusion using enhanced cross-visual cortex model based on artificial selection and impulse-coupled neural network" . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 229 (2022) . |
| APA | Xu, Wanni , Fu, You-Lei , Xu, Huasen , Wong, Kelvin K. L. . Medical image fusion using enhanced cross-visual cortex model based on artificial selection and impulse-coupled neural network . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2022 , 229 . |
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