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Lithium-ion battery SOH analysis
Sedlařík, Marek ; Kazda, Tomáš ; Vyroubal, Petr
Battery State-of-Health modelling can significantly reduce the amount of costly laboratory tests in the application being analyzed. This paper discusses the prediction of the State-of-Health, an indicator of battery life, using support vector regression. This experiment is performed on a sample cell of a lithium-ion battery, which is subjected to a method known as the Constant Current Constant Voltage method, where the battery is charged and discharged at a constant current of 0.5 C. Although this method in this paper is applied in laboratory conditions and it is a controlled method, or it deviates from the battery cycling of real applications, it can be used in these applications, thus the scope of this research can predict the State-of-Health also in the areas of batteries used in mobile devices or electromobility. The State-of-Health indicator then determines whether the battery is still suitable for that primary application. Assuming that we can predict this parameter with some accuracy, it is then also possible to tell after what length of time a battery will need to be replaced and when it will be suitable for secondary applications such as stationary storage. Once it reaches that state, this calculation can be further applied to those applications as well.
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Music mood and emotion recognition using Music information retrieval techniques
Smělý, Pavel ; Mucha, Ján (referee) ; Kiska, Tomáš (advisor)
This work focuses on scientific area called Music Information Retrieval, more precisely it’s subdivision focusing on the recognition of emotions in music called Music Emotion Recognition. The beginning of the work deals with general overview and definition of MER, categorization of individual methods and offers a comprehensive view of this discipline. The thesis also concentrates on the selection and description of suitable parameters for the recognition of emotions, using tools openSMILE and MIRtoolbox. A freely available DEAM database was used to obtain the set of music recordings and their subjective emotional annotations. The practical part deals with the design of a static dimensional regression evaluation system for numerical prediction of musical emotions in music recordings, more precisely their position in the AV emotional space. The thesis publishes and comments on the results obtained by individual analysis of the significance of individual parameters and for the overall analysis of the prediction of the proposed model.
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Music mood and emotion recognition using Music information retrieval techniques
Smělý, Pavel ; Mucha, Ján (referee) ; Kiska, Tomáš (advisor)
This work focuses on scientific area called Music Information Retrieval, more precisely it’s subdivision focusing on the recognition of emotions in music called Music Emotion Recognition. The beginning of the work deals with general overview and definition of MER, categorization of individual methods and offers a comprehensive view of this discipline. The thesis also concentrates on the selection and description of suitable parameters for the recognition of emotions, using tools openSMILE and MIRtoolbox. A freely available DEAM database was used to obtain the set of music recordings and their subjective emotional annotations. The practical part deals with the design of a static dimensional regression evaluation system for numerical prediction of musical emotions in music recordings, more precisely their position in the AV emotional space. The thesis publishes and comments on the results obtained by individual analysis of the significance of individual parameters and for the overall analysis of the prediction of the proposed model.
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Comparison of different models for forecasting of Czech electricity market
Kunc, Vladimír ; Krištoufek, Ladislav (advisor) ; Kopečná, Vědunka (referee)
There is a demand for decision support tools that can model the electricity markets and allows to forecast the hourly electricity price. Many different ap- proach such as artificial neural network or support vector regression are used in the literature. This thesis provides comparison of several different estima- tors under one settings using available data from Czech electricity market. The resulting comparison of over 5000 different estimators led to a selection of several best performing models. The role of historical weather data (temper- ature, dew point and humidity) is also assesed within the comparison and it was found that while the inclusion of weather data might lead to overfitting, it is beneficial under the right circumstances. The best performing approach was the Lasso regression estimated using modified Lars. 1
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