Open semester project and master thesis opportunities for students:
This project is part of a collaboration between the IMOS lab and Matterhorn Gotthard Bahn, a railway company operating in the Swiss Alps. The student will work on developing computer vision algorithms for automated visual inspection of retaining walls around railway tracks. Retaining (or supporting) walls are crucial infrastructure elements responsible for maintaining the structural integrity of terrains around railway tracks and ensure safe operation. They are subject to wear and damages including cracks, concrete cancer (i.e., alkali–silica reaction), displacements, erosion and water infiltration. Images of retaining walls have already been collected and labels are available. The goal will be to design algorithms to estimate the condition of a wall, with a focus on robustness, transfer learning, and explainability (XAI).
Keywords: Railway, Structural Health Monitoring, Computer Vision, Machine Learning, Transfer Learning, Domain Adaptation, XAI
Detailed description on SiROP
You can apply via SiROP or directly contact Florent Forest.
In this project the student will attempt to embed relevant physics priors in graph neural networks (GNNs) with the goal of developing an inverse modeling framework for the prediction of the underlying material model of a system, given the labeled input (load/excitation) and the output (mechanical response) data.
Keywords: Graph neural networks, Physics prior, finite element method
You can apply via SiROP or directly contact Vinay Sharma.
Time-series data is increasingly prevalent across various domains, including finance, healthcare, and environmental monitoring. The ability to extract meaningful information from time-series data is crucial for prediction, classification, and anomaly detection. This project focuses on exploring different time-series representations and their impact on machine learning tasks.
Objectives
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Benchmarking Time-Series Representations: Investigate the performance of various time-series representations including Fast Fourier Transform (FFT), Recurrence Plot (RP), Gramian Angular Field (GAF), and Markov Transition Field (MTF) in the context of machine learning tasks.
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Design of Fusion Method: Develop a novel method to fuse information from these representations, aiming to leverage their combined strengths.
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Evaluation on Diverse Datasets: Assess the effectiveness of the proposed method on a variety of datasets pertaining to Time-Series Classification, Forecasting, and Anomaly Detection.
Methodology
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Literature Review: Conduct a comprehensive review of existing literature on time-series representation methods and their applications.
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Implementation of Representations: Implement and fine-tune FFT, RP, GAF, and MTF, ensuring a fair and consistent basis for comparison.
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Benchmarking: Evaluate each representation method across multiple datasets, using standard metrics relevant to classification, forecasting, and anomaly detection.
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Fusion Method Development: Design a fusion algorithm to integrate the different representations, potentially using techniques like feature concatenation, ensemble methods, or deep learning architectures.
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Experimental Evaluation: Test the fusion method across the same datasets used for benchmarking, comparing its performance against the individual representation methods.
You can apply via SiROP or directly contact Hao Dong.
In this project, you will focus on the task of learning/simulating rigid objects dynamics from videos, with the end-goal of predicting future or alternative trajectories for the objects in the scene. This task includes the decomposition of a visual scene into multiple blocks (identifying individual objects), the modeling of their evolution and interactions in the scene (positions, velocities, collisions, …), and the prediction of future (or alternative) trajectories (also called rollout).
Keywords: Computer Vision, Mechanics, Scene Understanding, Object-centric Learning
You can apply via SiROP, IS-Academia – project #13957 or directly contact Amaury Wei.
In this project, you will focus on the task of learning/simulating rigid objects dynamics with Graph Neural Networks (GNNs), with the end-goal of predicting future or alternative trajectories for physical rigid objects in a given scene. This task includes the modeling of 3D objects with graphs (particles, meshes, …), the design of models suited for physics simulations (accounting for physical and mechanical laws) and the prediction of future (or alternative) trajectories (also called rollout).
Keywords: Graph Neural Networks, Physics, Mechanics, Meshes
You can apply via SiROP, IS-Academia – project #13956 or directly contact Amaury Wei.
The Industrial Internet of Things (IIoT) generates vast quantities of data from interconnected sensors and devices. To utilize the full potential of this data, it’s essential to go beyond individual sensor readings and understand the complex relationships between them. These relationships can be diverse: sensors may monitor specific equipment, while actuators directly control or operate parts of a process. Graph Neural Networks (GNNs) are powerful tools to model complex sensor networks, leveraging relationship information typically lost in traditional machine learning approaches. Analyzing IIoT data without considering sensor relationships can lead to missed insights and suboptimal decision-making. By inferring a graph structure from an IIoT sensor network, we create a structure capable of representing these relationships. GNNs, by learning directly from this graph, can model and distinguish between different relationship types (“monitors,” “operates,” etc.), leading to more accurate and interpretable predictions, fault/anomaly detections, and diagnostics. This thesis aims to bridge this gap, developing methods to capture sensor relationships and leverage GNNs for their analysis.
You can apply via SiROP or directly contact Mengjie Zhao.
Modern buildings are equipped with a large number of HVAC (Heating, Ventilation, and Air Conditioning) devices, comprising sensors, actuators, control systems mounted in very different locations. The integration of these components into the overarching building control system necessitates a meticulous configuration process known as commissioning. This undertaking represents the predominant portion of the interaction time between the device and the human operator, excluding routine operational tasks such as temperature readings from room sensors. Consequently, commissioning emerges as the most time-consuming, expensive, and error-prone phase in the establishment of a seamlessly operating building. as Any errors during commissioning can significantly impact the overall efficiency and functionality of the building.
In the context of this Master Thesis, the objective is to investigate the feasibility of developing an algorithm capable of predicting and verifying parameter values during the commissioning process, aiming to mitigate the occurrence of human errors.
You can apply via SiROP or directly contact Leandro von Krannichfeldt.