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Comparative Analysis of Gaussian Process Regression Modeling of an Induction Machine: Continuous vs. Mixed-Input Approaches
Bílek, Vladimír
This paper investigates the application of machine learning technique for modeling continuous and mixed-input parameters of electrical machines. The design of electrical machines typically requires the consideration of certain parameters as integer values due to their physical significance, including the number of stator/rotor slots, stator wires, and rotor bars. Traditional machine learning methods, which predominantly treat input parameters as purely continuous, may compromise modeling accuracy for such applications. To address this challenge, models capable of handling mixed-input parameters were used for the case study. Two training datasets were generated: one with purely continuous inputs and another with both continuous inputs and a categorical parameter, specifically, the number of stator conductors. Gaussian process regression was employed to build three models: two with continuous kernels, trained on both datasets, and one with a mixed kernel, trained only on the dataset containing a categorical parameter. A comparative analysis, demonstrated on a 1.5 kW induction machine - though applicable to a wide range of machines - illustrates the differences between the proposed approaches. The results highlight the importance of selecting an appropriate model for the Multi- Objective Bayesian optimization of electrical machines.
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Biometric fingerprint liveness detection
Rišian, Lukáš ; Vítek, Martin
This work addresses the problem of biometric recognition of fingerprint liveness to identify and differentiate between real fingerprints and their artificial replicas. The main objective was to identify the features that are crucial for fingerprint liveness recognition and based on these features to propose an efficient classification algorithm. We worked with the LivDet database from 2009, which contains both real and fake fingerprints. This database has been used in a worldwide competition and the results of all implemented algorithms are publicly available for subsequent comparison of success rates. An important part of this work was the preprocessing of the image data, which was crucial for testing the selected features and implementing the algorithms. We analyzed more than 180 different features from which we selected the most relevant ones. We then used the selected features to develop several fingerprint recognition and classification algorithms. Using the selected features, several possible variations of the algorithms have been proposed. Among all the implemented algorithms, we achieved the best result of almost 90%. Compared to other algorithms that have been implemented for the same purpose and have been used and tested on the same database, this can be considered a satisfactory and reliable result. In conclusion, the main objective of this work was to provide an efficient, secure, and reliable solution in the field of biometric fingerprint spoof detection.
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Automating Antibiotic Susceptibility Testing with Machine Learning for Disk Diffusion Test Analysis
Lepík, Jakub ; Čičatka, Michal
Rapid and reliable antibiotic susceptibility testing (AST) methods are imperative in response to the escalating challenges of antimicrobial resistance. This study focuses on enhancing disk diffusion testing, a cornerstone of AST, by integrating machine learning and automation. Leveraging state-of-the-art object detection models, including EfficientDet and Mask R-CNN and image-processing approaches, our methodology addresses the need for standardized evaluation processes across diverse laboratory equipment while enabling the integration of mobile devices into the workflow, democratizing AST, and enhancing its accessibility. We utilize a comprehensive disk diffusion dataset for object detection models captured by devices like mobile phones and professional solutions. Additionally, our experiments lay the groundwork for a web application adopting a device-agnostic approach, promising improved accessibility and efficiency in AST analysis.
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Mapping and analyzing of signal coverage of 4G/5G mobile networks
Baránek, Michal ; Polák, Ladislav ; Kufa, Jan
This paper addresses the enhanced measurement of signal coverage, capacity, and reliability in mobile networks, particularly with the growing prevalence of 4G and 5G technologies. Given the escalating importance of these networks in everyday activities, there arises a demand for open-source solutions to evaluate and enhance their performance effectively. The objective of this research is to analyze gathered data to pinpoint areas necessitating network enhancements and to develop opensource software and hardware solutions for extracting essential performance metrics (KPIs) from 4G/5G networks. The proposed system offers an interface for assessing network performance and signal coverage, enabling cost-efficient measurements across diverse environments.
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Deep Learning for Agar Plate Analysis: Predicting Microbial Cluster Counts
Čičatka, Michal ; Burget, Radim
Manual analysis of agar plates remains a bottleneck in microbiology, hindering automation efforts. This study investigates the feasibility of using machine learning for automated microbial cluster count detection from agar plate images. We employed various methods, including elbow detection (baseline) and supervised learning models (Support Vector Regression, Simple CNN, XGBoost, Random Forest, pre-trained VGG, and pre-trained Inceptionv3). The results demonstrate that machine learning models significantly outperform the baseline, achieving lower prediction errors and higher accuracy in identifying the correct number of clusters. Notably, both pre-trained VGG and InceptionV3 achieved strong performance, highlighting the effectiveness of transfer learning for this task. InceptionV3 exhibited the lowest error rates overall. This study establishes a foundation for developing robust automated systems for quantifying microbial growth, potentially streamlining workflows and improving efficiency in microbiological research and clinical settings.
