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Development of machine learning multi-city model for municipal solid waste generation prediction

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 9, doi: 10.1007/s11783-022-1551-6

Abstract:

● A database of municipal solid waste (MSW) generation in China was established.

Keywords： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Frontiers in Energy doi: 10.1007/s11708-023-0891-7

Abstract: methods in lithium-ion battery health management and in particular analyses the application of machine learningReports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has theby, first, utilizing more field data to play a more practical role in health feature screening and model

Keywords： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with theapplication of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model.A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL modelwas obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSIThe BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel,

Keywords： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

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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 fractureA total of 147 raw input features are considered in our model.The presented model is compared with several benchmarks based on various metrics to prove its effectiveness

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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

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

Abstract: In this paper, we propose a novel data-driven voltage profile improvement model, denoted as system-widerealize topology identification and decision-making optimization in sequence, the proposed end-to-end modelMore specifically, the proposed model consists of four modules, Pre-training Network and modified interior

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

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An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 237-250 doi: 10.1007/s11705-021-2058-6

Abstract: Advanced model-based control strategies, e.g., model predictive control, can offer superior control ofThe quality of the system model is critical to controller performance and should adequately describederiving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear modelboth output and manipulated variables were trained on simulated data and integrated into a nonlinear modelThis demonstration of how such system models could be identified for nonlinear model predictive control

Keywords： nonlinear model predictive control     black-box modeling     continuous-time system identification     machinelearning     industrial applications of process control    

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0688-0

Abstract: The classification accuracy of the popular machine learning methods has been evaluated in comparisonwith the proposed deep learning model.Based on the experimental data collected during the milling experiments, the proposed model proved toThe average classification accuracy obtained using the proposed deep learning model was 9.55% higherthan the best machine learning algorithm considered in this paper.

Keywords： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

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Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1316-1330 doi: 10.1007/s11709-020-0646-z

Abstract: In this study, the deep learning models for estimating the mechanical properties of concrete containingTwo well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM)The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectivelyIn addition, this study found that deep learning, which has a very good prediction ability with little

Keywords： concrete     high temperature     strength properties     deep learning     stacked auto-encoders     LSTM network    

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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： Short-term water demand forecasting     Long-short term memory neural network     Convolutional Neural Network     Wavelet multi-resolution analysis     Data-driven models    

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Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 340-352 doi: 10.1007/s11465-021-0629-3

Abstract: Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years.However, many deep learning methods cannot fully extract fault information to recognize mechanical healthTherefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNNseveral 1D DCNN models with different activation functions are trained through dimension reduction learningLastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features

Keywords： fault intelligent diagnosis     deep learning     deep convolutional neural network     high-dimensional samples    

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Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1097-1109 doi: 10.1007/s11709-020-0634-3

Abstract: A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committeewell-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed modelThe results showed that the RF model has the best prediction performance compared to SKM and Shannon

Keywords： compound channel     machine learning     SKM model     shear stress distribution     data mining models    

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A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8,   Pages 990-1002 doi: 10.1007/s11709-022-0864-7

Abstract: The ANN-GWO model was created using 115 samples from different sources, taking into account nine keyThe validation of the proposed model was evaluated via six indices, including correlation coefficient

Keywords： artificial neural network     grey wolf optimize algorithm     compressive strength     self-compacting concrete    

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Estimating Rainfall Intensity Using an Image-Based Deep Learning Model Article

Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan

Engineering 2023, Volume 21, Issue 2,   Pages 162-174 doi: 10.1016/j.eng.2021.11.021

Abstract: This paper proposes an image-based deep learning model to estimate urban rainfall intensity with highMore specifically, a convolutional neural network (CNN) model called the image-based rainfall CNN (irCNN) model is developed using rainfall images collected from existing dense sensors (i.e., smart phonesThe trained irCNN model is subsequently employed to efficiently estimate rainfall intensity based onThe results show that the irCNN model provides rainfall estimates with a mean absolute percentage error

Keywords： Urban flooding     Rainfall images     Deep learning model     Convolutional neural networks (CNNs)     Rainfall    

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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 6,   Pages 759-771 doi: 10.1007/s11705-022-2269-5

Abstract: This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), forThe model is comprised of self-organizing-map and the neural network parts.Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledgeMoreover, the MISR model has smoother error convergence than the previous model.Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which

Keywords： hydrocracking     convolutional neural networks     self-organizing map     deep learning     data-driven optimization    

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Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

Frontiers in Energy 2017, Volume 11, Issue 2,   Pages 175-183 doi: 10.1007/s11708-017-0471-9

Abstract: A locally weighted learning method is also proposed to utilize the processed feature set to produce theThe proposed model is simple and easy to use with parameters optimized automatically.

Keywords： set     minimal-redundancy-maximal-relevance (mRMR)     principal component analysis (PCA)     locally weighted learningmodel    

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Title Author Date Type Operation

Development of machine learning multi-city model for municipal solid waste generation prediction

Journal Article

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Journal Article

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

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

Journal Article

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

Journal Article

An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

Journal Article

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

Journal Article

Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

Journal Article

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

Journal Article

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Journal Article

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

Journal Article

A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Journal Article

Estimating Rainfall Intensity Using an Image-Based Deep Learning Model

Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan

Journal Article

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Journal Article

Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

Journal Article