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Reliability improvement of gas discharge tube by suppressing the formation of short-circuit pathways
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作者 Lingyun CHENG Nianwen XIANG +7 位作者 Kejie LI Weijiang CHEN Kai BIAN Jin YANG Zongqi XU congying han Hongyang GU Chun YANG 《Plasma Science and Technology》 SCIE EI CAS CSCD 2022年第3期138-151,共14页
After cumulative discharge of gas discharge tube(GDT),it is easy to form a short circuit pathway between the two electrodes,which increases the failure risk and causes severe influences on the protected object.To redu... After cumulative discharge of gas discharge tube(GDT),it is easy to form a short circuit pathway between the two electrodes,which increases the failure risk and causes severe influences on the protected object.To reduce the failure risk of GDT and improve cumulative discharge times before failure,this work aims to suppress the formation of two short-circuit pathways by optimizing the tube wall structure,the electrode materials and the electrode structure.A total of five improved GDT samples are designed by focusing on the insulation resistance change that occurs after the improvement;then,by combining these designs with the microscopic morphology changes inside the cavity and the differences in deposition composition,the reasons for the differences in the GDT failure risk are also analyzed.The experimental results show that compared with GDT of traditional structure and material,the method of adding grooves at both ends of the tube wall can effectively block the deposition pathway of the tube wall,and the cumulative discharge time before device failure is increased by 149%.On this basis,when the iron-nickel electrode is replaced with a tungsten-copper electrode,the difference in the electrode’s surface splash characteristics further extends the discharge time before failure by 183%.In addition,when compared with the traditional electrode structure,the method of adding an annular structure at the electrode edge to block the splashing pathway for the particles on the electrode surface shows no positive effect,and the cumulative discharge time before the failure of the two structures is reduced by 22.8%and 49.7%,respectively.Among these improved structures,the samples with grooves at both ends of the tube wall and tungsten-copper as their electrode material have the lowest failure risk. 展开更多
关键词 gas discharge tube short-circuit failure tube wall groove tungsten-copper electrode failure risk
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An Energy-Based Centrality for Electrical Networks
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作者 Ruiyuan Kong congying han +1 位作者 Tiande Guo Wei Pei 《Energy and Power Engineering》 2013年第4期597-602,共6页
We present an energy-based method to estimate centrality in electrical networks. Here the energy between a pair of vertices denotes by the effective resistance between them. If there is only one generation and one loa... We present an energy-based method to estimate centrality in electrical networks. Here the energy between a pair of vertices denotes by the effective resistance between them. If there is only one generation and one load, then the centrality of an edge in our method is the difference between the energy of network after deleting the edge and that of the original network. Compared with the local current-flow betweenness on the IEEE 14-bus system, we have an interesting discovery that our proposed centrality is closely related to it in the sense of that the significance of edges under the two measures are very similar. 展开更多
关键词 CENTRALITY ENERGY EFFECTIVE RESISTANCE Current-flow BETWEENNESS
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NeuroPrim:An attention-based model for solving NP-hard spanning tree problems 被引量:1
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作者 Yuchen Shi congying han Tiande Guo 《Science China Mathematics》 SCIE CSCD 2024年第6期1359-1376,共18页
Spanning tree problems with specialized constraints can be difficult to solve in real-world scenarios,often requiring intricate algorithmic design and exponential time.Recently,there has been growing interest in end-t... Spanning tree problems with specialized constraints can be difficult to solve in real-world scenarios,often requiring intricate algorithmic design and exponential time.Recently,there has been growing interest in end-to-end deep neural networks for solving routing problems.However,such methods typically produce sequences of vertices,which make it difficult to apply them to general combinatorial optimization problems where the solution set consists of edges,as in various spanning tree problems.In this paper,we propose NeuroPrim,a novel framework for solving various spanning tree problems by defining a Markov decision process for general combinatorial optimization problems on graphs.Our approach