Index
Brain Tumor Segmentation with Focus on Complex Subregions
Comparative analysis of word representations in LLMs using BERT and LSTM architecture
Exploring Narrative Representations in the Large Language Model BERT
Leakage Detection and Modeling in Water Distribution Systems
Abstract:
Leakage within Water Distribution Networks (WDNs) constitutes a primary concern for utilities, significantly impacting water conservation and the ability to meet consumer demand. In addressing this pervasive issue, the present study explores the efficacy of a comprehensive detection and localization strategy across District Meter Areas (DMAs). The research hinges on the development of a calibrated hydraulic model that adheres to stringent standards, minimizing error margins and setting a precedent in the domain of water network analysis. Central to the study is the implementation of an autoencoder-based deep learning framework, which has demonstrated proficiency in the detection of leaks. To transcend the limitations imposed by sparse sensor networks, a novel hybrid approach was adopted, integrating the hydraulic model with data-driven algorithms to enhance the localization of leaks. The methodology involved segmenting the DMA into subzones and applying rolling window simulations to create a diverse dataset for training a Convolutional Neural Network (CNN). The standout performance of the model, particularly with a 6-hour rolling window, was corroborated by its ability to precisely localize leaks in an actual case scenario. Addressing this issue, our research advances a strategy for leak detection and localization that is both economical and effective, utilizing minimal sensor input and also the importance of additional sensors to localize leak even more accurately. The implications of this research extend towards reinforcing the operational integrity of WDNs, fostering water conservation, and ensuring resource availability and sustainability in the face of escalating demand.
Keywords: Water Distribution Networks, Hydraulic Modelling, Data-Driven Methods, Spectral Clustering, Leakage Detection, Leakage Localization.
This thesis is part of the “UtilityTwin” project.
Adaptive Training for Heat Demand Prediction of District Heating Network
In this thesis, we focus on short-term heat demand forecasting based on measured consumer demand. The thesis also studies approaches from Continual Learning, for developing an adaptive forecasting framework to deploy at the energy supplier to optimize supply and demand.
Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble
Abstract:
One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimized supply of thermal energies through proactive techniques such as load forecasting. In this research thesis, we propose a deep learning-based forecasting framework for heat demand based on neural networks where the time series are represented as scalograms equipped with the capacity of embedding exogenous variables such as weather, and holiday/non-holiday. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM, and two variants of the proposed method.
The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.69% for Mean Absolute Percentage Error and 202.80kW for Root Mean Squared Error is achieved with the proposed framework in comparison to all other methods. Moreover, the proposed method exhibits minimal deviation of performance across different climatic seasons and geographical zones compared to the baseline methods.
This thesis is part of the “UtilityTwin” project.
Graph Neural Networks in Pathological Speech
Apply Graph knowledge to applications on pathological speech (e.g., Parkinson’s disease).
Pre-requirements:
- Pattern Analysis
- Deep Learning
- Optional: Speech and Language Understanding
- Optional: Seminar on pathological speech
Please send your grades to paula.andrea.perez@fau.de and luis.rivera@fau.de