Resource Type

Journal Article 370

Conference Videos 4

Year

2024 42

2023 73

2022 73

2021 39

2020 39

2019 25

2018 17

2017 22

2016 8

2015 10

2014 1

2013 1

2011 1

2010 1

2009 1

2008 1

2007 3

2006 1

2005 1

2001 3

open ︾

Keywords

Machine learning 55

Deep learning 42

machine learning 33

deep learning 21

Reinforcement learning 16

Artificial intelligence 14

artificial neural network 6

Federated learning 5

artificial intelligence 5

Active learning 4

Bayesian optimization 4

Decision-making 4

Transfer learning 4

Adversarial attack 3

Attention 3

Autonomous driving 3

Classification 3

convolutional neural network 3

3D printing 2

open ︾

Search scope:

排序： Display mode:

Federated unsupervised representation learning Research Article

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1181-1193 doi: 10.1631/FITEE.2200268

Abstract: To address these challenges, we propose the federated contrastive averaging with dictionary and alignmentWe adopt the contrastive approach for local model training.

Keywords： Federated learning     Unsupervised learning     Representation learning     Contrastive learning    

HTML PDF Collect

Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

HTML PDF Collect

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

HTML PDF Collect

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords： Ozone     Integrated method     Machine learning    

HTML PDF Collect

Deep learning based water leakage detection for shield tunnel lining

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 6,   Pages 887-898 doi: 10.1007/s11709-024-1071-5

Abstract: A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced

Keywords： water leakage detection     deep learning     deconvolutional-feature pyramid     spatial attention    

HTML PDF Collect

Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 12, doi: 10.1007/s11783-023-1752-7

Abstract:

● Online learning models accurately predict influent flow rate at

Keywords： Wastewater prediction     Data stream     Online learning     Batch learning     Influent flow rates    

HTML PDF Collect

Advancing agriculture with machine learning: a new frontier in weed management

Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2024564

Abstract:

● Machine learning offers innovative and sustainable weed management

Keywords： Weed management     herbicides     machine learning     agricultural practices     environmental impact    

HTML PDF Collect

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov

Frontiers of Engineering Management doi: 10.1007/s42524-024-0082-1

Abstract: This study proposes a method, the online hidden Markov model (OHMM), which combines online learning with

Keywords： geological risk prediction     machine learning     online learning     hidden Markov model     borehole logging    

HTML PDF Collect

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning

Keywords： machine learning     flowsheet simulations     constraints     exploration    

HTML PDF Collect

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Frontiers of Medicine 2023, Volume 17, Issue 4,   Pages 768-780 doi: 10.1007/s11684-023-0982-1

Abstract: illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learningMachine learning modeling based on personal whole-exome data identified 46 genes with mutation burden

Keywords： machine learning methods     hypertrophic cardiomyopathy     genetic risk    

HTML PDF Collect

Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

HTML PDF Collect

Prediction of bearing capacity of pile foundation using deep learning approaches

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 6,   Pages 870-886 doi: 10.1007/s11709-024-1085-z

Abstract: The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile foundations. This research compares the Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) algorithms utilizing a data set of 257 dynamic pile load tests for the first time. Also, this research illustrates the multicollinearity effect on DNN, CNN, RNN, LSTM, and BiLSTM models’ performance and accuracy for the first time. A comprehensive comparative analysis is conducted, employing various statistical performance parameters, rank analysis, and error matrix to evaluate the performance of these models. The performance is further validated using external validation, and visual interpretation is provided using the regression error characteristics (REC) curve and Taylor diagram. Results from the comparative analysis reveal that the DNN (Coefficient of determination (R2)training (TR) = 0.97, root mean squared error (RMSE)TR = 0.0413; R2testing (TS) = 0.9, RMSETS = 0.08) followed by BiLSTM (R2TR = 0.91, RMSETR = 0.782; R2TS = 0.89, RMSETS = 0.0862) model demonstrates the highest performance accuracy. It is noted that the BiLSTM model is better than LSTM because the BiLSTM model, which increases the amount of information for the network, is a sequence processing model made up of two LSTMs, one of which takes the input in a forward manner, and the other in a backward direction. The prediction of pile-bearing capacity is strongly influenced by ram weight (having a considerable multicollinearity level), and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network approach. In this study, the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

Keywords： deep learning algorithms     high-strain dynamic pile test     bearing capacity of the pile    

HTML PDF Collect

Evaluation and prediction of slope stability using machine learning approaches

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 4,   Pages 821-833 doi: 10.1007/s11709-021-0742-8

Abstract: In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the

Keywords： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

HTML PDF Collect

Automated synthesis of steady-state continuous processes using reinforcement learning

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 288-302 doi: 10.1007/s11705-021-2055-9

Abstract: The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis

Keywords： automated process synthesis     flowsheet synthesis     artificial intelligence     machine learning     reinforcementlearning    

HTML PDF Collect

Machine learning for fault diagnosis of high-speed train traction systems: A review

Frontiers of Engineering Management 2024, Volume 11, Issue 1,   Pages 62-78 doi: 10.1007/s42524-023-0256-2

Abstract: In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstratedMachine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensiveThis paper primarily aims to review the research and application of machine learning in the field ofThen, the research and application of machine learning in traction system fault diagnosis are comprehensivelydiagnosis under actual operating conditions are revealed, and the future research trends of machine learning

Keywords： high-speed train     traction systems     machine learning     fault diagnosis    

HTML PDF Collect

Title Author Date Type Operation

Federated unsupervised representation learning

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article

Deep learning based water leakage detection for shield tunnel lining

Journal Article

Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

Journal Article

Advancing agriculture with machine learning: a new frontier in weed management

Journal Article

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

Prediction of bearing capacity of pile foundation using deep learning approaches

Journal Article

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

Automated synthesis of steady-state continuous processes using reinforcement learning

Journal Article

Machine learning for fault diagnosis of high-speed train traction systems: A review

Journal Article