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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: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis.CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learningFirst, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingACNN 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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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 learningbranches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neuralnetwork (NN) methods in ML for lithium-ion battery SOH simulation and prediction.

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

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 214-223 doi: 10.1007/s11709-021-0800-2

Abstract: Such strategy leverages the high capacity of convolutional neural networks to identify and classify potentialIn a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural

Keywords： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Real-time tool condition monitoring method based on temperature measurement and artificial neural network

Frontiers of Mechanical Engineering doi: 10.1007/s11465-021-0661-3

Abstract: The spectrum features are then selected and input into the artificial neural network for classificationA learning rate adaption algorithm is introduced, greatly reducing the dependence on initial parameters

Keywords： tool condition monitoring     cutting temperature     neural network     learning rate adaption    

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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    

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Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neuralnetwork

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1621-4

Abstract:

● Used a double-stage attention mechanism model to predict ozone.

Keywords： Ozone prediction     Deep learning     Time series     Attention     Volatile organic compounds    

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Optimization of turbine cold-end system based on BP neural network and genetic algorithm

Chang CHEN,Danmei XIE,Yangheng XIONG,Hengliang ZHANG

Frontiers in Energy 2014, Volume 8, Issue 4,   Pages 459-463 doi: 10.1007/s11708-014-0335-5

Abstract: The operation condition of the cold-end system of a steam turbine has a direct impact on the economyand security of the unit as it is an indispensible auxiliary system of the thermal power unit.The optimization analysis of a 1000 MW ultra-supercritical (USC) unit, the turbine cold-end system, wasperformed utilizing the back propagation (BP) neural network method with genetic algorithm (GA) optimizationthat the optimized parameters were of significance to the performance and economic operation of the system

Keywords： optimization     turbine     cold-end system     BP neural network     genetic algorithm    

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: The collaborative filtering technology used in traditional systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional algorithms, thus leading to the emergence of systems based on . At present, s mostly use deep s to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the . Aimed at this problem, in this paper we propose a feedforward deep method, called the deep association (DAN), which is based on the joint action of multiple categories of information, for implicit feedback . Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint s can provide better performance.

Keywords： Neural network     Deep learning     Deep association neural network (DAN)     Recommendation    

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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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Research on Tracing Evaluation System in Virtual Enterprise Based on Neural Network

Wang Shuo,Tang Xiaowo

Strategic Study of CAE 2003, Volume 5, Issue 4,   Pages 65-69

Abstract:

The paper designed tracing evaluation index system in virtual enterprise and established neural network

Keywords： virtual enterprise     neural network     trace evaluation     system    

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A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 6,   Pages 667-684 doi: 10.1007/s11709-022-0822-4

Abstract: The study proposes a framework combining machine learning (ML) models into a logical hierarchical systemConsequently, a system containing three trained ML models is proposed to first predict the stabilityclassification, RFC) followed by 1) the cantilever top horizontal displacement of sheet wall (artificial neuralnetwork regression models, RANN1) and 2) vertical settlement of soil (RANN2).The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models, based

Keywords： finite element analysis     cantilever sheet wall     machine learning     artificial neural network     random forest    

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: based on understanding dissolution behavior to establish two solubility prediction models by machine learningconsidered in the prediction models, which were constructed by random decision forests and artificial neuralnetwork with optimized data structure and model accuracy.

Keywords： solubility prediction     machine learning     artificial neural network     random decision forests    

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Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

Frontiers of Structural and Civil Engineering   Pages 1281-1294 doi: 10.1007/s11709-023-0975-9

Abstract: assessment method for concrete structures is established using an artificial bee colony backpropagation neuralnetwork algorithm.

Keywords： hydraulic structure     curvature mode     damage detection     artifical neural network     artificial bee colony    

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: In the present study, a new image-based machine learning method is used to predict concrete compressiveThese include support-vector machine model and various deep convolutional neural network models, namelyThe images and corresponding compressive strength were then used to train machine learning models toOverall, the present findings validated the use of machine learning models as an efficient means of estimating

Keywords： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 2,   Pages 284-305 doi: 10.1007/s11709-022-0901-6

Abstract: In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps includeArtificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized

Keywords： compressive strength     self-compacting concrete     artificial neural network     decision tree     CatBoost    

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Title Author Date Type Operation

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

Journal Article

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

Journal Article

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Journal Article

Real-time tool condition monitoring method based on temperature measurement and artificial neural network

Journal Article

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

Journal Article

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neuralnetwork

Journal Article

Optimization of turbine cold-end system based on BP neural network and genetic algorithm

Chang CHEN,Danmei XIE,Yangheng XIONG,Hengliang ZHANG

Journal Article

DAN: a deep association neural network approach for personalization recommendation

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Journal Article

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

Journal Article

Research on Tracing Evaluation System in Virtual Enterprise Based on Neural Network

Wang Shuo,Tang Xiaowo

Journal Article

A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

Journal Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Journal Article

Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

Journal Article

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Journal Article

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Journal Article