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Documents authored by Li, Hong

Found 3 Possible Name Variants:

Li, Hong

Document
DOI: 10.4230/OASIcs.ASD.2019.2
Feasibility Study and Benchmarking of Embedded MPC for Vehicle Platoons

Authors: Iñaki Martín Soroa, Amr Ibrahim, Dip Goswami, and Hong Li

Published in: OASIcs, Volume 68, Workshop on Autonomous Systems Design (ASD 2019)

Abstract
This paper performs a feasibility analysis of deploying Model Predictive Control (MPC) for vehicle platooning on an On-Board Unit (OBU) and performance benchmarking considering interference from other (system) tasks running on an OBU. MPC is a control strategy that solves an implicit (on-line) or explicit (off-line) optimisation problem for computing the control input in every sample. OBUs have limited computational resources. The challenge is to implement an MPC algorithm on such automotive Electronic Control Units (ECUs) with an acceptable timing behavior. Moreover, we should be able to stop the execution if necessary at the cost of performance. We measured the computational capability of a unit developed by Cohda Wireless and NXP under the influence of its Operating System (OS). Next, we analysed the computational requirements of different state-of-the-art MPC algorithms by estimating their execution times. We use off-the-shelf and free automatic code generators for MPC to run a number of relevant MPC algorithms on the platform. From the results, we conclude that it is feasible to implement MPC on automotive ECUs for vehicle platooning and we further benchmark their performance in terms of MPC parameters such as prediction horizon and system dimension.
Cite as

Iñaki Martín Soroa, Amr Ibrahim, Dip Goswami, and Hong Li. Feasibility Study and Benchmarking of Embedded MPC for Vehicle Platoons. In Workshop on Autonomous Systems Design (ASD 2019). Open Access Series in Informatics (OASIcs), Volume 68, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{martinsoroa_et_al:OASIcs.ASD.2019.2,
  author =	{Mart{\'\i}n Soroa, I\~{n}aki and Ibrahim, Amr and Goswami, Dip and Li, Hong},
  title =	{{Feasibility Study and Benchmarking of Embedded MPC for Vehicle Platoons}},
  booktitle =	{Workshop on Autonomous Systems Design (ASD 2019)},
  pages =	{2:1--2:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-102-3},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{68},
  editor =	{Saidi, Selma and Ernst, Rolf and Ziegenbein, Dirk},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ASD.2019.2},
  URN =		{urn:nbn:de:0030-drops-103359},
  doi =		{10.4230/OASIcs.ASD.2019.2},
  annote =	{Keywords: Model predictive control, vehicle platoon, embedded implementation, code generation}
}

Hong, Liu

Document
DOI: 10.4230/DagSemProc.09181.3
09181 Working Group on Hybridization between R&S, DoE and Optimization

Authors: Chun-Hung Chen, Liu Hong, Paul B. Kantor, David P. Morton, Juta Pichitlamken, and Matthias Seeger

Published in: Dagstuhl Seminar Proceedings, Volume 9181, Sampling-based Optimization in the Presence of Uncertainty (2009)

Abstract
This is the report of the working group on the relation between, or hybrid combination of design experiment optimization and R&S. The rapporteur, Paul Kantor, learned a great deal at the conference which he summarized by sharing the cartoon shown here. ("A student asking the teacher'... may i be excused, my is full" (from a 1986 cartoon by Gary Larson) - omitted here for copyright reasons).
Cite as

Chun-Hung Chen, Liu Hong, Paul B. Kantor, David P. Morton, Juta Pichitlamken, and Matthias Seeger. 09181 Working Group on Hybridization between R&S, DoE and Optimization. In Sampling-based Optimization in the Presence of Uncertainty. Dagstuhl Seminar Proceedings, Volume 9181, pp. 1-14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{chen_et_al:DagSemProc.09181.3,
  author =	{Chen, Chun-Hung and Hong, Liu and Kantor, Paul B. and Morton, David P. and Pichitlamken, Juta and Seeger, Matthias},
  title =	{{09181 Working Group on Hybridization between R\&S, DoE and Optimization}},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  pages =	{1--14},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09181.3},
  URN =		{urn:nbn:de:0030-drops-21172},
  doi =		{10.4230/DagSemProc.09181.3},
  annote =	{Keywords: }
}

