Browsing by Author "Avots, Egils, supervisor"

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    Classification of Alzheimer’s Disease From MRI Images
    (Tartu Ülikool, 2019) Elshatoury, Heba Hesham Hamed; Avots, Egils, supervisor; Anbarjafari, Gholamreza, supersvisor
    In English: In this thesis work machine learning techniques are used to classify MRI brain scans of people with Alzheimers Disease. This work deals with binary classification between Alzheimers Disease (AD) and Cognitively Normal (CN). Supervised learning algorithms were used to train a classifier using MATLAB Classification Learner App in which the accuracy is being compared. The dataset used is from The Alzheimers Disease Neuroimaging Initiative (ADNI). Histogram is used for all slices of all images. Based on the highest performance, specific slices were selected for further examination. Majority voting and weighted voting is applied in which the accuracy is calculated and the best result is 69.5% for majority voting. Eesti keeles: Käesolevas töös kasutatakse masinõppe meetodeid, et klassifitseerida Alzheimeri tõvega inimeste MRI aju skaneeringuid. Töös rakendatakse binaarset liigitust Alzheimeri tõve (AD) ja kognitiivse normaalsuse (CD) vahel. Kasutati juhendatud masinõppealgoritme, et treenida klassifikaatoreid MATLAB’i klassifikaatorite õpperakenduses (Classification Learner App), kus võrreldi algoritmi täpsust. Kasutatav andmestik pärineb ADNI andmebaasist (The Alzheimer’s Disease Neuroimaging Initiative). Kõikidest piltidest võetud osadele arvutati histogrammid. Kõrgeima jõudluse põhjal valiti konkreetsed osad edasiseks uurimiseks. Võtteldi enamus ja kaalutud valikute täpsust ja parimaks tulemuseks saadi enamusvalikuid kasutades 69.5%.
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    Online Battery Cell State of Charge Estimation for use in Electric Vehicle Battery Management Systems
    (Tartu Ülikool, 2018) Dreija, Kristaps; Anbarjafari, Gholamreza, supervisor; Avots, Egils, supervisor; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
    The electric vehicle (EV) is a complex, safety-critical system, which must ensure the safety of the operator and the reliability and longevity of the device. The battery management system (BMS) of an EV is an embedded system, whose main responsibility is the protection and safety of the high-voltage battery pack. The BMS must ensure that the requirements for health, status and deliverable power are met by maintaining the battery pack within the defined operational conditions for the defined lifetime of the battery. The state of charge (SOC) of a cell describes the ratio of its current capacity (amount of charge stored) to the nominal capacity as defined by the manufacturer. SOC estimation is a crucial, but not trivial BMS task as it can not be measured directly, but must be estimated from known and measured parameters, such as the cell terminal voltage, current, temperature, and the static and dynamic behavior of the cell in different conditions. Many different SOC estimation methods exist, out of which (currently) the most practical methods for implementing on a real-time embedded system are adaptive methods, which adapt to different internal and external conditions. This thesis proposes the use of the sigma point Kalman filter (SPKF) for non-linear systems as an equivalentcircuit model-based state estimator to be used in one of the current series production EV projects developed by Rimac Automobili. The estimator has been implemented and validated to yield better results than the currently used SOC estimation method, and will be deployed on the BMS of a high-performance hybrid-electric vehicle.