MERL - Mitsubishi Electric Research Laboratories

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Optimization

Efficient solutions to large-scale problems.

Much of MERL's research activity involves formulating scientific and engineering problems as optimizations, which can be solved in an efficient way. We have developed fundamental algorithms to better solve classic problems, such as quadratic programs and minimum-cost paths. Our work also involves developing theoretical bounds to understand performance limits.

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  • Awards

    •  AWARD   Outstanding Presentation Award at the 28th Conference of Information Processing Society of Japan/Consumer Device & Systems
      Date: October 20, 2020
      Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
      MERL Contacts: Jianlin Guo; Philip Orlik
      Research Areas: Communications, Optimization, Signal Processing
      Brief
      • MELCO and MERL researchers have won "Outstanding Presentation Award" at 28th Conference of Information Processing Society of Japan (IPSJ)/Consumer Device & Systems held on September 29-30, 2020. The paper titled "IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1 GHz Frequency Bands" reports IEEE 802.19.3 standard development on coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. MERL and MELCO have been leading this standard development and made major technical contributions, which propose methods to mitigate interference in smart meter systems. The authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
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    •  AWARD   Best conference paper of IEEE PES-GM 2020
      Date: June 18, 2020
      Awarded to: Tong Huang, Hongbo Sun, K.J. Kim, Daniel Nikovski, Le Xie
      MERL Contacts: Kyeong Jin (K.J.) Kim; Daniel Nikovski; Hongbo Sun
      Research Areas: Data Analytics, Electric Systems, Optimization
      Brief
      • A paper on A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Natural Disasters, written by Tong Huang, a former MERL intern from Texas A&M University, has been selected as one of the Best Conference Papers at the 2020 Power and Energy Society General Meeting (PES-GM). IEEE PES-GM is the flagship conference for the IEEE Power and Energy Society. The work was done in collaboration with Hongbo Sun, K. J. Kim, and Daniel Nikovski from MERL, and Tong's advisor, Prof. Le Xie from Texas A&M University.
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  • News & Events


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  • Internships

    • CA1399: Optimization Algorithms for Stochastic Predictive Control

      MERL is looking for a highly motivated individual to work on tailored numerical optimization algorithms and applications of stochastic learning-based model predictive control (MPC) methods. The research will involve the study and development of novel optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), convex and non-convex optimization, machine learning, numerical optimization and (inverse) optimal control. PhD students in engineering or mathematics with a focus on stochastic (learning-based) MPC or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.

    • MD1406: Numerical Analysis of Electric Machines

      MERL is seeking a motivated and qualified intern to conduct research in the design, modeling and optimization of electrical machines. The ideal candidate should have solid backgrounds in electromagnetic theory, electric machine design, and numerical modeling techniques (including model reduction), research experiences in electric, magnetic, and thermal modeling and analysis of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and optimization techniques is a strong plus. Senior Ph.D. students in electrical or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.

    • CA1260: Model Predictive Control of Hybrid Systems

      The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to work on hybrid model predictive control. The scope of work includes the development of model predictive control algorithms for hybrid dynamical systems, switched systems, and quantized systems, analysis and property proving, and applications in automotive, space systems, and energy systems. PhD students with expertise in some among control, optimization, model predictive control and hybrid systems, and with working knowledge of Matlab implementation are welcome to apply. The expected duration of the internship is 3-6 months with flexible start date.


