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arXiv 2023年

作者： Zhu, Mingzhe Zhu, Liwang Li, Huan Li, Wei Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Shanghai Engineering Research Institute of Blockchains Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Academy for Engineering and Technology Fudan University Shanghai200433 China

Resistance distance has been studied extensively in the past years, with the majority of previous studies devoted to undirected networks, in spite of the fact that various realistic networks are directed. Although several generalizations of resistance distance on directed graphs have been proposed, they either have no physical interpretation or are not a metric. In this paper, we first extend the definition of resistance distance to strongly connected directed graphs based on random walks and show that the two-node resistance distance on directed graphs is a metric. Then, we introduce the Laplacian matrix for directed graphs that subsumes the Laplacian matrix of undirected graphs as a particular case, and use its pseudoinverse to express the two-node resistance distance, and many other relevant quantities derived from resistance distances. Moreover, we define the resistance distance between a vertex and a vertex group on directed graphs and further define a problem of optimally selecting a group of fixed number of nodes, such that their resistance distance is minimized. Since this combinatorial optimization problem is NP-hard, we present a greedy algorithm with a proved approximation ratio, and conduct experiments on model and realistic networks to validate the performance of this approximation algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

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Visualization of the non-dominated solutions in many-objective optimization 4

Visualization of the non-dominated solutions in many-objecti...

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4th IEEE International Conference on Data science in Cyberspace, DSC 2019

作者： Xiong, Minghui Xiong, Wei Jian, Ping Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China

ISBN: (纸本)9781728145280

The high dimensionality of many-objective optimization makes it difficult to represent the relationships between objectives and solutions of such problems. The overlapping and visual confusion problems occur when mapping high-dimensional solution sets to two-dimensional planes in the conventional parallel coordinate plots, which adds difficulty for decision maker choosing a preference solution. This paper introduces a visualization method for many-objective optimization non-dominated solution sets based on fuzzy theory and electromagnetic field clustering. The fuzzy theory is used to classify the non-dominated solution set, and each rating grade denotes the preference degree of decision maker. Visual clustering is conducted by electromagnetic field clustering, the solutions in the same cluster attract each other while solutions between clusters are mutually exclusive. The distribution structure of preference solution is highlighted through brightness gradient enhancement to optimize the visual perception. The experimental results show that the visualization plot of the proposed method is more clear and intuitive than the original plot, which is convenient for decision makers to discover the pattern of solution sets and make final decisions. © 2019 IEEE.

关键词： Visualization

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Spiking Adaptive Dynamic Programming with Poisson Process 12th

Spiking Adaptive Dynamic Programming with Poisson Process

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12th International Conference on Advances in Swarm Intelligence, ICSI 2021

作者： Wei, Qinglai Han, Liyuan Zhang, Tielin The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Research Center for Brain-Inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783030788100

A new iterative spiking adaptive dynamic programming (SADP) algorithm based on the Poisson process for optimal impulsive control problems is investigated with convergence discussion of the iterative process. For a fixed time interval, a 3-tuple can be computed, and then the iterative value functions and control laws can be obtained. Finally, a simulation example verifies the effectiveness of the developed algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Dynamic programming

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Role of Black Carbon in Modulating Aerosol Direct Effects Driven by Air Pollution Controls During 2013-2017 in China

SSRN

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SSRN 2022年

作者： Zhang, Fenfen Xing, Jia Ding, Dian Wang, Jiandong Zheng, Haotian Zhao, Bin Qi, Ling Wang, Shuxiao State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing100084 China Institute for Atmospheric and Earth System Research Faculty of Science University of Helsinki Helsinki00014 Finland School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing210044 China School of Energy and Environmental Engineering University of Science and Technology Beijing Beijing100083 China

