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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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Theoretical Framework for a Polymorphic Network Environment

Jiangxing Wu,Junfei Li,Penghao Sun,Yuxiang Hu,Ziyong Li,

Engineering doi: 10.1016/j.eng.2024.01.018

Abstract: The question of whether an ideal network exists with global scalability in its full life cycle has alwaysbeen a first-principles problem in the research of network systems and architectures.Thus far, it has not been possible to scientifically practice the design criteria of an ideal networkin a unimorphic network system, making it difficult to adapt to known services with clear applicationsymbiosis and coexistence with the generative network morpha in the spatiotemporal dimensions.

Keywords： Polymorphic network environment Impossible triangle Network development paradigm Future 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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Crack identification in concrete, using digital image correlation and neural network

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 4, Pages 536-550 doi: 10.1007/s11709-024-1013-2

Abstract: Digital image correlation (DIC) technology can provide a large amount of experimental data, and neural network

Keywords： digital image correlation convolutional neural network back propagation neural neural network damage

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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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Diffusion of municipal wastewater treatment technologies in China: a collaboration network perspective

Yang Li, Lei Shi, Yi Qian, Jie Tang

Frontiers of Environmental Science & Engineering 2017, Volume 11, Issue 1, doi: 10.1007/s11783-017-0903-0

Abstract: Real wastewater treatment technology diffusion process was investigated. The research is based on a dataset of 3136 municipal WWTPs and 4634 organizations. A new metric was proposed to measure the importance of a project in diffusion. Important projects usually involve central organizations in collaboration. Organizations become more central by participating in less important projects. The diffusion of municipal wastewater treatment technology is vital for urban environment in developing countries. China has built more than 3000 municipal wastewater treatment plants in the past three decades, which is a good chance to understand how technologies diffused in reality. We used a data-driven approach to explore the relationship between the diffusion of wastewater treatment technologies and collaborations between organizations. A database of 3136 municipal wastewater treatment plants and 4634 collaborating organizations was built and transformed into networks for analysis. We have found that: 1) the diffusion networks are assortative, and the patterns of diffusion vary across technologies; while the collaboration networks are fragmented, and have an assortativity around zero since the 2000s. 2) Important projects in technology diffusion usually involve central organizations in collaboration networks, but organizations become more central in collaboration by doing circumstantial projects in diffusion. 3) The importance of projects in diffusion can be predicted with a Random Forest model at a good accuracy and precision level. Our findings provide a quantitative understanding of the technology diffusion processes, which could be used for water-relevant policy-making and business decisions.

Keywords： Innovation diffusion Collaboration network Wastewater treatment plant Complex network Data driven

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Gradient boosting dendritic network for ultra-short-term PV power prediction

Frontiers in Energy doi: 10.1007/s11708-024-0915-y

Abstract: Based on a gradient boosting strategy and a dendritic network, this paper proposes a novel ensemble predictionmodel, named gradient boosting dendritic network (GBDD) model which can reduce the forecast error by

Keywords： photovoltaic (PV) power prediction dendrite network gradient boosting strategy

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Identifying spreading influence nodes for social networks

Frontiers of Engineering Management 2022, Volume 9, Issue 4, 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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Negative weights in network time model

Zoltán A. VATTAI, Levente MÁLYUSZ

Frontiers of Engineering Management 2022, Volume 9, Issue 2, Pages 268-280 doi: 10.1007/s42524-020-0109-1

Abstract: Previous network techniques (CPM/PERT/PDM) did not support negative parameters and/or loops (potentiallyMonsieur Roy and John Fondahl implicitly introduced negative weights into network techniques to representincorporating the dynamic model of the inner logic of time plan), and a surprisingly flexible and handy networkreview the theoretical possibilities and technical interpretations (and use) of negative weights in network

Keywords： graph technique network technique construction management scheduling

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PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 4, Pages 418-422 doi: 10.1007/s11465-010-0117-7

Abstract: Results show that the neural network PID controller can adapt to the changes in system structure parameters

Keywords： PID neural network membrane structure

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Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 6, doi: 10.1007/s11783-021-1430-6

Abstract:

• UV-vis absorption analyzer was applied in drainage type online recognition.

Keywords： Drainage online recognition UV-vis spectra Derivative spectrum Convolutional neural network

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A new neural-network-based method for structural damage identification in single-layer reticulated shells

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 1, Pages 104-121 doi: 10.1007/s11709-024-1031-0

Abstract: In this paper, a new neural-network-based method for structural damage identification in SLRSs is proposedBased on the damaged region identified previously, a fully connected neural network (FCNN) is constructed

Keywords： single-layer reticulated shell damage identification neural network convolutional neural network