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Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting Article

Jia Shi, Jinchun Song, Bin Song, Wen F. Lu

Engineering 2019, Volume 5, Issue 3,   Pages 586-593 doi: 10.1016/j.eng.2018.12.009

Abstract: this paper, a multi-objective optimization (MOO) design method for DOD printing parameters through fullyconnected neural networks (FCNNs) is proposed in order to solve these challenges.

Keywords： Drop-on-demand printing     Inkjet printing     Gradient descent multi-objective optimization     Fully connectedneural networks    

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Novel interpretable mechanism of neural networks based on network decoupling method

Frontiers of Engineering Management 2021, Volume 8, Issue 4,   Pages 572-581 doi: 10.1007/s42524-021-0169-x

Abstract: The lack of interpretability of the neural network algorithm has become the bottleneck of its wide applicationthat a simple linear mapping relationship exists between network structure and network behavior in the neuralOur simulation and theoretical results fully demonstrate this interesting phenomenon.new interpretation mechanism provides not only the potential mathematical calculation principle of neuralor animal activities, which can further expand and enrich the interpretable mechanism of artificial neural

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

Frontiers of Chemical Science and Engineering 2013, Volume 7, Issue 3,   Pages 357-365 doi: 10.1007/s11705-013-1336-3

Abstract: Several simulation systems including a back-propagation neural network (BPNN), a radial basis functionneural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested andThe performance of these networks was evaluated using the coefficient of determination ( ) and the mean

Keywords： oil recovery     artificial intelligence     extraction     neural networks     supercritical extraction    

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 3,   Pages 609-622 doi: 10.1007/s11709-020-0623-6

Abstract: grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial NeuralNetworks (ANNs).

Keywords： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

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Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system andRBFNSM for wind turbine in the grid connected mode

Alireza REZVANI,Ali ESMAEILY,Hasan ETAATI,Mohammad MOHAMMADINODOUSHAN

Frontiers in Energy 2019, Volume 13, Issue 1,   Pages 131-148 doi: 10.1007/s11708-017-0446-x

Abstract: generation system for loads and improve the dynamic performance of the whole generation system in the grid connectedIn order to capture the maximum power, a hybrid fuzzy-neural maximum power point tracking (MPPT) methodThe average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentageDetailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid

Keywords： photovoltaic     wind turbine     hybrid system     fuzzy logic controller     genetic algorithm     RBFNSM    

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Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 4,   Pages 490-503 doi: 10.1007/s11709-017-0445-3

Abstract: This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of

Keywords： artificial neural networks     root mean square error     SIFCON     silica fume     metakaolin     steel fiber    

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Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Frontiers in Energy 2016, Volume 10, Issue 1,   Pages 105-113 doi: 10.1007/s11708-016-0393-y

Abstract: This paper proposes the day-ahead electricity price forecasting using the artificial neural networks

Keywords： day-ahead electricity markets     price forecasting     load forecasting     artificial neural networks     load serving    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 520-536 doi: 10.1007/s11709-021-0689-9

Abstract: Two artificial-intelligence-based models including artificial neural networks and support vector machinessupport vector machines in predicting the strength of the investigated soils compared with artificial neuralnetworks.

Keywords： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 2,   Pages 167-177 doi: 10.1007/s11709-014-0236-z

Abstract: Artificial neural network (ANN) approach has been also employed to derive empirical formulae for predicting

Keywords： steel I-beams     lateral-torsional buckling     finite element (FE) method     artificial neural network (ANN) approach    

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A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks

Zhang Junyan,Feng Shouzhong,Liu Donghai

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 87-90

Abstract: inflexion points, a method for forecasting tunnel surrounding rock deformation using radial basis function neuralnetworks is presented.curves, but also has higher convergence speed and better globally-searching ability than those using BP neuralnetworks.

Keywords： RBF neural networks     tunnel construction     surrounding rock deformation     forecasting    

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Diffractive Deep Neural Networks at Visible Wavelengths Article

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Engineering 2021, Volume 7, Issue 10,   Pages 1485-1493 doi: 10.1016/j.eng.2020.07.032

Abstract: One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing

Keywords： Optical computation     Optical neural networks     Deep learning     Optical machine learning     Diffractive deepneural networks    

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immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neuralnetworks for plug-in hybrid electric vehicles fuel economy

Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 2,   Pages 154-167 doi: 10.1007/s11465-015-0336-z

Abstract: The objective function of the optimizer is derived from a computationally efficient artificial neural

Keywords： information preview     intelligent transportation     state-of-charge trajectory builder     immune systems     artificial neural    

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Service life prediction of fly ash concrete using an artificial neural network

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 793-805 doi: 10.1007/s11709-021-0717-9

Abstract: lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificial neuralMoreover, experimental validation carried out for the developed model shows that the artificial neural

Keywords： concrete     fly ash     carbonation     neural networks     experimental validation     service life    

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high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neuralnetworks

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 1, doi: 10.1007/s11783-023-1606-3

Abstract:

● Reducting the sampling frequency can enhance the modelling process.

Keywords： HDPE     Pyrolysis     Kinetics     Thermogravimetric     ANOVA     Artificial neural network    

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Learning and Applications of Procedure Neural Networks

He Xingui,Liang Jiuzhen,Xu Shaohua

Strategic Study of CAE 2001, Volume 3, Issue 4,   Pages 31-35

Abstract:

This paper deals with learning algorithms for procedure neural networks (PNN) and its applications

Keywords： procedure neural networks     learning algorithm     pattern recognition     chemical reaction     seepage    

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Title Author Date Type Operation

Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting

Jia Shi, Jinchun Song, Bin Song, Wen F. Lu

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

Journal Article

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Journal Article

Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system andRBFNSM for wind turbine in the grid connected mode

Alireza REZVANI,Ali ESMAEILY,Hasan ETAATI,Mohammad MOHAMMADINODOUSHAN

Journal Article

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

Journal Article

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Journal Article

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Journal Article

Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

Journal Article

A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks

Zhang Junyan,Feng Shouzhong,Liu Donghai

Journal Article

Diffractive Deep Neural Networks at Visible Wavelengths

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Journal Article

immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neuralnetworks for plug-in hybrid electric vehicles fuel economy

Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD

Journal Article

Service life prediction of fly ash concrete using an artificial neural network

Journal Article

high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neuralnetworks

Journal Article

Learning and Applications of Procedure Neural Networks

He Xingui,Liang Jiuzhen,Xu Shaohua

Journal Article