28,997 research outputs found
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
Embodying entrepreneurship: everyday practices, processes and routines in a technology incubator
The growing interest in the processes and practices of entrepreneurship has
been dominated by a consideration of temporality. Through a thirty-six-month
ethnography of a technology incubator, this thesis contributes to extant
understanding by exploring the effect of space. The first paper explores how
class structures from the surrounding city have appropriated entrepreneurship
within the incubator. The second paper adopts a more explicitly spatial analysis
to reveal how the use of space influences a common understanding of
entrepreneurship. The final paper looks more closely at the entrepreneurs within
the incubator and how they use visual symbols to develop their identity. Taken
together, the three papers reject the notion of entrepreneurship as a primarily
economic endeavour as articulated through commonly understood language and
propose entrepreneuring as an enigmatic attractor that is accessed through the
ambiguity of the non-verbal to develop the ‘new’. The thesis therefore contributes
to the understanding of entrepreneurship and proposes a distinct role for the non-verbal in that understanding
Underwater optical wireless communications in turbulent conditions: from simulation to experimentation
Underwater optical wireless communication (UOWC) is a technology that aims to apply high speed optical wireless communication (OWC) techniques to the underwater channel. UOWC has the potential to provide high speed links over relatively short distances as part of a hybrid underwater network, along with radio frequency (RF) and underwater acoustic communications (UAC) technologies. However, there are some difficulties involved in developing a reliable UOWC link, namely, the complexity of the channel. The main focus throughout this thesis is to develop a greater understanding of the effects of the UOWC channel, especially underwater turbulence. This understanding is developed from basic theory through to simulation and experimental studies in order to gain a holistic understanding of turbulence in the UOWC channel.
This thesis first presents a method of modelling optical underwater turbulence through simulation that allows it to be examined in conjunction with absorption and scattering. In a stationary channel, this turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. It is also demonstrated using the technique presented that the relative impact of turbulence on a received signal is lower in a highly scattering channel, showing an in-built resilience of these channels. Received intensity distributions are presented confirming that fluctuations in received power from this method follow the commonly used Log-Normal fading model. The impact of turbulence - as measured using this new modelling framework - on link performance, in terms of maximum achievable data rate and bit error rate is equally investigated.
Following that, experimental studies comparing both the relative impact of turbulence induced scattering on coherent and non-coherent light propagating through water and the relative impact of turbulence in different water conditions are presented. It is shown that the scintillation index increases with increasing temperature inhomogeneity in the underwater channel. These results indicate that a light beam from a non-coherent source has a greater resilience to temperature inhomogeneity induced turbulence effect in an underwater channel. These results will help researchers in simulating realistic channel conditions when modelling a light emitting diode (LED) based intensity modulation with direct detection (IM/DD) UOWC link.
Finally, a comparison of different modulation schemes in still and turbulent water conditions is presented. Using an underwater channel emulator, it is shown that pulse position modulation (PPM) and subcarrier intensity modulation (SIM) have an inherent resilience to turbulence induced fading with SIM achieving higher data rates under all conditions. The signal processing technique termed pair-wise coding (PWC) is applied to SIM in underwater optical wireless communications for the first time. The performance of PWC is compared with the, state-of-the-art, bit and power loading optimisation algorithm. Using PWC, a maximum data rate of 5.2 Gbps is achieved in still water conditions
Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process
In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov-
ernment through the ELKARTEK program (OILTWIN project, ref. KK-
2020/00052)
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Quantitative Character and the Composite Account of Phenomenal Content
I advance an account of quantitative character, a species of phenomenal character that presents as an intensity (cf. a quality) and includes experience dimensions such as loudness, pain intensity, and visual pop-out. I employ psychological and neuroscientific evidence to demonstrate that quantitative characters are best explained by attentional processing, and hence that they do not represent external qualities. Nonetheless, the proposed account of quantitative character is conceived as a compliment to the reductive intentionalist strategy toward qualitative states; I argue that an account of perceptual experience that combines a tracking account of qualitative character with my functionalist proposal of quantitative character permits replies to some notoriously difficult problems for tracking representationalism without sacrificing its chief virtues
Interactive Sonic Environments: Sonic artwork via gameplay experience
The purpose of this study is to investigate the use of video-game technology in the design and implementation of interactive sonic centric artworks, the purpose of which is to create and contribute to the discourse and understanding of its effectiveness in electro-acoustic composition highlighting the creative process. Key research questions include: How can the language of electro-acoustic music be placed in a new framework derived from videogame aesthetics and technology? What new creative processes need to be considered when using this medium? Moreover, what aspects of 'play' should be considered when designing the systems? The findings of this study assert that composers and sonic art practitioners need little or no coding knowledge to create exciting applications and the myriad of options available to the composer when using video-game technology is limited only by imagination. Through a cyclic process of planning, building, testing and playing these applications the project revealed advantages and unique sonic opportunities in comparison to other sonic art installations. A portfolio of selected original compositions, both fixed and open are presented by the author to complement this study. The commentary serves to place the work in context with other practitioners in the field and to provide compositional approaches that have been taken
Diseño de un sistema de control y planeamiento de trayectoria coordinado en el tiempo para múltiples robots móviles no holonómicos en presencia de obstáculos
La presente tesis tiene como objetivo diseñar un sistema de control y planeamiento
de trayectoria coordinado para múltiples robots móviles no holonómicos en mapas
con presencia de obstáculos variados. En esta se simula el control y planeamiento
en modelos matemáticos de tipo bicicleta.
