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Novel interpretable mechanism of neural networks based on network decoupling method

《工程管理前沿（英文）》 2021年第8卷第4期页码 572-581 doi: 10.1007/s42524-021-0169-x

摘要： 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.

关键词： 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

《机械工程前沿（英文）》 2023年第18卷第2期 doi: 10.1007/s11465-022-0736-9

摘要： 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.

关键词： 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,

《工程（英文）》 doi: 10.1016/j.eng.2024.01.018

摘要： The question of whether an ideal network exists with global scalability in its full life cycle has always been 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 network in a unimorphic network system, making it difficult to adapt to known services with clear application scenarios while supporting the ever-growing future services with unexpected characteristics. Here, we theoretically prove that no unimorphic network system can simultaneously meet the scalability requirement in a full cycle in three dimensions—the service-level agreement (S), multiplexity (M), and variousness (V)—which we name as the “impossible SMV triangle” dilemma. It is only by transforming the current network development paradigm that the contradiction between global scalability and a unified network infrastructure can be resolved from the perspectives of thinking, methodology, and practice norms. In this paper, we propose a theoretical framework called the polymorphic network environment (PNE), the first principle of which is to separate or decouple application network systems from the infrastructure environment and, under the given resource conditions, use core technologies such as the elementization of network baselines, the dynamic aggregation of resources, and collaborative software and hardware arrangements to generate the capability of the “network of networks.” This makes it possible to construct an ideal network system that is designed for change and capable of symbiosis and coexistence with the generative network morpha in the spatiotemporal dimensions. An environment test for principle verification shows that the generated representative application network modalities can not only coexist without mutual influence but also independently match well-defined multimedia services or custom services under the constraints of technical and economic indicators.

关键词： 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

《结构与土木工程前沿（英文）》 2020年第14卷第6期页码 1285-1298 doi: 10.1007/s11709-020-0691-7

摘要： Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.

关键词： multiscale method constitutive model feedforward neural network recurrent neural network

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Heat, mass, and work exchange networks

Zhiyou CHEN, Jingtao WANG

《化学科学与工程前沿（英文）》 2012年第6卷第4期页码 484-502 doi: 10.1007/s11705-012-1221-5

摘要： Heat (energy), water (mass), and work (pressure) are the most fundamental utilities for operation units in chemical plants. To reduce energy consumption and diminish environment hazards, various integration methods have been developed. The application of heat exchange networks (HENs), mass exchange networks (MENs), water allocation heat exchange networks (WAHENs) and work exchange networks (WENs) have resulted in the significant saving of energy and water. This review presents the main works related to each network. The similarities and differences of these networks are also discussed. Through comparing and discussing these different networks, this review inspires researchers to propose more efficient and convenient methods for the design of existing exchange networks and even new types of networks including multi-objective networks for the system integration in order to enhance the optimization and controllability of processes.

关键词： process system engineering integration methods heat exchange network mass exchange network work exchange network

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

《机械工程前沿（英文）》 2021年第16卷第4期页码 814-828 doi: 10.1007/s11465-021-0650-6

摘要： The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional 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 tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

关键词： bearing cross-severity fault diagnosis hierarchical fault diagnosis convolutional neural network decision tree

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Crack identification in concrete, using digital image correlation and neural network

《结构与土木工程前沿（英文）》 2024年第18卷第4期页码 536-550 doi: 10.1007/s11709-024-1013-2

摘要： In engineering applications, concrete crack monitoring is very important. Traditional methods are of low efficiency, low accuracy, have poor timeliness, and are applicable in only a limited number of scenarios. Therefore, more comprehensive detection of concrete damage under different scenarios is of high value for practical engineering applications. Digital image correlation (DIC) technology can provide a large amount of experimental data, and neural network (NN) can process very rich data. Therefore, NN, including convolutional neural networks (CNN) and back propagation neural networks (BP), can be combined with DIC technology to analyze experimental data of three-point bending of plain concrete and four-point bending of reinforced concrete. In addition, strain parameters can be used for training, and displacement parameters can be added for comprehensive consideration. The data obtained by DIC technology are grouped for training, and the recognition results of NN show that the combination of strain and displacement parameters, i.e., the response of specimen surface and whole body, can make results more objective and comprehensive. The identification results obtained by CNN and BP show that these technologies can accurately identify cracks. The identification results for reinforced concrete specimens are less affected by noise than those of plain concrete specimens. CNN is more convenient because it can identify some features directly from images, recognizing the cracks formed by macro development. BP can issue early warning of the microscopic cracks, but it requires a large amount of data and computation. It can be seen that CNN is more intuitive and efficient in image processing, and is suitable when low accuracy is adequate, while BP is suitable for occasions with greater accuracy requirements. The two tools have advantages in different situations, and together they can play an important role in engineering monitoring.

