MERL - Mitsubishi Electric Research Laboratories

Data Analytics

Learning from data for optimal decisions.

Our data analytics work addresses predictive modeling techniques, including system identification, anomaly detection, feature selection, and time series analysis, as well as methods to solve various decision optimization problems including continuous optimization, combinatorial optimization, and sequential decision making.

  • Quick Links

  • Researchers

  • Awards

    •  AWARD    Mitsubishi Electric US Receives a 2022 CES Innovation Award for Touchless Elevator Control Jointly Developed with MERL
      Date: November 17, 2021
      Awarded to: Elevators and Escalators Division of Mitsubishi Electric US, Inc.
      MERL Contacts: Daniel N. Nikovski; William S. Yerazunis
      Research Areas: Data Analytics, Machine Learning, Signal Processing
      Brief
      • The Elevators and Escalators Division of Mitsubishi Electric US, Inc. has been recognized as a 2022 CES® Innovation Awards honoree for its new PureRide™ Touchless Control for elevators, jointly developed with MERL. Sponsored by the Consumer Technology Association (CTA), the CES Innovation Awards is the largest and most influential technology event in the world. PureRide™ Touchless Control provides a simple, no-touch product that enables users to call an elevator and designate a destination floor by placing a hand or finger over a sensor. MERL initiated the development of PureRide™ in the first weeks of the COVID-19 pandemic by proposing the use of infra-red sensors for operating elevator call buttons, and participated actively in its rapid implementation and commercialization, resulting in a first customer installation in October of 2020.
    •  
    •  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 N. 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.
    •  
    •  AWARD    MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019
      Date: October 10, 2019
      Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
      MERL Contact: Devesh K. Jha
      Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, Robotics
      Brief
      • MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
    •  

    See All Awards for Data Analytics

  • News & Events


    See All News & Events for Data Analytics

  • Internships

    • MD1937: Mechanical vibration analysis

      MERL is looking for a self-motivated intern to work on monitoring of motor drive system via vibration analysis. The ideal candidate would be a Ph.D. candidate in electrical engineering or mechanical engineering with solid research background in signal processing, vibration analysis, and mechanical system. Proficiency in Matlab or other related software is necessary. Experience in machine learning is a plus. The intern is expected to collaborate with MERL researchers to perform model simulation, data analysis, and prepare manuscripts for scientific publications. The total duration is anticipated to be 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

    • ST1863: Radar Perception Testbed Engineering (Undergraduate/Master Students)

      MERL is seeking an undergraduate or master student engineering intern to use MERL''s millimeter radar hardware testbed for experiments and data collection, and to maintain the data preprocessing pipeline from raw data acquisition, data organization, annotation, sanitization, and document preparation. Scripting in Python/MATLAB is required. Previous experience with radio frequency (RF) testbed/evaluation kits is preferred. The duration is from September to December with a flexible start date and a part-time option.

    • MS1903: Bayesian Optimization and MPC for Net-Zero Energy Buildings

      MERL is looking for a highly motivated and qualified candidate to work on Bayesian Optimization and predictive control for net-zero energy buildings. The ideal candidate will have a strong understanding of control, optimization, and/or machine learning with expertise demonstrated via, e.g., publications, in at least one of: Bayesian optimization, (stochastic) model predictive control, reinforcement learning, controller tuning; additional understanding of energy systems is a plus. Hands-on programming experience with numerical optimization solvers and Python is preferred. PhD students are strongly preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible.