reduces the action and state space using Prim's algorithm and trains the resulting model using REINFORCE.We apply our framework to three difficult problems on the Euclidean space:the degree-constrained minimum spanning tree problem,the minimum routing cost spanning tree problem and the Steiner tree problem in graphs.Experimental results on literature instances demonstrate that our model outperforms strong heuristics and achieves small optimality gaps of up to 250 vertices.Additionally,we find that our model has strong generalization ability with no significant degradation observed on problem instances as large as 1,000.Our results suggest that our framework can be effective for solving a wide range of combinatorial optimization problems beyond spanning tree problems. 展开更多
关键词 degree-constrained minimum spanning tree problem minimum routing cost spanning tree problem Steiner tree problem in graphs Prim's algorithm reinforcement learning
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Optimal pivot path of the simplex method for linear programming based on reinforcement learning 被引量:1
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作者 Anqi Li Tiande Guo +2 位作者 congying han Bonan Li Haoran Li 《Science China Mathematics》 SCIE CSCD 2024年第6期1263-1286,共24页
Based on the existing pivot rules,the simplex method for linear programming is not polynomial in the worst case.Therefore,the optimal pivot of the simplex method is crucial.In this paper,we propose the optimal rule to... Based on the existing pivot rules,the simplex method for linear programming is not polynomial in the worst case.Therefore,the optimal pivot of the simplex method is crucial.In this paper,we propose the optimal rule to find all the shortest pivot paths of the simplex method for linear programming problems based on Monte Carlo tree search.Specifically,we first propose the SimplexPseudoTree to transfer the simplex method into tree search mode while avoiding repeated basis variables.Secondly,we propose four reinforcement learning models with two actions and two rewards to make the Monte Carlo tree search suitable for the simplex method.Thirdly,we set a new action selection criterion to ameliorate the inaccurate evaluation in the initial exploration.It is proved that when the number of vertices in the feasible region is C_(n)^(m),our method can generate all the shortest pivot paths,which is the polynomial of the number of variables.In addition,we experimentally validate that the proposed schedule can avoid unnecessary search and provide the optimal pivot path.Furthermore,this method can provide the best pivot labels for all kinds of supervised learning methods to solve linear programming problems. 展开更多
关键词 simplex method linear programming pivot rules reinforcement learning
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逐层数据再表达的前后端融合学习的理论及其模型和算法 被引量:3
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作者 郭田德 韩丛英 李明强 《中国科学:信息科学》 CSCD 北大核心 2019年第6期739-759,共21页
基于学习的两个主要研究内容,本文提出了学习的二元分层模式,给出了前端学习、后端学习、前后端组合学习和前后端融合学习的概念,构建了前后端融合学习的理论框架与最优化模型;针对前端学习,模拟大脑的分级工作机制,提出了数据与模型混... 基于学习的两个主要研究内容,本文提出了学习的二元分层模式,给出了前端学习、后端学习、前后端组合学习和前后端融合学习的概念,构建了前后端融合学习的理论框架与最优化模型;针对前端学习,模拟大脑的分级工作机制,提出了数据与模型混合驱动的逐层数据再表达的方法;最后,以视觉(图像)学习为例,本文给出了一种数据与模型混合驱动的逐层数据再表达的具体方法. 展开更多
关键词 机器学习 模式识别 数据表达 数据与模型混合驱动
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一类随机方差缩减算法的分析与改进 被引量:3
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作者 刘彦 郭田德 韩丛英 《中国科学:数学》 CSCD 北大核心 2021年第9期1433-1450,共18页
近年来,随机方差缩减类算法在求解机器学习中的大规模优化问题时得到了广泛应用.但是如何选择此类算法的合适步长依然是值得研究的问题.受启发于结合Barzilai-Borwein步长的随机方差缩减梯度(stochastic variance reduced gradient with... 近年来,随机方差缩减类算法在求解机器学习中的大规模优化问题时得到了广泛应用.但是如何选择此类算法的合适步长依然是值得研究的问题.受启发于结合Barzilai-Borwein步长的随机方差缩减梯度(stochastic variance reduced gradient with Barzilai-Borwein step size,SVRG-BB)算法,本文针对方差缩减类算法提出基于局部Lipschitz常数估计的自适应步长,并通过构建一个极小极大化问题给出该步长应用于不同算法时的参数选取方法.然后将该步长与随机递归梯度算法(stochastic recursive gradient algorithm,SARAH)和随机方差缩减(stochastic variance reduced gradient,SVRG)算法相结合,分别提出结合自适应步长的随机递归梯度(SARAH with adaptive step size,SARAH-AS)方法和结合自适应步长的随机方差缩减梯度(SVRG with adaptive step size,SVRG-AS)算法,并且在强凸假设下证明以上算法点距离序列的线性收敛性质.此外,本文还提供一个新颖的视角揭示为什么SARAH+算法是有效的.在公开数据集上的数值实验结果表明本文提出的自适应步长在方差缩减类算法中表现良好. 展开更多
关键词 随机方差缩减类算法 自适应步长 线性收敛率
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人工智能机理解释与数学方法探讨 被引量:11
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作者 郭田德 韩丛英 《中国科学:数学》 CSCD 北大核心 2020年第11期1541-1578,共38页
人工智能是一门交叉学科,融合了脑认知科学、心理学、数学(包括统计学)、信息科学、计算机科学等诸多学科.目前,以大数据驱动的深度学习为代表的人工智能发展迅速,在许多领域有广泛的应用.但是,人工智能现在最大的缺陷是没有理论,基本... 人工智能是一门交叉学科,融合了脑认知科学、心理学、数学(包括统计学)、信息科学、计算机科学等诸多学科.目前,以大数据驱动的深度学习为代表的人工智能发展迅速,在许多领域有广泛的应用.但是,人工智能现在最大的缺陷是没有理论,基本上还处于实验科学阶段.探讨人工智能的机理,进行其理论研究,对人工智能的进一步发展至关重要.为了揭示人工智能的本质,进一步促进人工智能的理论研究,本文从认知生物学和认知心理学两个层面,探讨现有的三个主要的人工智能学派—联结主义、符号主义和行为主义—所蕴含的机理和具体实现的数学理论和方法,试图解释人工智能的本质,给出未来可解释的和具有创造性的人工智能的一些可能的基础研究方法和研究方向. 展开更多
关键词 人工智能 数学 认知生物学 认知心理学
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