A Deep Learning Method Based on Multi-Modality EEG for Automatic Depression Screening

Wang, MengWei; Zhang, Yiyang; Xu, Jin

doi:10.1016/j.ijpsycho.2021.07.557

Cited by 2 publications

(2 citation statements)

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“…At present, the more recognized de nition in the academic circle comes from the book "Deep Learning: A Modern Approach" by Stuart Russell and Peter Norvig: "deep learning" is the research and design of "intelligent agents" [2], and "an intelligent agent refers to a system that can observe the surrounding environment and take actions to achieve the goal." Today, deep learning has been widely used in speech recognition, machine vision, data mining, etc., and the cross-border between deep learning and publishing and design has gradually emerged [3]. Content is the starting point of book publishing, and content is often summarized and compiled by writers and scholars through learning, reading, investigation, and research, and this process requires a lot of time and energy to complete.…”

Section: Introductionmentioning

confidence: 99%

Research on Modern Book Packaging Design Based on Aesthetic Evaluation Based on a Deep Learning Model

Wen

2022

Scientific Programming

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Through the analysis of the application and development of deep learning in the field of book design and publishing, the article expounds on the positive impact of deep learning on book design and publishing, discusses the shortcomings of deep learning in creative ability, aesthetic ability, emotion, etc., and then discusses the design and publishing of books. The future development direction of intelligent aided design and intelligent personalized design is proposed to provide a reference for researchers in deep learning and book design and publication.

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Section: Introductionmentioning

confidence: 99%

Research on Modern Book Packaging Design Based on Aesthetic Evaluation Based on a Deep Learning Model

Wen

2022

Scientific Programming

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“…Internationally, in 2012, Germany launched the "Industry 4.0 Plan" focusing on "smart factories." In December 2016, the United Kingdom published "Artificial Intelligence: Opportunities and Implications for Future Decision Designation" [3]. France released its Artificial Intelligence Strategy in March 2017.…”

Section: Introductionmentioning

confidence: 99%

Corresponding Intelligent Calculation of the Whole Process of Building Civil Engineering Structure Based on Deep Learning

Liu

Zhang

et al. 2022

Scientific Programming

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This study proposes the first fully deep learning-based structural response intelligent computing framework for civil engineering. For the first time, from the data side to the model side, the structural information of the structure itself and any loading system is comprehensively considered, which can be applied to materials, components, and even structures, system and other multi-level mechanical response prediction problems. First, according to the characteristics of structural calculation scenarios, a unified data interface mode for structural static characteristics is formulated, which preserves the original structural information input and effectively reduces manual intervention. On this basis, an attention mechanism and a deep cross network are introduced, and a structural static feature representation learning model PADCN is proposed, which can take into account the memory and generalization of structural static features, and mine the coupling relationship of different structural information. Then, the PADCN model is integrated with the dynamic feature prediction model Mechformer and connected with the designed general data interface to form an end-to-end data-driven structural response intelligent computing framework. In order to verify the validity of the framework, numerical experiments were carried out with the steel plate shear wall structure as the carrier, in which a data augmentation algorithm suitable for the field of structural calculation was proposed to alleviate the problem of lack of structural engineering data. The results show that the deep learning model based on this framework successfully predicts the whole-process nonlinear response of specimens with different structures, the simulation accuracy is better than that of the fine finite element model, and the computational efficiency exceeds the traditional numerical method by more than 1000 times, achieving a qualitative improvement. It is proven that the intelligent computing framework has excellent accuracy and efficiency.

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A Deep Learning Method Based on Multi-Modality EEG for Automatic Depression Screening

Cited by 2 publications

References 0 publications

Research on Modern Book Packaging Design Based on Aesthetic Evaluation Based on a Deep Learning Model

Research on Modern Book Packaging Design Based on Aesthetic Evaluation Based on a Deep Learning Model

Corresponding Intelligent Calculation of the Whole Process of Building Civil Engineering Structure Based on Deep Learning

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