Korean mask-dance and Aristotle\u27s poetics

Korean mask-dance is the traditional theatre of Korea. It was formerly the country\u27s most well-known form of drama among traditional theatrical entertainments. This study explores the theatrical structure of Korean mask-dance as well as its historical background. The rise of Korean mask-dance may be traced back to the shamanistic village ritual which gradually became similar to the extant form after absorbing aspects of the Buddhism festival through the Goryeo Dynasty, which lasted from 918-1392). During the Joseon Dynasty (1392-1910), the mask-dance had acquired its basic form with aspects of professional theatrical entertainment. The mask-dances have been performed during traditional holidays and festivals over the past three hundred years. Four types of the mask-dance continue to exist today, all being derived from their geographic origins. Many scholar and artists have explained the value of Korean mask-dance and its own esthetic level. Dance, song, music, masks, costumes, props, stage, and audience participation of Korean mask-dance are obvious theatrical elements and have their own separate meaning. The main purpose of this study is to examine the weak parts of the dramatic structure of the art form and attempt to analyze it using Aristotle\u27s Poetics (384-322 BCE). When Korean mask-dance is analyzed by Aristotle\u27s concept of drama, the mask-dance exactly reverses this order of importance of the dramatic elements. Through recognition of both the uniqueness of Korean mask-dance and the dramatic standards of Aristotle\u27s concept, this study should enable scholars and artists to embrace more fully the universal nature of theatre

Data-driven sensors and their applications

Virtuální senzory jsou postupně se rozšiřující technikou v oblasti průmyslových měření. Jedná se o počítačové programy, které za pomoci dříve získaných dat poskytují další údaje podobně jako klasické hardwarové senzory. Tyto údaje získávají pomocí prediktivních modelů založených na metodách strojového učení jako jsou například neuronové sítě nebo support vector machines. Tato práce obsahuje především rešerši fungování, struktur a tvorby virtuálních senzorů. Dále popisuje strojové učení, rozdělení jeho algoritmů a seznamuje s metodami běžně využívanými v oblasti virtuálních senzorů. Ke konci autor popisuje jejich možný budoucí vývoj a směr dalších aplikací.Soft sensors are a gradually expanding technique in the field of industrial measurement. These sensors are computer programs that provide additional data using previously acquired data in a similar way to conventional hardware sensors. The additional data is obtained using predictive models based on machine learning methods such as neural networks or support vector machines. This work mainly includes a research on the function, structure and creation of soft sensors. It also describes machine learning, the distribution of its algorithms and introduces the methods commonly used in the field of virtual sensors. Towards the end, the author describes possible future development of soft sensors and the direction of further applications.

Test time augmentation for increasing the classification accuracy of a system aimed at automatic assessment of cardiomyocyte development stages

Tato práce se věnuje rešerši technik rozšíření dat v inferenčním režimu a následné aplikaci na převzatý systém automatického hodnocení vývojových fází kardiomyocytů s cílem zvýšení jeho přesnosti pro praktické aplikace. Tyto techniky spočívají v agregaci predikcí modelu strojového učení přes více rozšířených vzorků dat, čímž přispívá k zlepšení robustnosti predikcí. Bylo navrženo několik metod, které se snaží získat co největší nárůst přesnosti nebo berou v úvahu kompromis mezi výpočetní složitostí a ziskem na přesnosti.This thesis studies techniques of test-time augmentation and their application to the adopted system of automatic assessment of cardiomyocyte developmental stages in order to increase its accuracy for practical applications. These techniques consist of aggregating the predictions of a machine learning model across multiple augmented samples of data, consequently contributing to improve the robustness of the predictions. Several methods have been proposed that attempt to obtain the topmost increase in accuracy or take into account the trade-off between computational complexity and accuracy gains.

Data-driven sensors and their applications

Soft sensors are a gradually expanding technique in the field of industrial measurement. These sensors are computer programs that provide additional data using previously acquired data in a similar way to conventional hardware sensors. The additional data is obtained using predictive models based on machine learning methods such as neural networks or support vector machines. This work mainly includes a research on the function, structure and creation of soft sensors. It also describes machine learning, the distribution of its algorithms and introduces the methods commonly used in the field of virtual sensors. Towards the end, the author describes possible future development of soft sensors and the direction of further applications

Test time augmentation for increasing the classification accuracy of a system aimed at automatic assessment of cardiomyocyte development stages

This thesis studies techniques of test-time augmentation and their application to the adopted system of automatic assessment of cardiomyocyte developmental stages in order to increase its accuracy for practical applications. These techniques consist of aggregating the predictions of a machine learning model across multiple augmented samples of data, consequently contributing to improve the robustness of the predictions. Several methods have been proposed that attempt to obtain the topmost increase in accuracy or take into account the trade-off between computational complexity and accuracy gains

Statistical modeling of capacitor mismatch effects for successive approximation register ADCs

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A Facile Bioprinting Technique for Size-Controllable Microtissues Using Photocurable Decellularized Extracellular Matrix-Based Bioinks

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