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.

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  • Researchers

  • Awards

    •  AWARD    Best Paper Award at SDEMPED 2023
      Date: August 30, 2023
      Awarded to: Bingnan Wang, Hiroshi Inoue, and Makoto Kanemaru
      MERL Contact: Bingnan Wang
      Research Areas: Applied Physics, Data Analytics, Multi-Physical Modeling
      Brief
      • MERL and Mitsubishi Electric's paper titled “Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches” was awarded one of three best paper awards at the 14th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2023). MERL Senior Principal Research Scientist Bingnan Wang presented the paper and received the award at the symposium. Co-authors of the paper include Mitsubishi Electric researchers Hiroshi Inoue and Makoto Kanemaru.

        SDEMPED was established as the only international symposium entirely devoted to the diagnostics of electrical machines, power electronics and drives. It is now a regular biennial event. The 14th version, SDEMPED 2023 was held in Chania, Greece from August 28th to 31st, 2023.
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    •  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.
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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: 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.
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  • News & Events

    •  NEWS    Ankush Chakrabarty co-organized three sessions at the ACC2023, and was nominated for Best Energy Systems Paper.
      Date: June 30, 2023 - June 2, 2023
      Where: San Diego, CA
      MERL Contact: Ankush Chakrabarty
      Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
      Brief
      • Ankush Chakrabarty (researcher, Multiphysical Systems Team) co-organized and spoke at 3 sessions at the 2023 American Control Conference in San Diego, CA. These include: (1) A tutorial session (w/ Stefano Di Cairano) on "Physics Informed Machine Learning for Modeling and Control": an effort with contributions from multiple academic institutes and US research labs; (2) An invited session on "Energy Efficiency in Smart Buildings and Cities" in which his paper (w/ Chris Laughman) on "Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems" was nominated for Best Energy Systems Paper Award; and, (3) A special session on Diversity, Equity, and Inclusion to improve recruitment and retention of underrepresented groups in STEM research.
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    •  TALK    [MERL Seminar Series 2023] Prof. Shaowu Pan presents talk titled Neural Implicit Flow
      Date & Time: Wednesday, March 1, 2023; 1:00 PM
      Speaker: Shaowu Pan, Rensselaer Polytechnic Institute
      MERL Host: Saviz Mowlavi
      Research Areas: Computational Sensing, Data Analytics, Machine Learning
      Abstract
      • High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction to make solutions computationally tractable in real-time. Common existing paradigms for dimensionality reduction include linear methods, such as the singular value decomposition (SVD), and nonlinear methods, such as variants of convolutional autoencoders (CAE). However, these encoding techniques lack the ability to efficiently represent the complexity associated with spatio-temporal data, which often requires variable geometry, non-uniform grid resolution, adaptive meshing, and/or parametric dependencies. To resolve these practical engineering challenges, we propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatial-temporal data. NIF consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which isolates and represents the spatial complexity, and (ii) ParameterNet, which accounts for any other input complexity, including parametric dependencies, time, and sensor measurements. We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and improved generalization performance for sparse reconstruction.
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  • Recent Publications

    •  Wang, B., Inoue, H., Kanemaru, M., "Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches", IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), August 2023.
      BibTeX TR2023-107 PDF
      • @inproceedings{Wang2023aug,
      • author = {Wang, Bingnan and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches},
      • booktitle = {IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)},
      • year = 2023,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2023-107}
      • }
    •  Zhang, J., Nikovski, D., Kojima, T., "3T-Net: Transformer Encoders for Destination Prediction", The Chinese Control Conference, July 2023.
      BibTeX TR2023-094 PDF Presentation
      • @inproceedings{Zhang2023jul3,
      • author = {Zhang, Jing and Nikovski, Daniel and Kojima, Takuro},
      • title = {3T-Net: Transformer Encoders for Destination Prediction},
      • booktitle = {The Chinese Control Conference},
      • year = 2023,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2023-094}
      • }
    •  Zhang, J., Nikovski, D., Nakamura, T., "GPU-APUMPEDI: A Parallel Algorithm for Computing Approximate Pan Matrix Profiles of Time Series", International conference on Time Series and Forecasting, July 2023.
      BibTeX TR2023-091 PDF
      • @inproceedings{Zhang2023jul2,
      • author = {Zhang, Jing and Nikovski, Daniel and Nakamura, Takaaki},
      • title = {GPU-APUMPEDI: A Parallel Algorithm for Computing Approximate Pan Matrix Profiles of Time Series},
      • booktitle = {International conference on Time Series and Forecasting},
      • year = 2023,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2023-091}
      • }
    •  Jeon, E.S., Lohit, S., Anirudh, R., Turaga, P., "Robust Time Series Recovery and Classification Using Test-time Noise Simulator Networks", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2023.
      BibTeX TR2023-021 PDF Presentation
      • @inproceedings{Jeon2023may,
      • author = {Jeon, Eun Som and Lohit, Suhas and Anirudh, Rushil and Turaga, Pavan},
      • title = {Robust Time Series Recovery and Classification Using Test-time Noise Simulator Networks},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-021}
      • }
    •  Sharma, A., Zhang, J., Nikovski, D., Doshi-Velez, F., "Travel-time prediction using neural-network-based mixture models", International Workshop on Statistical Methods and Artificial Intelligence, DOI: 10.1016/​j.procs.2023.03.144, March 2023.
      BibTeX TR2023-012 PDF
      • @inproceedings{Sharma2023mar,
      • author = {Sharma, Abhishek and Zhang, Jing and Nikovski, Daniel and Doshi-Velez, Finale},
      • title = {Travel-time prediction using neural-network-based mixture models},
      • booktitle = {International Workshop on Statistical Methods and Artificial Intelligence},
      • year = 2023,
      • month = mar,
      • doi = {10.1016/j.procs.2023.03.144},
      • url = {https://www.merl.com/publications/TR2023-012}
      • }
    •  Wang, Y., Zhang, J., Nikovski, D., Kojima, T., "Estimating Traffic Density Using Transformer Decoders", International Workshop on Statistical Methods and Artificial Intelligence, DOI: 10.1016/​j.procs.2023.03.143, March 2023.
      BibTeX TR2023-011 PDF
      • @inproceedings{Wang2023mar,
      • author = {Wang, Yinsong and Zhang, Jing and Nikovski, Daniel and Kojima, Takuro},
      • title = {Estimating Traffic Density Using Transformer Decoders},
      • booktitle = {International Workshop on Statistical Methods and Artificial Intelligence},
      • year = 2023,
      • month = mar,
      • doi = {10.1016/j.procs.2023.03.143},
      • url = {https://www.merl.com/publications/TR2023-011}
      • }
    •  Wang, B., Albader, M., Inoue, H., Kanemaru, M., "Induction Motor Eccentricity Fault Analysis and Quantification with Modified Winding Function based Model", International Conference on Electrical Machines and Systems (ICEMS), DOI: 10.1109/​ICEMS56177.2022.9983377, December 2022, pp. 1-6.
      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 Electrical Machines and Systems (ICEMS)},
      • year = 2022,
      • pages = {1--6},
      • month = dec,
      • doi = {10.1109/ICEMS56177.2022.9983377},
      • 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), DOI: 10.1109/​IECON49645.2022.9968912, October 2022, pp. 1-6.
      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 = {IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society},
      • year = 2022,
      • pages = {1--6},
      • month = oct,
      • doi = {10.1109/IECON49645.2022.9968912},
      • url = {https://www.merl.com/publications/TR2022-130}
      • }
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