    See All Internships for Data Analytics

  • Recent Publications

    •  Wang, B., Albader, M., Inoue, H., Kanemaru, M., "Induction Motor Eccentricity Fault Analysis and Quantification with Modified Winding Function based Model", International Conference on Electric Machines and Systems, December 2022.
      BibTeX TR2022-153 PDF
      • @inproceedings{Wang2022dec,
      • author = {Wang, Bingnan and Albader, Mesaad and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Induction Motor Eccentricity Fault Analysis and Quantification with Modified Winding Function based Model},
      • booktitle = {International Conference on Electric Machines and Systems},
      • year = 2022,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2022-153}
      • }
    •  Wang, B., Lin, C., Inoue, H., Kanemaru, M., "Topological Data Analysis for Electric Motor Eccentricity Fault Detection", Annual Conference of the IEEE Industrial Electronics Society (IECON), October 2022.
      BibTeX TR2022-130 PDF
      • @inproceedings{Wang2022oct2,
      • author = {Wang, Bingnan and Lin, Chungwei and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Topological Data Analysis for Electric Motor Eccentricity Fault Detection},
      • booktitle = {Annual Conference of the IEEE Industrial Electronics Society (IECON)},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-130}
      • }
    •  Tsiligkaridis, A., Zhang, J., Paschalidis, I.C., Taguchi, H., Sakajo, S., Nikovski, D.N., "Context-Aware Destination and Time-To-Destination Prediction Using Machine Learning", IEEE International Smart Cities Conference, DOI: 10.1109/​ISC255366.2022.9922593, September 2022.
      BibTeX TR2022-120 PDF
      • @inproceedings{Tsiligkaridis2022sep,
      • author = {Tsiligkaridis, Athanasios and Zhang, Jing and Paschalidis, Ioannis Ch. and Taguchi, Hiroshi and Sakajo, Satoko and Nikovski, Daniel N.},
      • title = {Context-Aware Destination and Time-To-Destination Prediction Using Machine Learning},
      • booktitle = {IEEE International Smart Cities Conference},
      • year = 2022,
      • month = sep,
      • doi = {10.1109/ISC255366.2022.9922593},
      • url = {https://www.merl.com/publications/TR2022-120}
      • }
    •  Shirsat, A., Sun, H., Kim, K.J., Guo, J., Nikovski, D.N., "ConvEDNet: A Convolutional Energy Disaggregation Network Using Continuous Point-On-Wave Measurements", IEEE PES General Meeting, DOI: 10.1109/​PESGM48719.2022.9916802, July 2022.
      BibTeX TR2022-101 PDF
      • @inproceedings{Shirsat2022jul,
      • author = {Shirsat, Ashwin and Sun, Hongbo and Kim, Kyeong Jin and Guo, Jianlin and Nikovski, Daniel N.},
      • title = {ConvEDNet: A Convolutional Energy Disaggregation Network Using Continuous Point-On-Wave Measurements},
      • booktitle = {2022 IEEE Power & Energy Society General Meeting (PESGM)},
      • year = 2022,
      • month = jul,
      • doi = {10.1109/PESGM48719.2022.9916802},
      • url = {https://www.merl.com/publications/TR2022-101}
      • }
    •  Jha, D.K., Romeres, D., Yerazunis, W.S., Nikovski, D.N., "Imitation and Supervised Learning of Compliance for Robotic Assembly", European Control Conference (ECC), DOI: 10.23919/​ECC55457.2022.9838102, July 2022, pp. 1882-1889.
      BibTeX TR2022-099 PDF Video
      • @inproceedings{Jha2022jul,
      • author = {Jha, Devesh K. and Romeres, Diego and Yerazunis, William S. and Nikovski, Daniel N.},
      • title = {Imitation and Supervised Learning of Compliance for Robotic Assembly},
      • booktitle = {European Control Conference (ECC)},
      • year = 2022,
      • pages = {1882--1889},
      • month = jul,
      • publisher = {IEEE},
      • doi = {10.23919/ECC55457.2022.9838102},
      • isbn = {978-3-9071-4407-7},
      • url = {https://www.merl.com/publications/TR2022-099}
      • }
    •  Zhang, J., Nikovski, D., "APUMPEDI: Approximating Pan Matrix Profiles of Time Series Under Unnormalized Euclidean Distances by Interpolation", International conference on Time Series and Forecasting (ITISE), June 2022.
      BibTeX TR2022-088 PDF
      • @inproceedings{Zhang2022jun,
      • author = {Zhang, Jing and Nikovski, Daniel},
      • title = {APUMPEDI: Approximating Pan Matrix Profiles of Time Series Under Unnormalized Euclidean Distances by Interpolation},
      • booktitle = {International conference on Time Series and Forecasting (ITISE)},
      • year = 2022,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2022-088}
      • }
    •  Sanfelice, R.G., Di Cairano, S., "Reference Governor for Hybrid Dynamical Systems", American Control Conference (ACC), DOI: 10.23919/​ACC53348.2022.9867303, June 2022.
      BibTeX TR2022-061 PDF
      • @inproceedings{Sanfelice2022jun,
      • author = {Sanfelice, Ricardo G. and Di Cairano, Stefano},
      • title = {Reference Governor for Hybrid Dynamical Systems},
      • booktitle = {American Control Conference (ACC)},
      • year = 2022,
      • month = jun,
      • doi = {10.23919/ACC53348.2022.9867303},
      • url = {https://www.merl.com/publications/TR2022-061}
      • }
    •  Zhang, J., Nikovski, D.N., "Algorithms for Fast Computation of Matrix Profiles of Time Series Under Unnormalized Euclidean Distances", International Conference on Applied Statistics and Data Analytics, April 2022.
      BibTeX TR2022-040 PDF
      • @inproceedings{Zhang2022apr,
      • author = {Zhang, Jing and Nikovski, Daniel N.},
      • title = {Algorithms for Fast Computation of Matrix Profiles of Time Series Under Unnormalized Euclidean Distances},
      • booktitle = {International Conference on Applied Statistics and Data Analytics},
      • year = 2022,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2022-040}
      • }
    See All Publications for Data Analytics

  • Videos

  • Software Downloads