Throughout the scientific world the topic of cannabis usage and its link with psychosis seems to be discussed intensively. Considering the fact that the Czech Republic is a country with one of the highest prevalence of cannabis usage in the world it becomes a sensitive issue even in our circumstances. In the theoretical part of the work we attempted to review current knowledge of a link between cannabinoid system, canabis usage and psychosis and to point out possible future therapeutic potential of cannabinoids in the treatment of psychotic diseases. In the practical part of the work we focused on verification of propsychotic features of THC in animal model with particular attention to validation of acute subcutaneous admonistration of this drug as a novel cannabinoid model of psychosis. At the same time we tried to elucidate antipsychotic effect of CBD in this model. We tested these hypotheses in two behavioral tests (open field test, PPI ASR) and electrophysiologically (quantitative EEG). The whole analysis is enriched with pharmacokinetic data from subcutanneous and oral administration of cannabinoids. Powered by TCPDF (www.tcpdf.org)

Developing an Automatic Speech Recognition (ASR) system for the Somali language, though not novel, is not actively explored; hence there has been no success in a model for conversational speech. Neither are related works accessible as open-source. The unavailability of digital data is what labels Somali as a low resource language and poses the greatest impediment to the development of an ASR for Somali. The incentive to develop an ASR system for the Somali language is to contribute to reducing the Maternal Mortality Rate (MMR) in Somalia. Researchers acquire interview audio data regarding maternal health and behaviour in the Somali language; to be able to engage the relevant stakeholders to bring about the needed change, these audios must be transcribed into text, which is an important step towards translation into any language. This work investigates available ASR for Somali and attempts to develop a prototype ASR system to convert Somali audios into Somali text. To achieve this target, we first identified the available open-source systems for speech recognition and selected the DeepSpeech engine for the implementation of the prototype. With three hours of audio data, the accuracy of transcription is not as required and cannot be deployed for use. This we attribute to insufficient training data and estimate that the effort towards an ASR for Somali will be more significant by acquiring about 1200 hours of audio to train the DeepSpeech engine

Voice user interfaces have been growing in popularity and with them an interest for open vocabulary keyword spotting. In this thesis we focus on one particular approach to open vocabulary keyword spotting, query by example keyword spotting. Three types of query by example keyword spotting approaches are described and evaluated: sequence distances, speech to phonemes and deep distance learning. Evaluation is done on a series of custom tasks designed to measure a variety of aspects. The Google Speech Commands benchmark is used for evaluation as well, this to make it more comparable to existing works. From the results, the deep distance learning approach seem most promising in most environments except when memory is very constrained; in which sequence distances might be considered. The speech to phonemes methods is lacking in the usability evaluation. / Röstgränssnitt har växt i populäritet och med dem ett intresse för öppenvokabulärnyckelordsigenkänning. I den här uppsatsen fokuserar vi på en specifik form av öppenvokabulärnyckelordsigenkänning, den s.k nyckelordsigenkänning- genom- exempel. Tre typer av nyckelordsigenkänning- genom- exempel metoder beskrivs och utvärderas: sekvensavstånd, tal till fonem samt djupavståndsinlärning. Utvärdering görs på konstruerade uppgifter designade att mäta en mängd olika aspekter hos metoderna. Google Speech Commands data används för utvärderingen också, detta för att göra det mer jämförbart mot existerade arbeten. Från resultaten framgår det att djupavståndsinlärning verkar mest lovande förutom i miljöer där resurser är väldigt begränsade; i dessa kan sekvensavstånd vara av intresse. Tal till fonem metoderna visar brister i användningsuvärderingen.

