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Study on hydrodynamic and synthetic water quality model for river networks

Zhang Mingliang,Shen Yongming

Strategic Study of CAE 2008, Volume 10, Issue 10,   Pages 78-83

Abstract: for river networks.characteristics of river-junction-river method and the theory of WASP, the synthetic water quality modelis set up for river networks, which includes many contamination variables and considers the transformThis model is applied to simulate four river networks, the results of elevations and flows agree withquality in river networks.

Keywords： Preissmann implicit scheme     river networks     hydrodynamic model     water quality model     WASP model    

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Study and Application of Steady Flow and Unsteady Flow Mathematical Model for Canal Networks

Zhang Mingliang,Shen Yongming

Strategic Study of CAE 2007, Volume 9, Issue 8,   Pages 92-96

Abstract: for one- dimensional river networks and canal networks is developed and the key issues on the model This model is applied to simulating the tree-type irrigation canal networks and complex looped This model is a simple and practical tool for water resource regulation of irrigation canal networks These results show the application value of this model is to set up ecological numerical modelof water quality in river networks and canal networks.

Keywords： Preissmann implicit scheme     canal networks and river networks     discharge distribution     water quality    

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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: The 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 knowledgeyields and properties than the previously introduced self-organizing-map convolutional neural network modelMoreover, 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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The Networks Analysis of Fire Spread among the Rooms

You Yuhang,Li Yuanzhou,Huo Ran

Strategic Study of CAE 2005, Volume 7, Issue 7,   Pages 86-89

Abstract:

This paper introduces the Networks model to analyze the process of fire spread among the rooms.Results indicate the Networks model can predict the possibility and hazard of fire spread among the rooms

Keywords： fire spread     among the rooms     the Networks model     path    

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An Interactive ServiceorientedP2P Networks Architecture Reference Model

Liu Ye,Liu Linfen,Zhuang Yanyan

Strategic Study of CAE 2007, Volume 9, Issue 9,   Pages 72-77

Abstract:

An interactive service- oriented P2P networks architecture (ISPNA) isthe service demand of distributed P2P applications,  and considering the characteristics of P2P networksloose-coupled,  self-organizing,  and scalability,  the key availability enhancing issues of P2P networkssynthetically take into account the omnifarious factors that affect the availability of structure P2P networks

Keywords： P2P networks     architecture     reference model     resource     service    

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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: In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walledbeen considered in this study: those that perform feature extraction by using the convolutional neural networksBased 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% higherHence, the proposed hybrid model provides an efficient way of fusing different sources of process data

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

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Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   Pages 1213-1232 doi: 10.1007/s11709-022-0880-7

Abstract: The present study describes a reliability analysis of the strength model for predicting concrete columnsFabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network modelThe database of 330 samples collected for the training model contains many important parameters, i.e.The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive

Keywords： FRCM     deep neural networks     confinement effect     strength model     confined concrete    

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Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integratedpower distribution networks

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

Abstract: With multiple microgrids (MGs) integrated into power distribution networks in a distributed manner, theHowever, the operation of power distribution networks is challenged by the issues of multiple power flowIn 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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Fault Pattern Recognition of Rolling Bearing Based on Radial Basis Function Neural Networks

Lu Shuang,Zhang Zida,Li Meng

Strategic Study of CAE 2004, Volume 6, Issue 2,   Pages 56-60

Abstract: neural network and on the basis of the feature analysis of vibration signal of rolling bearing, AR modelRadial basis function neural networks is established based on AR model parameters.In the light of the theory of radial basis function neural networks, fault pattern of rolling bearingshow that the recognition of fault pattern of rolling bearing based on radial basis function neural networks

Keywords： rolling bearing     vibration signal     AR model     RBF neural networks     pattern recognition    

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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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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 application. We propose a general mathematical framework, which couples the complex structure of the system with the nonlinear activation function to explore the decoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neural network. We apply our framework to some network models and a real system of the whole neuron map of Caenorhabditis elegans. Result shows that a simple linear mapping relationship exists between network structure and network behavior in the neural network with high-dimensional and nonlinear characteristics. Our simulation and theoretical results fully demonstrate this interesting phenomenon. Our new interpretation mechanism provides not only the potential mathematical calculation principle of neural network but also an effective way to accurately match and predict human brain or animal activities, which can further expand and enrich the interpretable mechanism of artificial neural network in the future.

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing

Tao HUANG, Ying-lei TENG, Meng-ting LIU, Jiang LIU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 1,   Pages 1-11 doi: 10.1631/FITEE.1400129

Abstract: to irregular deployment of small base stations (SBSs), the interference in cognitive heterogeneous networks

Keywords： Cognitive heterogeneous networks     Markov chain     Stochastic geometry     Homogeneous Poisson point process (    

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Evaluation of the situational awareness effects for smart distribution networks under the novel

Leijiao GE, Yuanliang LI, Suxuan LI, Jiebei ZHU, Jun YAN

Frontiers in Energy 2021, Volume 15, Issue 1,   Pages 143-158 doi: 10.1007/s11708-020-0703-2

Abstract: As a key application of smart grid technologies, the smart distribution network (SDN) is expected to have a high diversity of equipment and complexity of operation patterns. Situational awareness (SA), which aims to provide a critical visibility of the SDN, will enable a significant assurance for stable SDN operations. However, the lack of systematic evaluation through the three stages of perception, comprehensive, and prediction may prevent the SA technique from effectively achieving the performance necessary to monitor and respond to events in SDN. To analyze the feasibility and effectiveness of the SA technique for the SDN, a comprehensive evaluation framework with specific performance indicators and systematic weighting methods is proposed in this paper. Besides, to implement the indicator framework while addressing the key issues of human expert scoring ambiguity and the lack of data in specific SDN areas, an improved interval-based analytic hierarchy process-based subjective weighting and a multi-objective programming method-based objective weighting are developed to evaluate the SDN SA performance. In addition, a case study in a real distribution network of Tianjin China is conducted whose outcomes verify the practicality and effectiveness of the proposed SA technique for SDN operating security.

Keywords： distribution networks     operation and maintenance     expert systems    

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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: inference system (ANFIS) were tested and their results were compared to determine the best predictive modelThe 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: obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks

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

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Title Author Date Type Operation

Study on hydrodynamic and synthetic water quality model for river networks

Zhang Mingliang,Shen Yongming

Journal Article

Study and Application of Steady Flow and Unsteady Flow Mathematical Model for Canal Networks

Zhang Mingliang,Shen Yongming

Journal Article

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

Journal Article

The Networks Analysis of Fire Spread among the Rooms

You Yuhang,Li Yuanzhou,Huo Ran

Journal Article

An Interactive ServiceorientedP2P Networks Architecture Reference Model

Liu Ye,Liu Linfen,Zhuang Yanyan

Journal Article

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

Journal Article

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

Journal Article

Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integratedpower distribution networks

Journal Article

Fault Pattern Recognition of Rolling Bearing Based on Radial Basis Function Neural Networks

Lu Shuang,Zhang Zida,Li Meng

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

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing

Tao HUANG, Ying-lei TENG, Meng-ting LIU, Jiang LIU

Journal Article

Evaluation of the situational awareness effects for smart distribution networks under the novel

Leijiao GE, Yuanliang LI, Suxuan LI, Jiebei ZHU, Jun YAN

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