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: 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

    •  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.
    •  
    •  NEWS    Jianlin Guo recently delivered an invited talk at 2022 6th International Conference on Intelligent Manufacturing and Automation Engineering
      Date: December 15, 2022 - December 17, 2022
      MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons
      Research Areas: Artificial Intelligence, Data Analytics, Machine Learning
      Brief
      • The performance of manufacturing systems is heavily affected by downtime – the time period that the system halts production due to system failure, anomalous operation, or intrusion. Therefore, it is crucial to detect and diagnose anomalies to allow predictive maintenance or intrusion detection to reduce downtime. This talk, titled "Anomaly detection and diagnosis in manufacturing systems using autoencoder", focuses on tackling the challenges arising from predictive maintenance in manufacturing systems. It presents a structured autoencoder and a pre-processed autoencoder for accurate anomaly detection, as well as a statistical-based algorithm and an autoencoder-based algorithm for anomaly diagnosis.
    •  

    See All News & Events for Data Analytics

  • Recent Publications

    •  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, 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,
      • 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, 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,
      • 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 Electric Machines and Systems, 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 = {2022 25th 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}
      • }
    •  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}
      • }
    See All Publications for Data Analytics

  • Videos

  • Software Downloads