Current Projects

  • IAI: Industrial Artificial Intelligence for intelligent machines and manufacturing digitalization

    IAI is an Innosuisse project, funded under the joint call in which partners from South Korea and Switzerland are invited to collaborate. At the Swiss side of the consortium, our team collaborates with ABB, a pioneering technology leader with a vivid interest and a profound knowledge in predictive maintenance. The South Korean side of the consortium is composed by three partners: The SHRM Lab from the Seoul National University contributes its considerable experience in Prognostic and Health Management, the SMLD team of the Dongguk University contributes with its physical modelling and data cleansing experience and, lastly, OnePredict brings its expertise in predictive maintenance. The predictive maintenance field has not yet benefited from the recent advances in deep learning. This is mainly because deep learning is rather data hungry, typically needing tens of thousands of training examples. Real-life maintenance data are composed of large quantities of samples referring to normal operating conditions with only very few samples of abnormal or faulty behaviour. In addition, the real-life samples are specific to the particular operating conditions under which they were collected. A change in the operating environment can have an important effect on the behaviour of the equipment and therefore the monitoring data.    In IAI, we build on the recent advances in deep learning and develop methods which are tailored to the particularities of predictive maintenance. To do so, we study regularization techniques to bring in domain knowledge into the modelling and data augmentation relying either on physical models and simulators or on machine learning conditional generative models to complement the scarce real-life data. The tools that will be developed in this project aim to push the state of the art in the field, by offering significant improvements to the predictive performance, resulting in significant savings in maintenance and operational costs. Read more
  • Rawfie: Road-, Air-, and Water- based Future Internet Experimentation

    RAWFIE (Road-, Air-, and Water- based Future Internet Experimentation) is a project funded by the European Commission (Horizon H2020 program) under the Future Internet Research Experimentation (FIRE+) initiative that aims at providing research facilities for Internet of Things (IoT) devices. The DMML team has been involved in the RAWFIE project since early 2015 and delivered their final contributions in June 2019 as the the project ended. The members of the team invested in the project, Jason and Lionel, were responsible for the design and development of the platform's data analysis tools, enabling experimenters to apply machine learning algorithms on data collected via the platform's unmanned devices. Read more
  • MEDInA: sMart EDge fabric for Iot Applications

    MEDInA is an Innosuisse project, enabling the creation of low cost IoT self-adaptive Machine Learning based applications by developing an Artificial-Intelligence-as-a-Service (AIaaS) framework. Current work by the DMML team includes partnering with SixSq in order to develop an AI solution that runs on edge devices such as a Raspberry Pi 4, providing traffic volume information monitoring, to be used for adaptive smart lighting in Smart Cities. Read more
  • SimGait: Modeling pathological gait resulting from motor impairments

    Within this project we seek to develop machine learning methods for the modelling of pathological human locomotion. This is a collaborative Sinergia project funded by the Swiss National Science Foundation. Read more
  • OrbiLoc: Learning to position IoT devices in outdoor environments

    OrbiLoc is an Innosuisse project in which the Data Mining and Machine Learning (DMML) group collaborates with the company Orbiwise. The goal of this project is to improve the localization accuracy achieved by IoT devices utilizing the LoRa network of Orbiwise. In the context of this project, DMML introduces machine learning techniques for localization, achieving significant performance improvements, having as a baseline the trilateration method currently used by Orbiwise. The percentage of improvement of the localization accuracy achieved so far lays in the range of 30%-55%, depending on the environment of the deployment. Read more