Hancheng Min

Hancheng Min
Ph.D. Candidate
Electrical and Computer Engineering

 

email: hanchmin@jhu.edu

I am a second year Ph.D. student in NetD-lab. Prior to entering Hopkins, I received Master’s degree in Systems Engineering from University of Pennsylvannia and Bachelor’s degree in Automation from Tongji Univerisity, Shanghai.

My research interests are coherent dynamics of large networked systems, and coherence-based reduced model and dynamic equivalence of large networks. 

  1. H. Min and E. Mallada, “Dynamics Concentration of Tightly-Connected Large-Scale Networks,” in 58th IEEE Conference on Decision and Control (CDC), 2019.
    [BibTeX] [Abstract] [Download PDF]
    The ability to achieve coordinated behavior –engineered or emergent– on networked systems has attracted widespread interest over several fields. This has led to remarkable advances on the development of a theoretical understanding of the conditions under which agents within a network can reach agreement (consensus) or develop coordinated behaviors such as synchronization. However, fewer advances have been made toward explaining another commonly observed phenomena in tightly-connected networks systems: output responses of nodes in the networks are almost identical to each other despite heterogeneity in their individual dynamics. In this paper, we leverage tools from high-dimensional probability to provide an initial answer to this phenomena. More precisely, we show that for linear networks of nodal random transfer functions, as the networks size and connectivity grows, every node in the network follows the same response to an input or disturbance — irrespectively of the source of this input. We term this behavior as dynamics concentration as it stems from the fact that the network transfer matrix uniformly converges in probability to a unique dynamic response –i.e., it concentrates– determined by the distribution of the random transfer function of each node. We further discuss the implications of our analysis in the context of model reduction and robustness, and provide numerical evidence that similar phenomena occur in small deterministic networks over a properly defined frequency band.
    @inproceedings{mm2019cdc,
      abstract = {The ability to achieve coordinated behavior --engineered or emergent--  on networked systems has attracted widespread interest over several fields. This has led to remarkable advances on the development of a theoretical understanding of the conditions under which agents within a network can reach agreement (consensus) or develop coordinated behaviors such as synchronization. However, fewer advances have been made toward explaining another commonly observed phenomena in tightly-connected networks systems: output responses of nodes in the networks are almost identical to each other despite heterogeneity in their individual dynamics. In this paper, we leverage tools from high-dimensional probability to provide an initial answer to this phenomena. More precisely, we show that for linear networks of nodal random transfer functions, as the networks size and connectivity grows, every node in the network follows the same response to an input or disturbance -- irrespectively of the source of this input. We term this behavior as dynamics concentration as it stems from the fact that the network transfer matrix uniformly converges in probability to a unique dynamic response --i.e., it concentrates-- determined by the distribution of the random transfer function of each node. We further discuss the implications of our analysis in the context of model reduction and robustness, and provide numerical evidence that similar phenomena occur in small deterministic networks over a properly defined frequency band.},
      author = {Min, Hangcheng and Mallada, Enrique},
      booktitle = {58th IEEE Conference on Decision and Control (CDC)},
      grants = {ARO-W911NF-17-1-0092, CPS-1544771, EPCN-1711188, CAREER-1752362, AMPS-1736448, ENERGISE-DE-EE0008006},
      month = {12},
      pubstate = {presented, submitted Mar. 2019},
      title = {Dynamics Concentration of Tightly-Connected Large-Scale Networks},
      url = {https://mallada.ece.jhu.edu/pubs/2019-CDC-MM.pdf},
      year = {2019}
    }
  2. H. Min, F. Paganini, and E. Mallada, Accurate Reduced Order Models for Coherent Synchronous Generators, 2019, submitted.
    [BibTeX] [Abstract] [Download PDF]
    We introduce a novel framework to approximate the aggregate frequency dynamics of coherent synchronous generators. By leveraging recent results on dynamics concentration of tightly connected networks, we develop a hierarchy of reduced order models –based on frequency weighted balanced truncation– that accurately approximate the aggregate system response. Our results outperform existing aggregation techniques and can be shown to monotonically improve the approximation as the hierarchy order increases.
    @unpublished{mpm2019a-preprint,
      abstract = {We introduce a novel framework to approximate the aggregate frequency dynamics of coherent synchronous generators. By leveraging recent results on dynamics concentration of tightly connected networks, we develop a hierarchy of reduced order models --based on frequency weighted balanced truncation-- that accurately approximate the aggregate system response. Our results outperform existing aggregation techniques and can be shown to monotonically improve the approximation as the hierarchy order increases.},
      author = {Min, Hangcheng and Paganini, Fernando and Mallada, Enrique},
      month = {10},
      title = {Accurate Reduced Order Models for Coherent Synchronous Generators},
      url = {https://mallada.ece.jhu.edu/pubs/2019-Preprint-MPM.pdf},
      year = {2019, submitted}
    }
  3. O. Arslan, H. Min, and D. E. Koditschek, “Voronoi-Based Coverage Control of Pan/Tilt/Zoom Camera Networks,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 5062-5069.
    [BibTeX]
    @INPROCEEDINGS{8460701,
    author={O. {Arslan} and H. {Min} and D. E. {Koditschek}},
    booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},
    title={Voronoi-Based Coverage Control of Pan/Tilt/Zoom Camera Networks},
    year={2018},
    volume={},
    number={},
    pages={5062-5069},
    keywords={cameras;computational geometry;event distribution;activity distribution;reactive coverage control algorithm;greedy gradient algorithms;pan camera network;tilt camera network;zoom camera network;continuous-and discrete-time first-order PTZ camera dynamics;coverage algorithms;locally optimal coverage configuration;first-order PTZ camera dynamics;camera network allocation problem;sensing quality measures;conic Voronoi diagrams;visual sensing quality;total coverage quality;camera orientations;PTZ camera networks;automated active network reconfiguration;flexible visual monitoring;Cameras;Sensors;Heuristic algorithms;Visualization;Resource management;Image resolution;Optimization},
    ISSN={2577-087X},
    url = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1907&context=ese_papers}
    month={May},}
  4. H. Min, “On Balancing Event and Area Coverage in Mobile Sensor Networks,” Master Thesis.
    [BibTeX]
    @MastersThesis{min_MScThesis2018,
    author = {Hancheng Min},
    title = {On Balancing Event and Area Coverage in Mobile Sensor Networks},
    school = {University of Pennsylvania},
    url = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1910&context=ese_papers}
    year = {2018},
    }