Resource Type

Journal Article 347

Year

2023 44

2022 40

2021 26

2020 37

2019 38

2018 20

2017 22

2016 11

2015 5

2014 13

2013 9

2012 3

2011 11

2010 13

2009 8

2008 3

2007 5

2006 4

2005 2

2004 2

open ︾

Keywords

Deep learning 36

deep learning 15

Artificial intelligence 11

Machine learning 6

concrete 5

artificial neural network 4

compressive strength 4

Autonomous driving 3

Deep neural network 3

Accelerator 2

Attention 2

Big data 2

COVID-19 2

Classification 2

Computed tomography 2

Computer vision 2

Deep mining 2

Deep reinforcement learning 2

PM2.5 2

open ︾

Search scope:

排序: Display mode:

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: Although previous researchers have made significant advances in brain encoding and decoding models, existingFor example, traditional methods usually build the encoding and decoding models separately, and are proneNext, 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 generativemodels     Dual learning    

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    

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3

Abstract:

● A novel deep learning framework for short-term water demand forecasting

Keywords: memory neural network     Convolutional Neural Network     Wavelet multi-resolution analysis     Data-driven models    

Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition Article

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 978-988 doi: 10.1631/FITEE.1600996

Abstract: integrate multi-layer perceptrons (MLPs) in either a hybrid or a tandem fashion into hidden Markov modelsIn this paper, we propose a deep architecture-based tandem approach for unconstrained offline handwritingIn the proposed model, deep belief networks are adopted to learn the compact representations of sequential

Keywords: Handwriting recognition     Hidden Markov models     Deep learning     Deep belief networks     Tandem approach    

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    

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    

A review on different theoretical models of electrocaloric effect for refrigeration

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 478-503 doi: 10.1007/s11708-023-0884-6

Abstract: This paper reviews the electrocaloric effect of ferroelectric materials based on different theoretical models

Keywords: electrocaloric effect     effective Hamiltonian     phase-field modeling     different theoretical models    

Predictive models on photolysis and photoinduced toxicity of persistent organic chemicals

Qing ZHANG

Frontiers of Environmental Science & Engineering 2013, Volume 7, Issue 6,   Pages 803-814 doi: 10.1007/s11783-013-0547-7

Abstract: Quantitative structure-activity relationship (QSAR) models that relate photodegradation kinetics or photoinducedThis paper reviews the QSAR models on photodegradation quantum yields and rate constants for toxic organicchemicals in different media including liquid phase, gaseous phase, surfaces of plant leaves, and QSAR models

Keywords: quantitative structure-activity relationship (QSAR) models     photodegradation     persistent organic pollutants    

Empirical models and design codes in prediction of modulus of elasticity of concrete

Behnam VAKHSHOURI, Shami NEJADI

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 38-48 doi: 10.1007/s11709-018-0479-1

Abstract: This study includes: (a) evaluation and comparison of the existing analytical models to estimating theIn addition, a wide range of experimental databases and empirical models to estimate the MOE from compressiveThe results show underestimation of MOE of conventional concrete in majority of the existing models.considering the consistency between density and mechanical properties of concrete, the predicted MOE in the models

Keywords: modulus of elasticity     normal strength normal weight concrete     empirical models     design codes     compressive    

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    

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    

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

Managing economic and social profit of cooperative models in three-echelon reverse supply chain for waste

Jian Li, Zhen Wang, Bao Jiang

Frontiers of Environmental Science & Engineering 2017, Volume 11, Issue 5, doi: 10.1007/s11783-017-0999-2

Abstract: The paper investigates cooperative models with different parties in a three-echelon reverse supply chainIn addition, the optimal decisions of four cooperative models and the effect of the market demand of

Keywords: electrical and electronic equipment (WEEE)     Reverse supply chains     Recycle quantity     Social benefit     Cooperative models    

Impact analytical models for earthquake-induced pounding simulation

Kun YE, Li LI

Frontiers of Structural and Civil Engineering 2009, Volume 3, Issue 2,   Pages 142-147 doi: 10.1007/s11709-009-0029-y

Abstract: pounding under earthquake has been recently extensively investigated using various impact analytical modelsIn this paper, a brief review on the commonly used impact analytical models is conducted.

Keywords: structural pounding     Hertz model     Kelvin model     nonlinear damping     coefficient of restitution    

Depth of cut models for multipass abrasive waterjet cutting of alumina ceramics with nozzle oscillation

Jun WANG

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 1,   Pages 19-32 doi: 10.1007/s11465-009-0082-1

Abstract: Predictive models for the depth of cut are then developed.particle erosion theories applied to alumina ceramics, before progressing to the development of the modelsThe models are finally assessed both qualitatively and quantitatively with experimental data.

Keywords: abrasive waterjet     engineering ceramics     depth of cut     cutting performance     nozzle oscillation     machining    

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

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Journal Article

Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition

Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET

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

A review on different theoretical models of electrocaloric effect for refrigeration

Journal Article

Predictive models on photolysis and photoinduced toxicity of persistent organic chemicals

Qing ZHANG

Journal Article

Empirical models and design codes in prediction of modulus of elasticity of concrete

Behnam VAKHSHOURI, Shami NEJADI

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

Managing economic and social profit of cooperative models in three-echelon reverse supply chain for waste

Jian Li, Zhen Wang, Bao Jiang

Journal Article

Impact analytical models for earthquake-induced pounding simulation

Kun YE, Li LI

Journal Article

Depth of cut models for multipass abrasive waterjet cutting of alumina ceramics with nozzle oscillation

Jun WANG

Journal Article