Aerosol direct effects (ADEs) can modulate shortwave radiation as well as atmospheric dynamics and air quality. As the key absorbing component of aerosol, the black carbon (BC) largely determines the aerosol optical properties. Therefore, it is expected that BC emission controls might gain co-benefits from the simultaneous reduction of ADEs. To demonstrate such synergy, we quantified the ADEs changes and role of BC controls in China during 2013-2017 using a regional two-way coupled meteorology chemistry transport model. Results suggest that the control action effectively reduced wintertime PM2.5 concentration (-26.0 μg m-3) and associated ADEs. The influence of ADEs on surface shortwave radiation, 2-meter temperature, and planetary boundary layer height was weakened from -16.7 W m-2, -0.20°C, and -15.4 m in 2013 to -11.3 W m-2, -0.06°C, and -10.7 m in 2017, respectively. The enhancement of SO2, NO2, and PM2.5 concentrations due to ADEs was reduced from +3.1%, +5.2%, and +5.4% in 2013, to +2.6%, +4.5%, and +3.3% in 2017, respectively, demonstrating the extra benefit of air pollution controls by reducing ADEs. The BC emission reduced by 12.5% simultaneously along with the effective controls during 2013-2017. Such BC controls contributes 15.6-60.2% of such changes in the ADEs influence on meteorological variables, and 32.6-41.1% on air pollutants. More specially, the effectiveness of collaborative reduction of BC further reduced surface shortwave radiation in China by 3.6 W m-2 in January and 1.0 W m-2 in July, leading to a more weakened ADEs that bring extra benefits in reducing PM2.5 concentrations by 1.8 μg m-3 in January and 0.3 μg m-3 in July. Apparently, BC played a dominant role in modulating the ADEs and associated influences on meteorology and air quality, suggesting a wise control strategy by targeting absorbing component of PM2.5 reduction to address both air pollution and climate change in the future. © 2022, The Authors. All rights reserved.

关键词： Air quality

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Deficient Excitation in Parameter Learning

arXiv

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arXiv 2025年

作者： Cao, Ganghui Wang, Shimin Guay, Martin Wang, Jinzhi Duan, Zhisheng Polycarpou, Marios M. State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Massachusetts Institute of Technology CambridgeMA02139 United States Queen’s University KingstonONK7L 3N6 Canada KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This paper investigates parameter learning problems under deficient excitation (DE). The DE condition is a rank-deficient, and therefore, a more general evolution of the well-known persistent excitation condition. Under the DE condition, a proposed online algorithm is able to calculate the identifiable and non-identifiable subspaces, and finally give an optimal parameter estimate in the sense of least squares. In particular, the learning error within the identifiable subspace exponentially converges to zero in the noise-free case, even without persistent excitation. The DE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary DE conditions. This protocol allows each local estimator to operate locally in its identifiable subspace, and reach a consensus with neighbours in its non-identifiable subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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A review of cooperative path planning of an unmanned aerial vehicle group

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Frontiers of Information technology & Electronic Engineering 2020年第12期21卷 1671-1694页

作者： Hao ZHANG Bin XIN Li-hua DOU Jie CHEN Kaoru HIROTA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081China Department of Computational Intelligence and Systems Science Tokyo Institute of TechnologyTokyo 1528550Japan

As a cutting-edge branch of unmanned aerial vehicle(UAV)technology,the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors,due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks,e.g.,search and rescue,fire-fighting,reconnaissance,and *** path planning(CPP)is a key problem for a UAV group in executing tasks *** this paper,an attempt is made to perform a comprehensive review of the research on CPP for UAV ***,a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements,i.e.,task,UAV group,and environment,as a basis for a comprehensive classification of different types of CPP *** following the proposed framework,a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified ***,a review and a statistical analysis are presented based on the taxonomy,emphasizing the coordinative elements in the existing CPP *** addition,a collection of challenging CPP problems are provided to highlight future research directions.

关键词： Unmanned aerial vehicle group Cooperation Path planning Optimization problem

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Path Planning for Distribution Network Robots with Adaptive Simulated Annealing-Enhanced Artificial Potential Field Method 26

Path Planning for Distribution Network Robots with Adaptive ...

