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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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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 7, doi: 10.1007/s11783-023-1688-y

Abstract:

● A novel VMD-IGOA-LSTM model has proposed for the prediction of

Keywords： Grasshopper optimization algorithm     Variational mode decomposition     Long short-term memory neural network    

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

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: ., stacked autoencoders and long short-term memory (LSTM) networks, were used for forecasting the compressiveThe forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectivelyThis study found that the LSTM network achieved better results than the stacked autoencoders.

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

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: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM Research Article

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12,   Pages 1551-1684 doi: 10.1631/FITEE.2000511

Abstract: We present a novel deep neural network framework for learning feature representations directly fromComparing four recurrent neural network structures and four classical machine learning and deep learningThus, this study proves that the application of APSO and LSTM techniques to biometric can achieve a

Keywords： 心电图生物特征；个体身份识别；长短期记忆网络；自适应粒子群优化算法    

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Dynamic time prediction for electric vehicle charging based on charging pattern recognition Research Article

Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 299-313 doi: 10.1631/FITEE.2200212

Abstract: Overcharging is an important safety issue in the charging process of electric vehicle power batteries, and can easily lead to accelerated battery aging and serious safety accidents. It is necessary to accurately predict the vehicle's to effectively prevent the battery from overcharging. Due to the complex structure of the battery pack and various s, the traditional prediction method often encounters modeling difficulties and low accuracy. In response to the above problems, data drivers and machine learning theories are applied. On the basis of fully considering the different electric vehicle battery management system (BMS) s, a prediction method with recognition is proposed. First, an intelligent algorithm based on dynamic weighted density peak clustering (DWDPC) and fusion is proposed to classify vehicle s. Then, on the basis of an improved simplified particle swarm optimization (ISPSO) algorithm, a high-performance prediction method is constructed by fully integrating and a strong tracking filter. Finally, the data run by the actual engineering system are verified for the proposed prediction algorithm. Experimental results show that the new method can effectively distinguish the s of different vehicles, identify the charging characteristics of different electric vehicles, and achieve high prediction accuracy.

Keywords： Charging mode     Charging time     Random forest     Long short-term memory (LSTM)     Simplified particle swarm    

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LDformer: a parallel neural network model for long-term power forecasting

田冉,李新梅,马忠彧,刘颜星,王晶霞,王楚

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1287-1301 doi: 10.1631/FITEE.2200540

Abstract: First, we combine Informer with long short-term memory (LSTM) to obtain deep representation abilities

Keywords： Long-term power forecasting     Long short-term memory (LSTM)     UniDrop     Self-attention mechanism    

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Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China Article

Yuanyuan Man, Qinli Yang, Junming Shao, Guoqing Wang, Linlong Bai, Yunhong Xue

Engineering 2023, Volume 24, Issue 5,   Pages 230-239 doi: 10.1016/j.eng.2021.12.022

Abstract: To address this issue, this study proposes an enhanced long short-term memory (LSTM) model for runoffThe feature extractor consisting of three LSTM networks is established for each meteorological stationResults indicate that the enhanced LSTM model performed well, achieving Nash–Sutcliffe efficiencyBalance Model (AWBM), Sacramento, SimHyd and Tank Model) and the data-driven models (artificial neural networkThe enhanced LSTM with PES as loss function performed best on extreme runoff prediction with a mean NSE

Keywords： Runoff prediction     Long short-term memory     Upper Huai River Basin     Extreme runoff     Loss function    

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Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1273-1286 doi: 10.1631/FITEE.2200304

Abstract: State-of-the-art studies linearly discretize the user’s spatiotemporal information and then use recurrent neural networkWe use the long short-term memory (LSTM) model with an attention mechanism as the basic framework and

Keywords： Point-of-interest recommendation     Spatiotemporal effects     Long short-term memory (LSTM)     Attention mechanism    

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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 applicationdimension reduction method of high-dimensional system and reveal the calculation mechanism of the neural networkWe apply our framework to some network models and a real system of the whole neuron map of CaenorhabditisResult shows that a simple linear mapping relationship exists between network structure and network behaviorin the neural network with high-dimensional and nonlinear characteristics.

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0736-9

Abstract: Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.

Keywords： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1285-1298 doi: 10.1007/s11709-020-0691-7

Abstract: This article intends to model the multiscale constitution using feedforward neural network (FNN) andrecurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict

Keywords： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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Heat, mass, and work exchange networks

Zhiyou CHEN, Jingtao WANG

Frontiers of Chemical Science and Engineering 2012, Volume 6, Issue 4,   Pages 484-502 doi: 10.1007/s11705-012-1221-5

Abstract: This review presents the main works related to each network.

Keywords： process system engineering     integration methods     heat exchange network     mass exchange network     work exchangenetwork    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 814-828 doi: 10.1007/s11465-021-0650-6

Abstract: To address this issue, this paper explores a decision-tree-structured neural network, that is, the deepconvolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings.The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision

Keywords： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network    

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Identifying spreading influence nodes for social networks

Frontiers of Engineering Management   Pages 520-549 doi: 10.1007/s42524-022-0190-8

Abstract: The identification of spreading influence nodes in social networks, which studies how to detect important individuals in human society, has attracted increasing attention from physical and computer science, social science and economics communities. The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence, describe the node’s position, and identify interaction centralities. This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks, emphasizing the contributions from physical perspectives and approaches, including the microstructure-based algorithms, community structure-based algorithms, macrostructure-based algorithms, and machine learning-based algorithms. We introduce diffusion models and performance evaluation metrics, and outline future challenges of the identification of spreading influence nodes.

Keywords： complex network     network science     spreading influence     machine learning    

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Information Network—— Frontier of Information Engineering Science

Zhong Yixin

Strategic Study of CAE 1999, Volume 1, Issue 1,   Pages 24-29

Abstract:

Information Network has been grown up and spread out to the entire globe extremely swiftly in recent

An attempt is made in the paper to establish a new discipline, the information network engineering, based on the above phenomenon.First, the concept of information network is re-defined clearly hereand then the working mechanism of information network is analyzed in depth.As a result of the analyses above, a list of the important issues and directions in information network

Keywords： information network     intelligent productive tools     network age     information network engineering    

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Title Author Date Type Operation

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

Journal Article

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Journal Article

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

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

Journal Article

: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Journal Article

Dynamic time prediction for electric vehicle charging based on charging pattern recognition

Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE

Journal Article

LDformer: a parallel neural network model for long-term power forecasting

田冉,李新梅,马忠彧,刘颜星,王晶霞,王楚

Journal Article

Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China

Yuanyuan Man, Qinli Yang, Junming Shao, Guoqing Wang, Linlong Bai, Yunhong Xue

Journal Article

Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Journal Article

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

Heat, mass, and work exchange networks

Zhiyou CHEN, Jingtao WANG

Journal Article

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Journal Article

Identifying spreading influence nodes for social networks

Journal Article

Information Network—— Frontier of Information Engineering Science

Zhong Yixin

Journal Article