关键词： digital image correlation convolutional neural network back propagation neural neural network damage detection concrete

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信息网络——现代信息工程学的前沿

钟义信

《中国工程科学》 1999年第1卷第1期页码 24-29

摘要：

信息网络正在各地迅猛崛起，并以史所罕见的规模和速度生长成为世界性社会基础结构，深刻地改变着人们的生产方式、工作方式、学习方式、交往方式、生活方式和思维方式，成为工程学界以至整个社会普遍关注的集点、热点和前沿。文章旨在从理论上廓清信息网络的概念，阐明为什么信息网络对于科学技术的进步、对于世界经济和人类社会的发展能够产生如此巨大和深远的作用与影响。在此基础上，论述信息网络在现代工程学中的作用与地位，以及信息网络工程学在当前的主要研究内容和方向。

关键词：信息网络智能化社会生产工具网络时代信息网络工程学

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

Yang Li, Lei Shi, Yi Qian, Jie Tang

《环境科学与工程前沿（英文）》 2017年第11卷第1期 doi: 10.1007/s11783-017-0903-0

摘要： 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.

关键词： 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

《能源前沿（英文）》 doi: 10.1007/s11708-024-0915-y

摘要： To achieve effective intraday dispatch of photovoltaic (PV) power generation systems, a reliable ultra-short-term power generation forecasting model is required. Based on a gradient boosting strategy and a dendritic network, this paper proposes a novel ensemble prediction model, named gradient boosting dendritic network (GBDD) model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation. Unlike other machine learning models, the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation. In addition, based on the structure of GBDD, this paper proposes a strategy that can improve the prediction performance of other types of prediction models. The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models. The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.

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

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Identifying spreading influence nodes for social networks

《工程管理前沿（英文）》 2022年第9卷第4期页码 520-549 doi: 10.1007/s42524-022-0190-8

摘要： 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.

关键词： complex network network science spreading influence machine learning

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Negative weights in network time model

Zoltán A. VATTAI, Levente MÁLYUSZ

《工程管理前沿（英文）》 2022年第9卷第2期页码 268-280 doi: 10.1007/s42524-020-0109-1

摘要： Time does not go backward. A negative duration, such as “time period” at first sight is difficult to interpret. Previous network techniques (CPM/PERT/PDM) did not support negative parameters and/or loops (potentially necessitating recursive calculations) in the model because of the limited computing and data storage capabilities of early computers. Monsieur Roy and John Fondahl implicitly introduced negative weights into network techniques to represent activities with fixed or estimated durations (MPM/PDM). Subsequently, the introduction of negative lead and/or lag times by software developers (IBM) apparently overcome the limitation of not allowing negative time parameters in time model. Referring to general digraph (Event on Node) representation where activities are represented by pairs of nodes and pairwise relative time restrictions are represented by weighted arrows, we can release most restraints in constructing the graph structure (incorporating the dynamic model of the inner logic of time plan), and a surprisingly flexible and handy network model can be developed that provides all the advantages of the abovementioned techniques. This paper aims to review the theoretical possibilities and technical interpretations (and use) of negative weights in network time models and discuss approximately 20 types of time-based restrictions among the activities of construction projects. We focus on pure relative time models, without considering other restrictions (such as calendar data, time-cost trade-off, resource allocation or other constraints).

关键词： 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

《机械工程前沿（英文）》 2010年第5卷第4期页码 418-422 doi: 10.1007/s11465-010-0117-7

摘要： Because it is difficult for the traditional PID algorithm for nonlinear time-variant control objects to obtain satisfactory control results, this paper studies a neuron PID controller. The neuron PID controller makes use of neuron self-learning ability, complies with certain optimum indicators, and automatically adjusts the parameters of the PID controller and makes them adapt to changes in the controlled object and the input reference signals. The PID controller is used to control a nonlinear time-variant membrane structure inflation system. Results show that the neural network PID controller can adapt to the changes in system structure parameters and fast track the changes in the input signal with high control precision.

关键词： PID neural network membrane structure

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

《环境科学与工程前沿（英文）》 2021年第15卷第6期 doi: 10.1007/s11783-021-1430-6

摘要：

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

关键词： 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

《结构与土木工程前沿（英文）》 2024年第18卷第1期页码 104-121 doi: 10.1007/s11709-024-1031-0

摘要： Single-layer reticulated shells (SLRSs) find widespread application in the roofs of crucial public structures, such as gymnasiums and exhibition center. In this paper, a new neural-network-based method for structural damage identification in SLRSs is proposed. First, a damage vector index, NDL, that is related only to the damage localization, is proposed for SLRSs, and a damage data set is constructed from NDL data. On the basis of visualization of the NDL damage data set, the structural damaged region locations are identified using convolutional neural networks (CNNs). By cross-dividing the damaged region locations and using parallel CNNs for each regional location, the damaged region locations can be quickly and efficiently identified and the undamaged region locations can be eliminated. Second, a damage vector index, DS, that is related to the damage location and damage degree, is proposed for SLRSs. Based on the damaged region identified previously, a fully connected neural network (FCNN) is constructed to identify the location and damage degree of members. The effectiveness and reliability of the proposed method are verified by considering a numerical case of a spherical SLRS. The calculation results showed that the proposed method can quickly eliminate candidate locations of potential damaged region locations and precisely determine the location and damage degree of members.

关键词： single-layer reticulated shell damage identification neural network convolutional neural network cross-partitioning method