Index

Uncertainty Estimation for Transformer-based Glacier Segmentation

Deep Learning-Based Optical Flow for Camera Pose Estimation in Navigation Assistance for Blind Pedestrians on Sidewalks

Automated knowledge management using a semantic database and large language models in the medical domain

Deep Metric Learning for Orca Identification

A hybrid approach forLeakage Localization in the Water Distribution Network

Climate change is expected to cause more frequent and intense weather events such as droughts and floods, which can place additional stress on water distribution networks (WDN). Leakage in water distribution networks is a significant challenge that exacerbate the effects of climate change by increasing the amount of water that needs to be extracted and treated, as well as increasing energy consumption and greenhouse gas emissions associated with pumping and treating water. Therefore, accurate leakage localization can help reduce the amount of water lost from distribution networks, thereby reducing the need for additional water extraction and treatment. This can lead to energy savings and reduced greenhouse gas emissions, as well as ensuring that water resources are used efficiently and effectively. Additionally, by reducing the amount of water lost to leakage, WDN can be made more resilient to the impacts of climate change, such as droughts and water scarcity.
State-of-the-art methods tackle the challenge of leakage localization in a WDN comprise acoustic methods [1], pressure transient methods [2], flow measurement methods [3], and machine learning (ML) based methods [4-5]. However, these methods have significant limitations that hinder their application in the daily routine of water utilities. For instance, acoustic methods are cost intensive as they require additional sensors and equipment. Furthermore, the accuracy of such methods is greatly affected by the material of the pipes and presence of noise. Although the sensors that are necessary for the pressure transient methods and flow measurement methods might available due to the daily operation of WDN, these methods are often not sensitive to detect small leaks. Data-driven methods using ML has gain more importance in the recent year. However, the data availability, data quality and the explainability of ML models are the major limitations.
Therefore, we would like to investigate the effectiveness of a hybrid AI approach combining hydraulic model and ML to tackle the leakage localization within WDN using real world data. The following aspects need to be considered:
• Literature review of leakage localization for WDN.
• Development and implementation of a hybrid framework combining hydraulic model and ML methods for leakage localization with existing sensor data.
• Comprehensive evaluation of the performance of the implemented framework w.r.t. accuracy, robustness, and explainability.
[1] Khulief, Yehia et. al., (2012). Acoustic Detection of Leaks in Water Pipelines Using Measurements inside Pipe. ASCE Journal of Pipeline System Engineering and Practice. 2021, 3, 47. Doi:10.1061/(ASCE)PS.1949-1204.0000089.
[2] Levinas, D. et. al., Water Leak Localization Using High-Resolution Pressure Sensors. Water 2021, 13, 591. https://doi.org/10.3390/w13050591
[3] L. Lindström, et. al. Leakage Localization in Water Distribution Networks: A Model-Based Approach, 2022 European Control Conference (ECC), London, United Kingdom, 2022, pp. 1515-1520, doi: 10.23919/ECC55457.2022.9838006.
[4] Huang, Pingjie, et al. “Real-time burst detection in district metering areas in water distribution system based on patterns of water demand with supervised learning.” Water 10.12 (2018): 1765. doi.org/10.3390/w10121765
[5] Soldevila, Adrià, et al. “Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation.” Water 11.7 (2019): 1500. doi.org/10.3390/w11071500

ML based Classification of States in LPWAN Current Consumption Curves

Evaluation of imperfect segmentation labels and the influence on deep learning models

Multi-organ segmentation in CT is of great clinical and research value [1], which can benefit the development of automatic computer-aided diagnosis tools and the accuracy of some interventional therapies, such as the treatment planning of radiation therapy. With the development of the deep learning (DL), the performance of the DL-based models has dramatically improved, compared with traditional segmentation methods [2].
For training a DL model for segmentation task, a paired segmentation dataset is needed. A paired segmentation dataset here indicates the accurate annotation of all voxels in all CT volumes, which is tedious and time-consuming. For this reason, the large-scale segmentation datasets for multiple organs in large body region are rarely published and mostly contain annotation errors. Several researches have been done to study the influence of imperfect segmentation labels on the training of the segmentation network, but to the best of our knowledge, none is done for the multi-organ segmentation task in CT. [3, 4]
In this thesis, our research problem is how the segmentation network will be influenced by the typical annotation errors. To achieve this, several typical annotation errors will be simulated on a public multiorgan segmentation dataset, CT-ORG, [5] and the influence will be analysed both quantitatively and qualitatively.

The thesis will comprise the following work items:

Literature overview of related analysis of imperfect segmentation labels.
Simulate some typical annotation errors on a segmentation dataset.
Train the baseline model on the perfect dataset and the models on the imperfect datasets.
Evaluate the influence of the errors with the baseline.
Record the result in the thesis

[1] Andreas Maier, Christopher Syben, Tobias Lasser, and Christian Riess. A gentle introduction to deep learning in medical image processing. Zeitschrift f¨ur Medizinische Physik, 29(2):86–101, 2019.
[2] Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, and Paul Kennedy. Deep learning techniques for medical image segmentation: achievements and challenges. Journal of digital imaging, 32:582–596, 2019.
[3] Eugene Vorontsov and Samuel Kadoury. Label noise in segmentation networks: mitigation must deal with bias. In DGM4MICCAI 2021 and DALI 2021, pages 251–258. Springer, 2021.
[4] Nicholas Heller, Joshua Dean, and Nikolaos Papanikolopoulos. Imperfect segmentation labels: How much do they matter? In CVII-STENT 2018 and LABELS 2018, pages 112–120. Springer, 2018.
[5] Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, and Daniel L Rubin. Ct-org, a new dataset for multiple organ segmentation in computed tomography. Scientific Data, 7(1):381, 2020