Eratosthenes is a research project funded under the Spark funding scheme of the Swiss National Science Foundation (SNSF). The aim of the Spark is to fund postdoctoral researchers to implement "projects that show unconventional thinking and introduce a unique approach". The relevant criteria for the award of Spark grants are the originality/novelty of the idea, the unconventionality of the proposed research project, the scientific quality of the project and the potential for significant impact. Dr. Grigorios Anagnostopoulos, postdoctoral researcher of the dmml group, is the grantee of the Eratosthenes project.
The ambition of the Eratosthenes project is to integrate the recent advancements of the field of generative modelling within the application domains of indoor and outdoor positioning. More specifically, the aim is to mitigate the barrier of the data collection requirement of fingerprinting localization methods through generative modelling.
SNSF webpage of the project: http://p3.snf.ch/project-195964
Related past project: http://dmml.ch/orbiloc/
Related Blog post: http://dmml.ch/outdoor-positioning-for-the-iot-world-2/
The proliferation of Location Based Services through smart devices has supported a variety of services. Unlike outdoor positioning, where systems such as the GPS or Galileo are considered a de facto standard, indoor positioning has presented systems utilizing a big variety of technologies and positioning methods. The main categorical division of these methods is set between ranging and fingerprinting methods. Moreover, the recent emergence of the usage of Internet-of-Things (IoT) technologies and their usage in creating Low Power Wide Area Networks (LPWAN) has shaped a new landscape in the field of outdoor localization. The low-power devices used in LPWANs cannot afford the battery consumption of a Global Navigation Satellite System (GNSS) chip set, such as the GPS. Therefore, an alternative approach is needed in order to localize these low power devices. Similarly, to the indoor positioning, ranging and fingerprinting methods are two main competing approaches for LPWAN positioning as well. Fingerprinting methods generally offer a higher accuracy of localization but suffer from the disadvantage of requiring a tedious and costly data collection phase. Since fingerprinting is a purely data-driven approach, the way data are collected, the volume of the collected data and the way they ... Read more
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.
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.
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.
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.