Meet our team
I received my MSc in computer sciences from TU Delft. I am currently working as the final year PhD student at TU Delft railway engineering. My research interests include topics in signal and image processing, vision-based object recognition, and defect detection and classification.
- Signal and image processingrn- Object recognitionrn- Rail defect detection and classification
ADvanced Monitoring of Intelligent Rail infrastructurE (ADMIRE) is a proposed approach for intelligent track monitoring and disruption detection, in partnership with the Dutch rail infrastructure manager: ProRail. The project aims for an integrated diagnosis and decision support system that can take advantage of all measurement data for detection and prediction of track infrastructure defects. This can reduce the maintenance costs (e.g. preventive and corrective action, track maintenance time scheduling, number of work force necessary, etc.) and also increase reliability, safety and passenger comfort of the railway network.
Currently a large amount of unstructured measurements data is available. New measurements are also regularly collected by ProRail and other companies. We apply data mining techniques to extract the most from the data without missing crucial information. Several types of measurements (e.g. ABA and rail images) have been used individually for monitoring. By applying advanced feature reduction and extraction models we propose to use these data together to obtain better prediction results. An essential task of the project is to utilize an stochastic/probabilistic approach for both online and offline defect detection and prediction. The goal is to design and implement a system that can identify different types of track irregularities.
January 1, 2016 by Siamak Hajizadeh
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Siamak Hajizadeh, MSc
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