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Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models Review

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Engineering 2019, Volume 5, Issue 5,   Pages 948-953 doi: 10.1016/j.eng.2019.03.010

Abstract: Next, we show that there are correspondences between deep neural networks and human visual streams interms of the architecture and computational rules Furthermore, deep generative models (e.g., variationalautoencoders (VAEs) and generative adversarial networks (GANs)) have produced promising results in studies

Keywords： Brain encoding and decoding     Functional magnetic resonance imaging     Deep neural networks     Deep generative    

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A Geometric Understanding of Deep Learning Article

Na Lei, Dongsheng An, Yang Guo, Kehua Su, Shixia Liu, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu

Engineering 2020, Volume 6, Issue 3,   Pages 361-374 doi: 10.1016/j.eng.2019.09.010

Abstract: font-family:"Times New Roman",serif">This work introduces an optimal transportation (OT) view of generativeWe also propose a novel generative model, which uses an autoencoder (AE) for manifold learning and OT

Keywords： Generative     Adversarial     Deep learning     Optimal transportation     Mode collapse    

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SmartPaint: a co-creative drawing system based on generative adversarial networks Special Feature on Intelligent Design

Lingyun SUN, Pei CHEN, Wei XIANG, Peng CHEN, Wei-yue GAO, Ke-jun ZHANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 12,   Pages 1644-1656 doi: 10.1631/FITEE.1900386

Abstract: To bridge this gap, we have developed SmartPaint, a co-creative drawing system based on generative adversarial

Keywords： Co-creative drawing     Deep learning     Image generation    

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Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology Article

Yufei Liu, Yuan Zhou, Xin Liu, Fang Dong, Chang Wang, Zihong Wang

Engineering 2019, Volume 5, Issue 1,   Pages 156-163 doi: 10.1016/j.eng.2018.11.018

Abstract:

It is essential to utilize deep-learning algorithms based on big data for the implementation of theEffective utilization of deep learning relies considerably on the number of labeled samples, which restrictsthe application of deep learning in an environment with a small sample size.In this paper, we propose an approach based on a generative adversarial network (GAN) combined with adeep neural network (DNN).

Keywords： Artificial intelligence     Generative adversarial network     Deep neural network     Small sample size     Cancer    

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Generative adversarial network based novelty detection usingminimized reconstruction error Article

Huan-gang WANG, Xin LI, Tao ZHANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 116-125 doi: 10.1631/FITEE.1700786

Abstract: Generative adversarial network (GAN) is the most exciting machine learning breakthrough in recent years

Keywords： Generative adversarial network (GAN)     Novelty detection     Tennessee Eastman (TE) process    

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Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial Special Feature on Intelligent Design

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 12,   Pages 1632-1643 doi: 10.1631/FITEE.1900399

Abstract: The synthesis module is developed based on a conditional generative adversarial network, which enables

Keywords： Cantonese porcelain     Classification     Generative adversarial network     Creative arts    

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Surprising Advances in Generative Artificial Intelligence Prompt Amazement—and Worries

Dana Mackenzie

Engineering 2023, Volume 25, Issue 6,   Pages 9-11 doi: 10.1016/j.eng.2023.04.004

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Digital image correlation-based structural state detection through deep learning

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 1,   Pages 45-56 doi: 10.1007/s11709-021-0777-x

Abstract: This paper presents a new approach for automatical classification of structural state through deep learning

Keywords： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

Frontiers in Energy 2023, Volume 17, Issue 2,   Pages 211-227 doi: 10.1007/s11708-022-0847-3

Abstract: interior point methods with adversarial networks (Modified IPMAN) as core modules, and discriminator generative

Keywords： end-to-end learning     microgrids     voltage profile improvement     generative adversarial network    

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Development and deep-sea exploration of the Haidou-1

Frontiers of Engineering Management   Pages 546-549 doi: 10.1007/s42524-023-0260-6

Abstract: Development and deep-sea exploration of the Haidou-1

Keywords： hadal zone     autonomous and remotely-operated vehicle     integrated exploration operation     deep dive exceeding    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predicteffective decision support for moving trajectory control and serve as a foundation for the application of deep

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingSecond, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-termACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 11,   Pages 930-939 doi: 10.1631/FITEE.1500125

Abstract: In this paper we propose a novel method to estimate head pose based on a deep convolutional neural network

Keywords： Head pose estimation     Deep convolutional neural network     Multiclass classification    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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Stability analysis on Tingzikou gravity dam along deep-seated weak planes during earthquake

Weiping HE, Yunlong HE

Frontiers of Structural and Civil Engineering 2012, Volume 6, Issue 1,   Pages 69-75 doi: 10.1007/s11709-012-0146-x

Abstract: The stability of a gravity dam against sliding along deep-seated weak planes is a universal and importantThere is no recommended method for stability analysis of the dam on deep-seated weak planes under earthquakeis focused on searching a proper way to evaluate the seismic safety of the dam against sliding along deep-seatedweak planes and the probable failure modes of dam on deep-seated weak planes during earthquake.

Keywords： gravity dam     deep-seated weak planes     stability against sliding     earthquake    

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Title Author Date Type Operation

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Journal Article

A Geometric Understanding of Deep Learning

Na Lei, Dongsheng An, Yang Guo, Kehua Su, Shixia Liu, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu

Journal Article

SmartPaint: a co-creative drawing system based on generative adversarial networks

Lingyun SUN, Pei CHEN, Wei XIANG, Peng CHEN, Wei-yue GAO, Ke-jun ZHANG

Journal Article

Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

Yufei Liu, Yuan Zhou, Xin Liu, Fang Dong, Chang Wang, Zihong Wang

Journal Article

Generative adversarial network based novelty detection usingminimized reconstruction error

Huan-gang WANG, Xin LI, Tao ZHANG

Journal Article

Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Journal Article

Surprising Advances in Generative Artificial Intelligence Prompt Amazement—and Worries

Dana Mackenzie

Journal Article

Digital image correlation-based structural state detection through deep learning

Journal Article

Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

Journal Article

Development and deep-sea exploration of the Haidou-1

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Journal Article

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Stability analysis on Tingzikou gravity dam along deep-seated weak planes during earthquake

Weiping HE, Yunlong HE

Journal Article