The Korean peninsula knows a long history of book culture, and attention to it has ranged from the earliest known texts to the recently growing interest in modern Korean literature that came with...Show moreThe Korean peninsula knows a long history of book culture, and attention to it has ranged from the earliest known texts to the recently growing interest in modern Korean literature that came with the ‘Korean wave’. Many of the now canonical works find their roots in the colonial era in Korea (1910-1945) and various aspects pertaining to textual production during this era have been researched by scholars. However, an often-overlooked history is that of the printers and printshops in colonial Korea who had a significant influence over book production. With this loss of primary source material directly stemming from these printshops, the only other source indicating the printer of a text is the included colophon. This colophon is often damaged or simply missing in older books, and to this date there is no efficient method to recover this lost information. Thereby preventing any sizeable quantitative study of printshops in colonial Korea. This MA Thesis will examine the possibility of using convolutional neural networks (ConvNet) to identify the printshop of a given text dating to colonial Korea in order to allow large- scale quantitative research into colonial Korean printshops, which has been impossible thus far It will do this through a case study approach that aims to successfully classify books of four colonial Korean printshops, namely, the Hansŏng Tosŏ Chusik Hoesa (漢城圖書株式會社), Taedong inswaeso (大東印刷所), Sinmungwan (新文館), and Chosŏn inswae chusik hoesa (朝鮮印刷株 式會社). The findings here can be applied to a more extensive set of printers, given enough data and time. Therefore, this research is of high importance to the field of Korean history, as it is an essential step in charting the history of colonial Korean printers. Additionally, the benefits gained from this study are also helpful in the field of digital humanities, as this study will not only focus on the production and performance of a model but also include dataset construction and model explainability. The latter is a vital part and often missing in other DH scholarly work related to ConvNets. Hence, this paper is highly relevant to Korean historical research, and its methodology can be used far beyond the context of colonial-era printshops.Show less
The flood of archaeological remote sensing data in present times calls for digital solutions which can reduce the time and cost required to manually analyse these vast amounts of data. In recent...Show moreThe flood of archaeological remote sensing data in present times calls for digital solutions which can reduce the time and cost required to manually analyse these vast amounts of data. In recent times, Deep Learning techniques based on Covolutional Neural Networks for auto- mated detection of archaeological objects, are fast gaining traction due to the potential they hold. However, much of these studies remain re- stricted to detection of discrete objects with uniform morphologies. Thus, there lies a gap in the use of these methodologies for mapping of larger archaeological systems, which can contribute immensely to landscape archaeology, and our knowledge of human cultural activity in the past. This thesis attempts to make this shift by using a CNN-based in- stance segmentation methodology to detect individual plots of large Celtic Field systems. It was implemented on LiDAR data from the Veluwe region in the Netherlands, using the Mask R-CNN model. The results show that the methodology has the ability to not only detect field plots present in the landscape, but also outline their exact shape. These results when embedded in a wider framework can con- tribute greatly to archaeological prospection and our understanding of the archaeological landscape in the Veluwe.Show less
Electroencephalography (EEG) has been used for over a century to measure electric potentials in the brain. The brain signals can be used in a Brain Computer Interface (BCI) to control machines,...Show moreElectroencephalography (EEG) has been used for over a century to measure electric potentials in the brain. The brain signals can be used in a Brain Computer Interface (BCI) to control machines, like prostheses or exoskeletons. In the last decades, consumer-grade EEG devices have become available. Their performance and reliability, however, does not match that of medical EEG equipment. In this project, the possibilities for classification with such a consumer-grade EEG device, the EMOTIV EPOC+, were explored. The main challenge was to overcome the large noise and nuisances in the EEG signal to be able to classify brain states induced by motoric tasks and sensory stimuli. The effect of several preprocessing techniques was studied: Independent Component Analysis, a denoising autoencoder, and adaptive filtering with an autoencoder. For the classification, various machine learning approaches were proposed to analyse the EEG signals, ranging from a naive Bayes classifier to Deep Convolutional Neural Networks. Furthermore, the effect of motivating the structure of a neural network for this classification task was investigated. It was found that using the spectral features of the EEG signal as a motivation for the structure of neural networks helps classifiers identify brain states that are not classified by other types of neural networks.Show less
In this thesis paper the possibility of using Convolutional Neural Networks (CNN) for wavefront reconstruction is studied. This is tested with the Generalised Optical Differentiation Wavefront...Show moreIn this thesis paper the possibility of using Convolutional Neural Networks (CNN) for wavefront reconstruction is studied. This is tested with the Generalised Optical Differentiation Wavefront Sensor (g-ODWFS), a nonlinear pupil plane wavefront sensor that uses spatially varying polarisation rotators in the focal plane. Network architectures are developed for modal reconstruction in the Zernike and actuator basis. Both simulations and experimental work show that using a CNN for wavefront reconstruction improves the estimation of large phase aberrations as compared to the currently used linear method. This increases the convergence rate of the adaptive optics system, which is demonstrated in closed loop simulations.Show less