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26th International Conference on Industrial technology, ICIT 2025

作者： Zhang, Baichao Chen, Xin Chen, Jinge Li, Jinbin Wang, Xiaokai Jian, Xu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan430077 China Hubei Fangyuan Dongli Electric Power Science Research Co. Ltd Wuhan430077 China

ISBN: (纸本)9798331521950

In this paper, we propose a hybrid algorithm that combines an improved Artificial Potential Field (APF) method with the Simulated Annealing (SA) algorithm for path planning of an electric power operation robot manipulator in complex distribution grid environments. To address the unreachable target issue inherent in traditional APF, we introduce a distance regulation factor to optimize the repulsive function. This modification allows the manipulator to smoothly approach the target point as it nears, while the repulsion from obstacles gradually decreases. Additionally, to overcome the limitations of the traditional SA algorithm, such as its tendency to get trapped in local minimum solutions and its inefficiency in complex environments, we propose an adaptive temperature rise strategy. This strategy increases the temperature, enhancing the probability of escaping local optimal solutions. When the APF algorithm becomes trapped in a local optimum, the improved SA algorithm is applied to escape the local minimum. Once the local optimum is avoided, the algorithm switches back to APF to continue the path planning process. simulation results demonstrate that the proposed improved APF-SA algorithm adapts effectively to various complex environments, achieving shorter planning times and higher success rates compared to traditional APF and SA algorithms. It successfully resolves the unreachable target and local minimum problems associated with APF. Finally, the feasibility of the proposed APF-SA fusion algorithm is validated through experiments conducted on an electric power operation robot experimental platform for distribution grid applications. © 2025 IEEE.

关键词： Motion planning

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A KDC-enhanced closest point method for thermal analysis on curved geometries using LS-GFDM

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Applied Mathematics Letters 2025年 171卷

作者： Tang, Zhuochao Gu, Jiming Chen, Bin Fu, Zhuojia School of Management Science and Engineering Anhui University of Technology Maanshan243002 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Maanshan243002 China Center for Numerical Simulation Software in Engineering and Sciences College of Mechanics and Engineering Science Hohai University Nanjing211100 China

This paper presents a Krylov-deferred correction (KDC)-enhanced closest point method (CPM) integrated with the least-squares generalized finite difference method (LS-GFDM) for thermal analysis on curved geometries. To overcome the inherent limitations of conventional single-step/multi-step temporal schemes in long-time simulations, we systematically unify three advanced components: (i) CPM's embedding strategy that transforms surface PDEs into extended problems over Cartesian grids, (ii) LS-GFDM's spatial discretization employing dual point sets to enhance applicability beyond conventional GFDM, and (iii) KDC's predictor–corrector framework that iteratively refines temporal solutions to achieve high-order accuracy. Building upon our previous work (Tang et al. 2025) where LS-GFDM was integrated with CPM using explicit Euler temporal schemes, this study constitutes a critical advancement by replacing low-order temporal discretization with the KDC methodology. Numerical experiments demonstrate that our KDC-CPM-LSGFDM framework achieves superior numerical performance. © 2025 Elsevier Ltd

关键词： Least squares approximations

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Artificial intelligence in gastric cancer:applications and challenges

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Gastroenterology Report 2022年第1期10卷 227-242页

作者： Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian School of Artificial Intelligence University of Chinese Academy of SciencesBeijingP.R.China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingP.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Radiology DepartmentPeking University Cancer Hospital&InstituteBeijingP.R.China College of Medicine and Biological Information Engineering School Northeastern UniversityShenyangLiaoningP.R.China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingP.R.China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxiP.R.China

Gastric cancer(GC)is one of the most common malignant tumors with high *** diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical ***,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer ***,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data *** are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including ***,we aim to provide a broad framework to summarize current research on AI in *** the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological *** the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype *** treatment decisions,AI can help with surgical margin determination and prognosis ***,current approaches are challenged by data scarcity and poor *** tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

关键词： gastric cancer artificial intelligence radiomics endoscopy computed tomography pathology

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Distributed Containment Control of Markov Jump Multi-agent systems under Asynchronous Communications

Distributed Containment Control of Markov Jump Multi-agent S...

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第40届中国控制会议

作者： Chao Ma Yidao Ji Hang Fu Wei Wu School of Mechanical Engineering University of Science and Technology Beijing School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

This paper studies the distributed containment control issue for general Markov jump multi-agent systems. More specifically, a novel asynchronous mode-dependent communication scheme is proposed, which is based on observed communication mode information. By means of mode-dependent Lyapunov method, sufficient containment conditions are derived and communication protocol gains are further designed by convex optimization. A simulation example is finally presented to validate the correctness of our developed control scheme.

关键词： Distributed containment Markov jump multi-agent systems asynchronous communications

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