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Developing a power monitoring and protection system for the junction boxes of an experimental seafloorobservatory network

Jun WANG,De-jun LI,Can-jun YANG,Zhi-feng ZHANG,Bo JIN,Yan-hu CHEN

《信息与电子工程前沿（英文）》 2015年第16卷第12期页码 1034-1045 doi: 10.1631/FITEE.1500099

摘要： A power monitoring and protection system based on an embedded processor was designed for the junction boxes (JBs) of an experimental seafloor observatory network in China. The system exhibits high reliability, fast response, and high real-time performance. A two-step power management method which uses metal-oxide-semiconductor field-effect transistors (MOSFETs) and a mechanical contactor in series was adopted to generate a reliable power switch, to limit surge currents and to facilitate automatic protection. Grounding fault diagnosis and environmental monitoring were conducted by designing a grounding fault detection circuit and by using selected sensors, respectively. The data collected from the JBs must be time-stamped for analysis and for correlation with other events and data. A highly precise system time, which is necessary for synchronizing the times within and across nodes, was generated through the IEEE 1588 (precision clock synchronization protocol for networked measurement and control systems) time synchronization method. In this method, time packets were exchanged between the grandmaster clock at the shore station and the slave clock module of the system. All the sections were verified individually in the laboratory prior to a sea trial. Finally, a subsystem for power monitoring and protection was integrated into the complete node system, installed in a frame, and deployed in the South China Sea. Results of the laboratory and sea trial experiments demonstrated that the developed system was effective, stable, reliable, and suitable for continuous deep-sea operation.

关键词： Power monitoring and protection Embedded processor Seafloor observatory network IEEE 1588 Junction boxes

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海洋水下立体观测技术装备发展研究

马蕊,赵修涛,柳存根

《中国工程科学》 2020年第22卷第6期页码 19-25 doi: 10.15302/J-SSCAE-2020.06.003

摘要：

建立水下立体观测网来获得科学、实时、全面的数据，是未来认识、开发、利用海洋的重要方向。本文分析了发展海洋水下立体观测技术装备的需求和必要性，对比了国内外相关装备的发展现状，进一步剖析我国领域发展面临的问题，研判作为海洋水下观测关键环节的传感器技术进展点。研究认为，我国海洋观测平台技术有了很大进步，但在海洋关键传感器、高精度传感器方面依然落后于世界先进水平；海洋观测的大数据与实际需求之间有所脱节，海洋传感器缺乏改进平台支撑。研究提出了支持海洋关键传感器研究成果高效转化、统筹管理国家海洋水下立体观测技术装备、建立海上仪器装备国家公共试验平台等对策建议，以期为相关领域中长期发展提供方向参照。

关键词：海洋观测海底观测网水下移动观测平台水下传感器

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海底不稳定性研究进展及展望

高伟健,李伟

《中国工程科学》 2023年第25卷第3期页码 109-121 doi: 10.15302/J-SSCAE-2023.03.010

摘要：

海底不稳定性及次生海底地质灾害广泛存在于海洋之中，对海岸港口设施、海洋钻井平台、海底管道光缆等海底基础建设颇具威胁。但目前对海底不稳定性的成因机制与主控因素仍知之甚少，为加深对海底不稳定性的认识，本文回顾了海底不稳定性研究进展，梳理了海底不稳定性及次生海底地质灾害的主要类别、全球分布情况和地球物理识别特征，归纳了目前海底不稳定性研究的定量分析方法，进一步分析了其成因机制、控制因素及工程地质灾害风险，探讨了海底陆坡失稳演化过程试验模拟技术的适用范畴与技术瓶颈。最后，从海底不稳定性的致灾机理研究、多源数据智能分析和海底失稳立体监测3 个维度提出了未来海底不稳定性研究的发展方向与对策建议，以期为海底不稳定性的模拟、预测和预警等工作提供指导性建议。

