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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Night Setback Identification of District Heating Substations

Gerima, Kassaye January 2021 (has links)
Energy efficiency of district heating systems is of great interest to energy stakeholders. However, it is not uncommon that district heating systems fail to achieve the expected performance due to inappropriate operations. Night setback is one control strategy, which has been proved to be not a suitable setting for well-insulated modern buildings in terms of both economic and energy efficiency. Therefore, identification of a night setback control is vital to district heating companies to smoothly manage their heat energy distribution to their customers. This study is motivated to automate this identification process. The method used in this thesis is a Convolutional Neural Network(CNN) approach using the concept of transfer learning. 133 substations in Oslo are used in this case study to design a machine learning model that can identify a substation as night setback or non-night setback series. The results show that the proposed method can classify the substations with approximately 97% accuracy and 91% F1-score. This shows that the proposed method has a high potential to be deployed and used in practice to identify a night setback control in district heating substations.
2

An Automated Discharge Summary System Built for Multiple Clinical English Texts by Pre-trained DistilBART Model

Alaei, Sahel January 2023 (has links)
The discharge summary is an important document, summarizing a patient’s medical information during their hospital stay. It is crucial for communication between clinicians and primary care physicians. Creating a discharge sum- mary is a necessary task. However, it is time-consuming for physicians. Using technology to automatically generate discharge summaries can be helpful for physicians and assist them in concentrating more on the patients than writing clinical summarization notes and discharge summaries. This master’s thesis aims to contribute to the research of building a transformer-based model for an automated discharge summary with a pre-trained DistilBART language model. This study plans to answer this main research question: How e↵ective is the pre-trained DistilBART language model in predicting an automated discharge summary for multiple clinical texts? The research strategy used in this study is experimental. the dataset is MIMIC- III. To evaluate the e↵ectiveness of the model, ROUGE scores are selected. The result of this model is compared with the result of the baseline BART model, which is implemented on the same dataset in the other recent research. This study regards multiple document summarization as the process of combining multiple inputs into a single input, which is then summarized. The findings indicate an improvement in ROUGE-2 and ROUGE-Lsum in the DistilBART model in comparison with the baseline BART model. However, one important limitation was computational resource constraint. The study also provides eth- ical considerations and some recommendations for future works.

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