Iskanje po katalogu digitalne knjižnice

Opis: This thesis focuses on improving the simulation, estimation, and accuracy of parameter identification in lithium-ion battery models. The key objective was to enhance a previously developed program by transitioning it to an object-oriented design, making it more efficient, user-friendly, and modular. Additionally, efforts were made to optimize the parameter estimation process by upgrading the cost function used during simulations and integrating real-world battery measurement data, specifically for the LGM50 battery type. The first step in the thesis involved reworking the codebase to an object-oriented structure, which improved not only the code’s clarity but also its extensibility and efficiency. With this change, the program was better suited for future improvements and became more accessible for other users through simplified installation procedures. This was accompanied by the implementation of unit testing to ensure the reliability of the code. Experiments were conducted across a range of discharge rates (from 0.05C to 1C) to evaluate the performance of the model under different conditions. These tests helped to identify trends in how the model responded to changes in operational parameters. Additionally, a dynamic pulse test was performed, which allowed for more precise estimation of the parameters. The results of these tests demonstrated the robustness of the methodology, especially under dynamic conditions. A major innovation introduced in this thesis was the development of a new cost function, which led to noticeable improvements in parameter estimation accuracy, particularly under high discharge rates and when estimating multiple parameters simultaneously. This new cost function proved especially effective in more complex scenarios, where the original cost function struggled to maintain the same level of accuracy. The program’s capabilities were further extended by incorporating real experimental data. Using a constant discharge profile for the LGM50 battery, the results showed some challenges when dealing with real-world data, particularly due to issues in measurement or data preprocessing. Nonetheless, the model consistently produced solutions, although the accuracy was influenced by the quality of the input data. The thesis concludes by highlighting the success of the improvements made, both in terms of the program’s structure and the precision of its estimations. However, it also emphasizes the importance of improving the quality of real-world data to fully leverage the model’s potential in practical applications. This work lays a foundation for future developments in battery modeling, providing a framework that is adaptable for further research and practical use.
Ključne besede: Machine Learning, Lithium-Ion Batteries, Parameter Estimation, Uncertainty Quantification, Real-experimental data
Objavljeno v DKUM: 03.03.2025; Ogledov: 0; Prenosov: 25
Celotno besedilo (5,99 MB)

Opis: Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.
Ključne besede: multimodal depression classification, scoping review, real-world data, mental health
Objavljeno v DKUM: 11.08.2023; Ogledov: 529; Prenosov: 81
Celotno besedilo (1,43 MB)
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