El sistema implementado consiste de tres partes, las cuales son el planeamiento de
caminos, el generador de trayectorias y el control de seguimiento de trayectorias. El
planeamiento de caminos se dividió en tres partes. En la primera parte se desarrolló
el planeador local para un robot no holonómico, modificando el algoritmo Hybrid A*,
de manera que utilice las ecuaciones movimiento circular del móvil en vez de las
cinemáticas. Este algoritmo permite al robot encontrar los caminos que lo llevan de
una configuración de posición y orientación inicial a una final en mapas con
obstáculos variados. En la segunda parte se agregó al planeador local el
planeamiento en el tiempo, combinando a este con el algoritmo de planeamiento de
caminos en intervalos seguros (SIPP), el cual permite al robot evadir obstáculos en
el tiempo. Finalmente, en la tercera parte se desarrolló el planeador global usando
el algoritmo de búsqueda basada en conflictos (CBS), el cual resuelve los conflictos
que se presentan entre los caminos de los móviles, imponiendo restricciones en el
tiempo en el movimiento de cada uno de ellos. Por otro lado, el generador de
trayectorias es desarrollado en una única parte, en la cual, se plantea la función de
costo a optimizar, se calcula todos los gradientes y se plantea utilizar el algoritmo
de descenso de gradiente de forma desacoplada para la optimización de trayectoria
de cada móvil. Mientras que el desarrollo del sistema de control de seguimiento de
trayectoria se dividió en dos partes. En la primera se linealiza el modelo matemático
por extensión dinámica para sistemas flatness diferencial y en la segunda parte se
desarrolla el controlador LQR de cada móvil que permite seguir las trayectorias de
referencia deseadas.
Al término de la tesis se logra el planeamiento, generación de trayectoria y el
control de seguimiento de trayectoria de hasta 10 móviles no holonómicos en
mapas con obstáculos variados, evitando la colisión con los obstáculos del entorno
y la colisión con otros móviles durante el planeamiento y la optimización de
trayectoria. Asà mismo, se verifica que el planeador es capaz de resolver conflictos
en entornos propensos al atasco como mapas tipo T o H
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Know Your Bugs: A Collaborative Evaluation of a Community Health Education Module That Aims to be Accessible to Adults with Learning Disabilities
The right to health should be a fundamental right of everyone. However, despite initiatives to improve the health of adults with learning disabilities, concerns about poorer health and health inequalities remain, and have been exacerbated by the COVID-19 pandemic. Accessible health promotion can help to overcome barriers to healthy behaviour but the effectiveness of health education in infection prevention and self-care is unknown. This research aimed to understand the health education experiences of adults with learning disabilities regarding a module designed to improve knowledge about self-care, infection prevention and antibiotic use.
Beginning with a scoping review of ‘what works’, this research involved observation of the learning context in two locations and semi-structured interviews with 18 course participants to explore health knowledge and behaviour change in the short, medium and longer term. Data were analysed iteratively, addressing the realist concept of context/mechanism/outcome configurations.
Participants had a positive learning experience and gained knowledge about microbes, hand hygiene, self-care, and antibiotic use. Some participants reported behaviour change regarding handwashing and self-care. The contexts that influenced learning were personal, social, physical, active, and external. Mechanisms that interacted with these contexts to trigger learning included: accessible teaching methods, interactive resources, relaxed and effective participant interactions, facilitation of independent thinking and planning, appropriate involvement of supporters, and an inclusive and engaging educator style.
Knowledge gain and changed behaviour intentions were achieved through an engaging, interactive, and focused learning environment, underpinned by a complex and changing combination of interactions. However, further research is needed to understand effective ways of communicating health information in an education context, to understand the impact of education on behaviour change, and to identify ways in which the longer-term retention of learning can be achieved. The research proposes a draft model that can guide effective community health education provision
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