关键词：海底不稳定性；海底地质灾害；成因机制；风险评估；不稳定性分析

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海洋科学装备研究进展与发展建议

宋宪仓,杜君峰,王树青,李华军

《中国工程科学》 2020年第22卷第6期页码 76-83 doi: 10.15302/J-SSCAE-2020.06.010

摘要：

海洋科学装备是知海、用海、护海的重要基础。本文从深入认知海洋、合理利用海洋、积极保护海洋、有效管控海洋四方面对我国海洋科学装备的需求进行了剖析，系统梳理了世界海洋强国在海气界面观测装备、水下移动式观测装备、海底观测网络系统等海洋科学观测平台领域的发展现状，总结了未来海洋科学装备的发展趋势；阐述了我国在海洋遥感卫星、海洋科考船、海洋深潜器、海底观测网络等海洋科学装备领域取得的进展，对标国际先进水平分析了通用技术、核心装备、集成系统等方面存在的差距与不足。研究提出了适合国情的发展建议，包括基础理论研究和关键技术突破、新型海洋科学装备研发和成果转换、海洋科学装备共享机制和综合服务平台、国际合作机制和标准体系建立，以期为海洋科学装备更好服务于国家海洋强国战略实施提供方向参考。

关键词：海洋科学装备深海观测平台海气界面观测水下移动式观测海底观测网络装备研发

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基于可编程电压信号实现海底观测网海缆切换及故障隔离 None

Zhi-feng ZHANG, Yan-hu CHEN, De-jun LI, Bo JIN, Can-jun YANG, Jun WANG

《信息与电子工程前沿（英文）》 2018年第19卷第11期页码 1328-1339 doi: 10.1631/FITEE.1601843

摘要：缆系海底观测网可实现长时间、实时、原位海洋在线观测，在海洋观测领域扮演重要角色。可靠的海缆切换方法对于建立永久、可靠、鲁棒性高的海底观测网是必要的。对比已有海缆切换方法优缺点，针对海底观测网的网络组态提出一种新颖的海缆切换方法。无需配置额外通讯路由，借助已有电力传输缆，传输基于特定序列的可编程电压信号，实现水下分支器与陆地岸基站的通讯。建立系统仿真模型，分析电压信号最大数据位频率，确保准确识别控制命令。最后，在实验室环境下建立基于所提切换方法的水下分支器样机，验证该方法的功能及可靠性。

关键词：缆系海底观测网；海缆切换及故障隔离；可编程电压信号；最大数据位频率

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深碳观测计划——对地球内部碳的十年探索

Craig M. Schiffries, Andrea Johnson Mangum, Jennifer L. Mays, Michelle Hoon-Starr, Robert M. Hazen

《工程（英文）》 2019年第5卷第3期页码 372-378 doi: 10.1016/j.eng.2019.03.004

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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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一种面向恒流输电水下观测网的无级功率重构转换器 Research Article

臧玉嘉1,2,陈燕虎1,杨灿军1,张浩宇1,段志勇1,Gul MUHAMMAD1

《信息与电子工程前沿（英文）》 2021年第22卷第12期页码 1551-1684 doi: 10.1631/FITEE.2100259

摘要：恒流（CC）电能到恒压（CV）电能的转换是恒流输电水下观测网的关键技术之一。该系统通常采用具有高稳定性和高可靠性的并联稳压器以稳定输出电压。然而，并联稳压方法存在高热损耗和低转换效率的缺点。本文对传统并联稳压方法进行改进，提出一种CC/CV转换模块的无级功率重构方法。针对稳定负载或缓慢变化负载的应用场景，介绍两种无级功率重构转换模式：（1）基于单环控制的手动无级功率重构（MSPR）；（2）基于内—外环控制的自动无级功率重构（ASPR）。所述方法在保证系统留有预设功率裕度的同时，可以尽可能减少并联稳压方法中不必要的能量损失。分析了该方法的转换效率，讨论了系统关键参数选择方法。实验结果表明，MSPR和ASPR方法均保留了并联稳压方法的高稳定优点，同时降低了CC/CV转换模块的热耗散，提高了CC/CV转换效率。

关键词：恒流/恒压转换；并联稳压器；无级功率重构；水下观测网

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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