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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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Predictive model to decouple the contributions of friction and plastic deformation to machined surface

Subhash ANURAG, Yuebin GUO,

《机械工程前沿（英文）》 2010年第5卷第3期页码 247-255 doi: 10.1007/s11465-010-0097-7

摘要： Temperature on the machined surface is critical for surface integrity and the performance of a precision component. However, the temperature of a machined surface is challenging for in-situ measurement. Furthermore, the individual contribution of tool/work friction and plastic deformation of work materials to surface temperature is very difficult to quantify because the measured temperature is always the resultant temperature. This lack of understanding on the temperature distribution blocks the design of effective cutting tool geometries and materials to minimize surface temperature. This study provides a finite element method based on a predictive model to decouple the contributions of tool/work friction and material plastic deformation to surface temperature in a dry cutting process. The study shows that the plastic deformation of work material contributes to the majority of surface temperature, whereas the tool/work friction contribution is secondary. High temperatures are produced when more materials are plowed under the cutting edge. A large tool/work friction leads to higher surface temperatures, and the use of a cutting tool with physical properties in process simulation significantly improves the accuracy of predicted surface temperatures. Residual stress reversal from subsurface maximum residual to surface maximum residual stress may occur when tool/work friction increases.

关键词： surface temperature friction residual stress finite element analysis (FEA) dry cutting tool property

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单边直线感应电机纵向边缘效应的研究

徐伟,孙广生

《中国工程科学》 2007年第9卷第3期页码 21-27

摘要：

单边直线感应电机（SLIM）因结构简单牢固，直接产生前进的电磁推力，在交通中得到广泛应用。由于其磁路断续，电机运行中存在横向和纵向边缘效应；次级的入口和出口因气隙磁链守恒原理会产生涡流，并随速度上升而加大。边缘效应和涡流大小对直线电机气隙磁链产生重要影响，造成气隙有效磁通和牵引力系数降低。探讨直线电机工作特点，得到了电机d－q轴等效电路，通过d轴互感和电阻变化来实时反应第二纵向边缘效应和次级涡流的影响。结合旋转电机转子磁场矢量控制方法，建立了对应的SLIM控制方程。采用转子磁链、速度和电流三个调节器，提出了uds，uqs，iqs实时在线解耦补偿控制方法，对SLIM的推力、d－q轴电压、d－q轴磁链等进行调节和控制，对d轴互感大小进行限制。结果表明，该方法在电机速度突变时减小了力矩脉动，定子电流过渡平滑，速度平稳，改善了SLIM的运行性能，为电机实际运行分析提供了理论依据。

关键词：单边直线感应电机转子磁场定向控制边缘效应解耦补偿

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应用于5G/WLAN频段的MIMO手持天线 Correspondences

Yong CHENG, Jing LU, Bing-qing SHENG

《信息与电子工程前沿（英文）》 2020年第21卷第1期页码 182-187 doi: 10.1631/FITEE.1900478

摘要：提出一种用于便携式终端的多输入多输出（MIMO）天线。手持终端天线的工作频带为5G（3.4–3.8 GHz）和WLAN（5.150–5.925 GHz）。5G和WLAN频段的天线单元交错放置，尽量保证同频单元间距离较远，以减少天线元件间的耦合。所有天线元件回波损耗均大于6 dB。所有元件间隔离度大于14 dB。高频天线辐射效率大于50%，低频天线辐射效率大于40%。所有元件远场增益大于2.2 dBi。

关键词：第五代通信系统（5G）；手持天线；MIMO天线；去耦技术

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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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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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Identifying spreading influence nodes for social networks

《工程管理前沿（英文）》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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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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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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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 neural network-based production process modeling and variable importance analysis approach in corn

《化学科学与工程前沿（英文）》 2023年第17卷第3期页码 358-371 doi: 10.1007/s11705-022-2190-y

摘要： Corn to sugar process has long faced the risks of high energy consumption and thin profits. However, it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes. Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions. In this paper, a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes, which contains data preprocessing, dimensionality reduction, multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method. In the established model, dextrose equivalent value is selected as the output, and 654 sites from the DCS system are selected as the inputs. LASSO analysis is first applied to reduce the data dimension to 155, then the inputs are dimensionalized to 50 by means of genetic algorithm optimization. Ultimately, variable importance analysis is carried out by the extended weight connection method, and 20 of the most important sites are selected for each neural network. The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%, which have a better prediction result than other models, and the 20 most important sites selected have better explicable performance. The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.

关键词： big data corn to sugar factory neural network variable importance analysis

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网络系统行为效用计算——概念与原理 Article

胡昌振

《工程（英文）》 2018年第4卷第1期页码 78-84 doi: 10.1016/j.eng.2018.02.010

摘要：

网络的服务与应用是一个面向业务与任务的行为过程，其度量与评估目前尚处于粗略比较状态。本文把网络行为场景刻画为微分流形，根据光滑微分流形的同胚变换，给出了网络行为的数学定义，提出了网络行为路径及行为效用的数学描述。基于微分几何原理，提出了网络行为效用的计算原理与方法，并构建了基于行为效用的网络攻防对抗评估计算框架，从而奠定了网络行为定量度量和评估的数学基础。

关键词：网络度量评估微分流形网络行为效用网络攻防对抗