Index

New speech, motor and cognitive exercises for mobile Parkinson’s Disease monitoring with Apkinson

Contextual Meta Knowledge Integrated into a Fully-Automated Multi-Stage Deep Learning Framework for Killer Whale Individual Classification

Unstained White Blood Cells Classification Using Deep Learning

Represent senor data of district heating network using contrastive learning

According to the European Commissions, heating and cooling in buildings and industry take up half of the total EU energy consumption [1]. One of the state-of-the-art thermal energy supply infrastructures is the district heating network, which is widespread in middle and north Europe. Thus, a robust and efficient management of energy supply trough out district heating networks has significant impact on reducing the greenhouse gas emission. In the last decades, smart sensors, such as pressure or consumption sensors, have been utilized to monitor the condition of the networks, therefore providing a solid basis for further data-driven approaches addressing various tasks, e.g., heat load prediction, automated anomaly detection etc.

With the latest advances of machine learning and deep learning, data-driven approaches have gained more importance and applicability for improving the efficiency of district heating networks. Although many works have been proposed for heat load prediction [2, 3] or anomaly detection [4] to date, a fundamental challenge remains the learning of universal representation for time series. Recently, contrastive learning paradigm has been adapted for the learning tasks with time series [5]. However, the proposed works haven´t been validated with real-world sensor data of district heating network. In this work, we are aiming at finding appropriate representation using contrastive learning for real-world sensor data acquired from district heating network. The thesis should consist of the following aspects:

Literature review of contrastive learning for time series
Analysis and understanding of real-world sensor data
Develop and implement contrastive learning framework for district heating network
Evaluate the proposed method(s)

[1] European Commission, Heating and cooling. 2018 https://ec.europa.eu/energy/topics/energy-efficiency/heating-and-cooling_en.

[2] Xue et al., District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model. Energies 2019, 12, 2122. https://www.mdpi.com/1996-1073/12/11/2122

[3] Chatterjee et al., Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model, NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, Dec. 2021. https://www.climatechange.ai/papers/neurips2021/42

[4] F. Zhang and H. Fleyeh, “Anomaly Detection of Heat Energy Usage in District Heating Substations Using LSTM based Variational Autoencoder Combined with Physical Model,” 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020, pp. 153-158, doi: 10.1109/ICIEA48937.2020.9248108.

[5] Yue et al., TS2Vec: Towards Universal Representation of Time Series. arXiv:2106.10466. Feb.2022. https://arxiv.org/abs/2106.10466

Deep Learning-based XRM Projection Super Resolution

Unsupervised Super Resolution in X-ray Microscopy Using a Cycle-Consistent Generative Model

Automatic Rotation of Spinal X-Ray Images

Guidance in orthopedic and trauma surgery is increasingly relying on intraoperative fluoroscopy with a mobile C-arm. Mobile fluoroscopy is also used to assess the success of fracture reduction, implant position, and overall outcome [1]. This reduces the number of necessary revision surgeries [2]. Accurate standardized image rotation is essential to improve reading performance and interpretation. While alignment of a patient with the imaging system is not always achievable, the images must be rotated manually by radiographers [3].

As the interaction of the user with the imaging system should be minimized, the goal of this thesis is to develop an automatic procedure that determines the orientation of the acquired images and rotates them to a standard position to be viewed by radiologists. In this work, the focus is on the regression of the image rotation of anterior-posterior (AP) and lateral radiographs of the spine since these are the most frequently acquired and most relevant for spine procedures.

[1] Lisa Kausch, Sarina Thomas, Holger Kunze, Maxim Privalov, Sven Vetter, Jochen Franke, Andreas H.
Mahnken, Lena Maier-Hein, and Klaus Maier-Hein. Toward automatic c-arm positioning for standard
projections in orthopedic surgery. Int. J. Comput. Assist. Radiol. Surg., 15(7):1095–1105, Jul 2020.

[2] Celia Mart´ın Vicario, Florian Kordon, Felix Denzinger, MarkusWeiten, Sarina Thomas, Lisa Kausch, Jochen
Franke, Holger Keil, Andreas Maier, and Holger Kunze. Automatic plane adjustment of orthopedic intraoperative
flat panel detector ct-volumes. In Proc. MICCAI, Part II, volume 12262, pages 486–495, 2020.

[3] Ivo M. Baltruschat, Axel Saalbach, Mattias P. Heinrich, Hannes Nickisch, and Sascha Jockel. Orientation
regression in hand radiographs: a transfer learning approach. In Proc. SPIE Medical Imaging, volume 10574,
pages 473 – 480, 2018.