Recycled concrete is among the rising eco-friendly construction materials which helps to reduce waste and the need for new natural resources. However, such concrete may present previous deterioration due to, for instance, alkali-silica reaction (ASR), which is an ongoing distress mechanism that may keep being developed in the recycled material. This work aims to evaluate the potential of further distress and crack development (i.e. initiation and propagation) of AAR-affected RCA concrete in recycled mixtures displaying distinct past damage degrees and reactive aggregate types. Therefore, concrete specimens incorporating two highly reactive aggregates (Springhill coarse aggregate and Texas sand) were manufactured in the laboratory and stored in conditions enabling ASR development. The specimens were continuously monitored over time and once they reached marginal (0.05%) and very high (0.30%) expansion levels, they were crushed into RCA particles and re-used to fabricate RCA concrete. The RCA specimens were then placed in the same previous conditions and the “secondary” ASR-induced development monitored over time. Results show that the overall damage in ASR-affected RCA concrete is quite different from affected conventional concrete, especially with regards to the severely damaged RCA particles, where ASR is induced by a reactive coarse aggregate, as the RCA particle itself may present several levels of damage simultaneously caused by past/ongoing ASR and newly formed ASR. Moreover, the influence of the original damage extent in such RCA concrete was captured by the slightly damaged RCA mixture eventually reaching the same damage level as the severely damaged mixture. Furthermore, the original extent of deterioration influence the “secondary” induced expansion and damage of RCA concrete since the higher the original damage level, the higher the cracks numbers and lengths observed in the RCA concrete for the same expansion level whereas wider cracks are generated by RCA having previously been subjected to slight damage thus indicating the difference in the distress mechanism as a function of original extent of damage. In addition, it has been found that distress on RCA containing a reactive sand generates and propagates from the residual mortar (RM) into the new mortar (NM) as opposed to RCA containing a reactive coarse aggregate, being generated and propagated from the original coarse aggregate (i.e. original virgin aggregate – OVA) into the NM. Likewise, RCA containing a reactive sand caused longer and higher number of cracks for the same “secondary” induced expansion than the RCA made of reactive coarse aggregate. Finally, novel qualitative and descriptive models are proposed in this research to explain ASR-induced distress generation and propagation on RCA mixtures made of reactive fine and coarse aggregates.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21077号 / 工博第4441号 / 新制||工||1690(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 河野 広隆, 教授 高橋 良和, 准教授 山本 貴士 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This dissertation presents the design of a novel joining-in-type humanoid robot-assisted language learning that uses two robots to conduct a goal-oriented conversation with the human learner to practice English as a second language. The system uses implicit learning as the main learning style to teach the usage of a specific expression form. A mix of tutoring and peer learning is implemented in the course of a three-party conversation. This learning style enables the learner to gain linguistic knowledge, and at the same time it improves the performance of the speech recognition engine. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Controlling external actions with the voice is something humans have tried to do for a long time. There are many different ways to implement a voice control system, and many of these applications require internet connections. Leaving the application area limited, as commercially available voice controllers have been stagnating behind due to the cost of developing and maintaining. In this project an artifact was created to work as an easy to use, generic, voice controller tool that allows the user to easily create different voice commands that can be implemented in many different applications and platforms. The user shall have no need of understanding or experience of voice controls in order to use and implement the voice controller.

This thesis evaluates different controllers for traction control on an electric forklift truck and has been done in cooperation with Toyota Material Handling Manufacturing Sweden. The need for a traction control system has increased with the introduction of lithium-ion batteries replacing the older lead-acid batteries, reducing the battery weight and therefore the downward force on the driving wheel increasing the risk for slip. The forklift truck was modelled using Simulink and validated by experiment. Different possible control strategies were investigated and three were chosen for implementation in simulation. These were controllers based on Model Following Control, Maximum Transmissible Torque Estimation and Sliding Mode Control. Model Following Control makes use of a nominal model to compare actual wheel speed values with nominal wheel speed values to determine if slip is occurring, Maximum Transmissible Torque Estimation makes use of a closed-loop disturbance observer to compute the maximum transmissible torque possible without inducing slip and using it as a limitation on the input signal, and Sliding Mode Control uses different functions to \say{slide} along a sliding surface to stay around a specific slip value. All three controller types were developed both as speed controlled and torque controlled. All of the controllers could reduce slip heavily in simulation. The Maximum Transmissible Torque Estimation controller reduced slip the most and kept oscillations at a minimum, but was not as responsive as the others to driver commands. The conclusion was that the controller of choice would depend on the working environment of the forklift truck. In a low friction environment where slip is expected to occur often, the Maximum Transmissible Torque Estimation controller is advisable, while the other two would be a better choice for environment with low slip occurrence. The use of torque control, while often better with regards to decreasing slip, could not be advised due to a perceived increase in implementation cost.

Testing oral proficiency is an important, but often neglected part of the foreign language classroom. Currently accepted methods in testing oral proficiency are timely and expensive. Some work has been done to test and implement new assessment methods, but have focused primarily on English or Spanish (Graham et al. 2008). In this thesis, I demonstrate that the processes established for English and Spanish elicited imitation (EI) testing are relevant to French EI testing. First, I document the development, implementation and evaluation of an EI test to assess French oral proficiency. I also detail the incorporation of the use of automatic speech recognition to score French EI items. Last, I substantiate with statistical analyses that carefully engineered, automatically scored French EI items correlate to a high degree with French OPI scores.

The Missionary Training Center (MTC), affiliated with the Church of Jesus Christ of Latter-day Saints, needs a reliable and cost effective way to measure the oral language proficiency of missionaries learning Spanish. The MTC needed to measure incoming missionaries' Spanish language proficiency for training and classroom assignment as well as to provide exit measures of institutional progress. Oral proficiency interviews and semi-direct assessments require highly trained raters, which is costly and time-consuming. The Elicited Imitation (EI) test is a computerized, automated test that measures oral language proficiency by having the participant hear and repeat utterances of varying syllable length in the target language. It is economical, simple to administer, and rate. This dissertation outlined the process of creating and scoring an EI test for the MTC. Item Response Theory (IRT) was used to analyze a large bank of EI items. The best performing 43 items comprise the final version MTC Spanish EI test. Questions about what linguistic features (syllable length, grammatical difficulty) contribute to item difficulty were addressed. Regression analysis showed that syllable length predicted item difficulty, whereas grammar difficulty did not.