Li, Hongbo

Document
DOI: 10.4230/LIPIcs.CP.2022.32
A Portfolio-Based Approach to Select Efficient Variable Ordering Heuristics for Constraint Satisfaction Problems

Authors: Hongbo Li, Yaling Wu, Minghao Yin, and Zhanshan Li

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Variable ordering heuristics (VOH) play a central role in solving Constraint Satisfaction Problems (CSP). The performance of different VOHs may vary greatly in solving the same CSP instance. In this paper, we propose an approach to select efficient VOHs for solving different CSP instances. The approach contains two phases. The first phase is a probing procedure that runs a simple portfolio strategy containing several different VOHs. The portfolio tries to use each of the candidate VOHs to guide backtracking search to solve the CSP instance within a limited number of failures. If the CSP is not solved by the portfolio, one of the candidates is selected for it by analysing the information attached in the search trees generated by the candidates. The second phase uses the selected VOH to guide backtracking search to solve the CSP. The experiments are run with the MiniZinc benchmark suite and four different VOHs which are considered as the state of the art are involved. The results show that the proposed approach finds the best VOH for more than 67% instances and it solves more instances than all the candidate VOHs and an adaptive VOH based on Multi-Armed Bandit. It could be an effective adaptive search strategy for black-box CSP solvers.
Cite as

Hongbo Li, Yaling Wu, Minghao Yin, and Zhanshan Li. A Portfolio-Based Approach to Select Efficient Variable Ordering Heuristics for Constraint Satisfaction Problems. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 32:1-32:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{li_et_al:LIPIcs.CP.2022.32,
  author =	{Li, Hongbo and Wu, Yaling and Yin, Minghao and Li, Zhanshan},
  title =	{{A Portfolio-Based Approach to Select Efficient Variable Ordering Heuristics for Constraint Satisfaction Problems}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{32:1--32:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.32},
  URN =		{urn:nbn:de:0030-drops-166616},
  doi =		{10.4230/LIPIcs.CP.2022.32},
  annote =	{Keywords: Constraint Satisfaction Problem, Variable Ordering Heuristic, Adaptive Search Heuristic, Portfolio}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2021.9
Failure Based Variable Ordering Heuristics for Solving CSPs (Short Paper)

Authors: Hongbo Li, Minghao Yin, and Zhanshan Li

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
Variable ordering heuristics play a central role in solving constraint satisfaction problems. In this paper, we propose failure based variable ordering heuristics. Following the fail first principle, the new heuristics use two aspects of failure information collected during search. The failure rate heuristics consider the failure proportion after the propagations of assignments of variables and the failure length heuristics consider the length of failures, which is the number of fixed variables composing a failure. We performed a vast experiments in 41 problems with 1876 MiniZinc instances. The results show that the failure based heuristics outperform the existing ones including activity-based search, conflict history search, the refined weighted degree and correlation-based search. They can be new candidates of general purpose variable ordering heuristics for black-box CSP solvers.
Cite as

Hongbo Li, Minghao Yin, and Zhanshan Li. Failure Based Variable Ordering Heuristics for Solving CSPs (Short Paper). In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 9:1-9:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{li_et_al:LIPIcs.CP.2021.9,
  author =	{Li, Hongbo and Yin, Minghao and Li, Zhanshan},
  title =	{{Failure Based Variable Ordering Heuristics for Solving CSPs}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{9:1--9:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.9},
  URN =		{urn:nbn:de:0030-drops-153002},
  doi =		{10.4230/LIPIcs.CP.2021.9},
  annote =	{Keywords: Constraint Satisfaction Problem, Variable Ordering Heuristic, Failure Rate, Failure Length}
}
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