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Machine Learning-based Fingerprinting Localization in 5G Cellular Networks
Dinh Le, Thao ; Mašek, Pavel
This study explores the viability of employing machine learning (ML)-based fingerprinting localization in 5G heterogeneous cellular networks. We conducted an extensive measurement campaign to collect data and utilized them to train three ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). The findings reveal that RF delivers the highest accuracy among the three ML algorithms. Furthermore, the results indicate that 5G New Radio (NR) can benefit the most from this localization method due to the dense deployment of base stations, achieving median localization errors of 17.5 m and 106 m during the validation and testing phases, respectively.
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Detection of parking space availability based on video
Kužela, Miloslav ; Frýza, Tomáš
This paper deals with the use of Machine vision and ML (Machine Learning) for a parking lot occupation detection. It presents and compares an already existing technology that solves such a problem with an AI (Artificial Intelligence) usecase. It introduces tools used to train and create such models and their subsequent results as well as a dataset that was used to verify the trained networks and discusses the future of how such a technology could be used to effectively and more affordably detect occupied parking spaces on parking lots.
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Percepce generativní umělé inteligence ve vybraných redakcích tuzemských zpravodajských médií, včetně proměny novinářské etiky
Vaněček, Lukáš ; Moravec, Václav (vedoucí práce) ; Klimeš, David (oponent)
Bakalářská práce se věnuje tématu umělé inteligence a současně jejímu užívání v tuzemských redakcích a novým etickým výzvám, jež se s těmito technologiemi pojí. V teoretické části kromě představení samotné generativní umělé inteligence a platforem jako ChatGPT nebo Midjourney fungujících na této bázi popisuje oblasti etiky, v nichž mohou zmíněné nástroje vyvolat změny. Součástí práce je kvalitativní výzkum založený na dvanácti hloubkových polostrukturovaných rozhovorech s respondenty z veřejnoprávních i soukromých redakcí, kteří s generativní umělou inteligencí pracují. Klíčová slova Umělá inteligence, AI, ChatGPT, Midjourney, etika, proměna, strojové učení, automatizovaná žurnalistika, média
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Impact of European Central Bank and Federal Reserve System statements on cryptocurrency markets via sentiment analysis
Krejcar, Vilém ; Krištoufek, Ladislav (vedoucí práce) ; Čech, František (oponent)
Tato studie zkoumá dopad ve ejn˝ch prohláöení hlavních centrálních bank, konkrétn FED a ECB, na volatilitu Bitcoinu v letech 2018 aû 2021. S vyuûitím vysokofrekven ních dat jsme vypo ítali volatilitu Bitcoinu a extrahovali senti- mentové skóre z ve ejn˝ch prohláöení centrálních bank pomocí dvou metod: jazykového modelu FinBERT a state-of-the-art modelu generativní um lé in- teligence GPT-4 s p izp soben˝mi prompty. Model GPT-4 se prokázal jako preferovaná varianta díky efektivn jöímu zachycení sentimentov˝ch nuancí. Naöe anal˝za zahrnovala porovnání r zn˝ch model pro finan ní data, p i emû HAR model vykazoval nejlepöí v˝sledky. Záv ry studie jsou následující: negativní sentiment ECB b hem pandemie byl spojen s okamûit˝m a v˝razn˝m nár stem volatility Bitcoinu, nazna ující reakci trhu v podob opatrnosti. Tato zjiöt ní poukazují na v˝znamn˝ vliv sentimentu centrálních bank na volatilitu Bitcoinu a potvrzují p vodní hypotézu tohoto v˝zkumu. V˝sledky navíc poskytují mo- tivaci k za len ní generativní um lé inteligence do akademického v˝zkumu jako nástroj pro zkoumání dosud neobjeven˝ch znalostí a vzorc . Klasifikace JEL C32, C55, C58, E58, G15 Klí ová slova centrální banky, sentimentová anal˝za, volatilita, Bitcoin, GenAI, HAR, FED, ECB Název práce Vliv prohláöení ECB a FED na trh kryp- toaktiv skrze...
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Machine learning through geometric mechanics and thermodynamics
Šípka, Martin ; Pavelka, Michal (vedoucí práce) ; Monmarché, Pierre (oponent) ; Maršálek, Ondřej (oponent)
30. prosinec 2023 Tato práce zkoumá nové přístupy k učení fyzikálních modelů, vynucování vazeb v rovnicích a k optimalizaci penalizačních funkcí závislých na trajektorii. Nedávné pokroky ve výzkumu hlubokého učení a umělé inteligence jsou v této práci propojeny se zavedenými poznatky o dynamických a chemických systémech a otevírají tak nové možnosti mezioborového propojení. Tato práce prezentuje významný příspěvek k simulačním technikám, které využívají automatickou diferenciaci pro propagaci dynamiky, a ukazuje nejen jejich slibné využití v praxi, ale zároveň formuluje nové teoretické výsledky z oblasti o chování se gra- dientů v evolucích kontrolovaných neuronovými sítěmi. Všechny nové nástroje jsou důsledně testovány na příkladech z fyziky a chemie, čímž vytvářejí potenciál pro jejich další aplikaci v praxi. 1
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