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A novel power system reconfiguration for a distribution system with minimum load balancing index using

K. Sathish KUMAR, T. JAYABARATHI

《能源前沿（英文）》 2012年 第6卷 第3期   页码 260-265 doi: 10.1007/s11708-012-0196-8

摘要： In this paper, the objective of minimum load balancing index (LBI) for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm (BFOA). The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.

关键词： bacterial foraging optimization algorithm (BFOA)     distribution system     network reconfiguration     load balancing index (LBI)     radial network    

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Power system reconfiguration and loss minimization for a distribution systems using “Catfish PSO” algorithm

K Sathish KUMAR,S NAVEEN

《能源前沿（英文）》 2014年 第8卷 第4期   页码 434-442 doi: 10.1007/s11708-014-0313-y

摘要： One of the very important ways to save electrical energy in the distribution system is network reconfiguration for loss reduction. Distribution networks are built as interconnected mesh networks; however, they are arranged to be radial in operation. The distribution feeder reconfiguration is to find a radial operating structure that optimizes network performance while satisfying operating constraints. The change in network configuration is performed by opening sectionalizing (normally closed) and closing tie (normally opened) switches of the network. These switches are changed in such a way that the radial structure of networks is maintained, all of the loads are energized, power loss is reduced, power quality is enhanced, and system security is increased. Distribution feeder reconfiguration is a complex nonlinear combinatorial problem since the status of the switches is non-differentiable. This paper proposes a new evolutionary algorithm (EA) for solving the distribution feeder reconfiguration (DFR) problem for a 33-bus and a 16-bus sample network, which effectively ensures the loss minimization.

关键词： distribution system reconfiguration (DFR)     power loss reduction     catfish particle swarm optimization (catfish PSO)     radial structure    

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Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

《能源前沿（英文）》 2012年 第6卷 第4期   页码 394-402 doi: 10.1007/s11708-012-0211-0

摘要： This paper investigates the capability of support vector machines (SVM) for prediction of fault classification and the use of the concept of equivalent capacity margin (ECM) for restoration of the power system. The SVM, as a novel type of machine learning based on statistical learning theory, achieves good generalization ability by adopting a structural risk minimization (SRM) induction principle aimed at minimizing a bound on the generalization error of a model rather than the minimization of the error on the training data only. Here, the SVM has been used as a classification. The inputs of the SVM model are power and voltage values. An equation has been developed for the prediction of the fault in the power system based on the developed SVM model. The next steps of this paper are the restoration and reconfiguration by using the ECM concept, the development of a code, and the testing of the results with various load outages, which have been executed for a 12 load system.

关键词： support vector machines (SVM)     structural risk minimization (SRM)     equivalent capacity margin (ECM)     restoration     fault classification    

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协同设计中基于DSM过程重构的研究

徐路宁,张和明,张永康

《中国工程科学》 2006年 第8卷 第5期   页码 52-57

摘要：

将设计结构矩阵方法应用到复杂产品的协同设计开发过程中，通过分析设计行为之间的信息交互获得DSM的量化表达，利用图论中的强连通分支问题算法识别耦合活动集，并在此基础上根据DSM重构原则对DSM进行模块化的过程重构，同时对块内设计任务进行聚类、撕裂，获得协同设计开发过程中的优化重组，为复杂产品的设计开发提供了有效的解决方法，并通过实例说明了该方法的实用性。

关键词： DSM（设计结构矩阵）     过程重构     耦合     撕裂    

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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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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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标题 作者 时间 类型 操作

A novel power system reconfiguration for a distribution system with minimum load balancing index using

K. Sathish KUMAR, T. JAYABARATHI

期刊论文

Power system reconfiguration and loss minimization for a distribution systems using “Catfish PSO” algorithm

K Sathish KUMAR,S NAVEEN

期刊论文

Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

期刊论文

协同设计中基于DSM过程重构的研究

徐路宁,张和明,张永康

期刊论文

Novel interpretable mechanism of neural networks based on network decoupling method

期刊论文

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

期刊论文

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

期刊论文

Heat, mass, and work exchange networks

Zhiyou CHEN, Jingtao WANG

期刊论文

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

期刊论文

Identifying spreading influence nodes for social networks

期刊论文

信息网络——现代信息工程学的前沿

钟义信

期刊论文

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

Yang Li, Lei Shi, Yi Qian, Jie Tang

期刊论文

Negative weights in network time model

Zoltán A. VATTAI, Levente MÁLYUSZ

期刊论文

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

期刊论文

A neural network-based production process modeling and variable importance analysis approach in corn

期刊论文