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  • Recent Publications

    •  Tang, Y., Kojima, K., Koike-Akino, T., Wang, Y., Wu, P., TaherSima, M., Jha, D., Parsons, K., Qi, M., "Generative Deep Learning Model for Inverse Design of Integrated Nanophotonic Devices", Lasers and Photonics Reviews, DOI: 10.1002/lpor.202000287, Vol. 2020, pp. 2000287, October 2020.
      BibTeX TR2020-135 PDF
      • @article{Tang2020oct,
      • author = {Tang, Yingheng and Kojima, Keisuke and Koike-Akino, Toshiaki and Wang, Ye and Wu, Pengxiang and TaherSima, Mohammad and Jha, Devesh and Parsons, Kieran and Qi, Minghao},
      • title = {Generative Deep Learning Model for Inverse Design of Integrated Nanophotonic Devices},
      • journal = {Lasers and Photonics Reviews},
      • year = 2020,
      • volume = 2020,
      • pages = 2000287,
      • month = oct,
      • doi = {10.1002/lpor.202000287},
      • url = {https://www.merl.com/publications/TR2020-135}
      • }
    •  Quirynen, R., Di Cairano, S., ": Block-Structured Preconditioning of Iterative Solvers within a Primal Active-Set Method for fast MPC", Optimal Control Applications & Methods, September 2020.
      BibTeX TR2020-134 PDF
      • @article{Quirynen2020sep,
      • author = {Quirynen, Rien and Di Cairano, Stefano},
      • title = {: Block-Structured Preconditioning of Iterative Solvers within a Primal Active-Set Method for fast MPC},
      • journal = {Optimal Control Applications & Methods},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-134}
      • }
    •  Xia, Y., Wang, P., Berntorp, K., Boufounos, P.T., Orlik, P.V., Svensson, L., Granstrom, K., "Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model", IEEE Radar Conference (RadarCon), September 2020.
      BibTeX TR2020-131 PDF
      • @inproceedings{Xia2020sep,
      • author = {Xia, Yuxuan and Wang, Pu and Berntorp, Karl and Boufounos, Petros T. and Orlik, Philip V. and Svensson, Lennart and Granstrom, Karl},
      • title = {Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model},
      • booktitle = {IEEE Radar Conference (RadarCon)},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-131}
      • }
    •  Kalabic, U., Chiu, M., "Cap-and-trade scheme for ridesharing", Intelligent Transportation Systems Conference, September 2020.
      BibTeX TR2020-129 PDF
      • @inproceedings{Kalabic2020sep,
      • author = {Kalabic, Uros and Chiu, Michael},
      • title = {Cap-and-trade scheme for ridesharing},
      • booktitle = {Intelligent Transportation Systems Conference},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-129}
      • }
    •  Kojima, K., Tang, Y., Koike-Akino, T., Wang, Y., Jha, D., Parsons, K., TaherSima, M., Sang, F., Klamkin, J., Qi, M., "Inverse Design of Nanophotonic Devices using Deep Neural Networks", Asia Communications and Photonics Conference (ACP), September 2020.
      BibTeX TR2020-130 PDF
      • @inproceedings{Kojima2020sep,
      • author = {Kojima, Keisuke and Tang, Yingheng and Koike-Akino, Toshiaki and Wang, Ye and Jha, Devesh and Parsons, Kieran and TaherSima, Mohammad and Sang, Fengqiao and Klamkin, Jonathan and Qi, Minghao},
      • title = {Inverse Design of Nanophotonic Devices using Deep Neural Networks},
      • booktitle = {Asia Communications and Photonics Conference (ACP)},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-130}
      • }
    •  Han, M., Ozdenizci, O., Wang, Y., Koike-Akino, T., Erdogmus, D., "Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction", IEEE Signal Processing Letters, DOI: 10.1109/LSP.2020.3020215, Vol. 27, pp. 1565-1569, September 2020.
      BibTeX TR2020-128 PDF
      • @article{Han2020sep,
      • author = {Han, Mo and Ozdenizci, Ozan and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz},
      • title = {Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction},
      • journal = {IEEE Signal Processing Letters},
      • year = 2020,
      • volume = 27,
      • pages = {1565--1569},
      • month = sep,
      • doi = {10.1109/LSP.2020.3020215},
      • issn = {1558-2361},
      • url = {https://www.merl.com/publications/TR2020-128}
      • }
    •  Guo, J., Nagai, Y., Sumi, T., Orlik, P.V., Yamauchi, T., "Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g", International Workshop on Informatics (IWIN), September 2020.
      BibTeX TR2020-127 PDF
      • @inproceedings{Guo2020sep,
      • author = {Guo, Jianlin and Nagai, Yukimasa and Sumi, Takenori and Orlik, Philip V. and Yamauchi, Takahisa},
      • title = {Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g},
      • booktitle = {International Workshop on Informatics (IWIN)},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-127}
      • }
    •  Chakrabarty, A., Danielson, C., Wang, Y., "Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems", IEEE Conference on Control Technology and Applications, August 2020.
      BibTeX TR2020-116 PDF
      • @inproceedings{Chakrabarty2020aug,
      • author = {Chakrabarty, Ankush and Danielson, Claus and Wang, Yebin},
      • title = {Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems},
      • booktitle = {IEEE Conference on Control Technology and Applications},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-116}
      • }
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