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With the growing demand for sustainable options, the existing sorting capacities are limiting the potential for fiber-to-fiber recycling. With the help of near-infrared spectroscopy (NIRS), automated sorting of textiles with high accuracy is possible due to the easy access for polymer identification. Despite the effectiveness of NIRS, some limitations of the process still limit its full potential. Possible disruptors may interfere with and disturb the identification of polymer identities and compositions in different ways. In the following thesis, additives, treatments, and other environmental factors that may hinder fiber identification are further acknowledged. The key results of the thesis state that stains and factors due to wear and tear are the most common possible disruptors that could be identified from pre-sorted post-consumer end-of-life textiles. Further on, stains of ketchup, deodorant, and oil affect the polymer recognition by lowering the recognized fiber content. Water-repellent coatings on 100 % polyamide woven fabric were not detected correctly according to the NIR scanner, as the stated polymer composition was >90 %. Even though some investigated factors, e.g., material structures, were correctly identified by the NIR scanner, the internal deviation of the knitted polyester structure indicates that porous and loose structures hold the ability to interfere with the detection of polymers. To what extent the operating software has been developed is highly relevant to the outcome of how accurate textile sorting may be.

Purpose: This research will study how hyperspectral sensoring can assess the moisture content of lettuce by monitoring its growth in indoor vertical farming. Research questions: “What accuracy can be achieved when using hyperspectral sensoring for assessing the moisture content of lettuce leaves grown in vertical farming?” “How can vertical farming contribute to sustainability in conjunction with integration of NIR spectroscopy?” Methodology: This study is an experimental study with a deductive approach in which experiments have been performed using the hyperspectral technologies singlespot sensor and the hyperspectral camera Specim FX17 to collect spectral data. To analyze the data from the experiments two regression models were used and trained to make it possible to predict future moisture content values in lettuce. In order to get a better understanding and analyze the results from the experiments, a literature review was also conducted on how hyperspectral imaging has been applied to assess the quality of food products. Conclusion: The achieved accuracies were 58.24 % and 65.54 % for the PLS regression model and the Neural Network model respectively. Employing hyperspectral sensoring as a non-destructive technique to assess the quality of food products grown and harvested in vertical farming systems, contributes to sustainability from several aspects such as reducing food waste, minimizing costs and detecting different quality attributes that affect the food products. / Syfte: Syftet med denna studie är att undersöka hur hyperspektral avbildning kan användas för att bedöma fuktigheten i sallad genom att kontrollera hur den växer i vertikal odling inomhus. Frågeställningar: “Vilken noggrannhet kan uppnås vid användning av hyperspektral avbildning för att bedöma fukthalt hos salladsblad som odlas i vertikal odling?” “Hur kan vertikal odling bidra till hållbarhet i kombination med integration av NIR spectroscopy?”  Metod: Denna studie är en experimentell studie med en kvantitativ metod inom vilken en deduktiv ansats har tillämpats genom användning av de hyperspektrala teknologierna single-spot sensor och hyperspektralkameran Specim FX17 för insamling av spektral data. För att analysera datan från experimenten skapades och tränades två olika regressionsmodeller till att möjliggöra förutsägning av framtida värden av fukthalt i sallad. För att få en bättre förståelse för och kunna göra en bättre analys av resultaten från experimenten, utfördes även en litteraturöversikt på vad tidigare forskning om tillämpningen av hyperspektral avbildning för kvalitetssäkring av matprodukter har visat. Slutsats: Noggrannheten för PLS-regressionsmodellen var 58,24 % och 65,54 % för Neural Network-modellen. Minskat matsvinn och kostnader samt upptäcka olika kvalitetsattribut som påverkar livsmedelsprodukterna är de hållbara resultaten vid bedömning av kvalitet via hyperspektral sensing.

The continuing need for greater energy security and energy independence has motivated researchers to develop new energy technologies for better energy resource management and efficient energy usage. The focus of this dissertation is the development of optical (spectroscopic) sensing methodologies for various fuels, and energy applications. A fiber-optic NIR sensing methodology was developed for predicting water content in bio-oil. The feasibility of using the designed near infrared (NIR) system for estimating water content in bio-oil was tested by applying multivariate analysis to NIR spectral data. The calibration results demonstrated that the spectral information can successfully predict the bio-oil water content (from 16% to 36%). The effect of ultraviolet (UV) light on the chemical stability of bio-oil was studied by employing laser-induced fluorescence (LIF) spectroscopy. To simulate the UV light exposure, a laser in the UV region (325 nm) was employed for bio-oil excitation. The LIF, as a signature of chemical change, was recorded from bio-oil. From this study, it was concluded that phenols present in the bio-oil show chemical instability, when exposed to UV light. A laser-induced breakdown spectroscopy (LIBS)-based optical sensor was designed, developed, and tested for detection of four important trace impurities in rocket fuel (hydrogen). The sensor can simultaneously measure the concentrations of nitrogen, argon, oxygen, and helium in hydrogen from storage tanks and supply lines. The sensor had estimated lower detection limits of 80 ppm for nitrogen, 97 ppm for argon, 10 ppm for oxygen, and 25 ppm for helium. A chemiluminescence-based spectroscopic diagnostics were performed to measure equivalence ratios in methane-air premixed flames. A partial least-squares regression (PLS-R)-based multivariate sensing methodology was investigated. It was found that the equivalence ratios predicted with the PLS-R-based multivariate calibration model matched with the experimentally measured equivalence ratios within 7 %. A comparative study was performed for equivalence ratios measurement in atmospheric premixed methane-air flames with ungated LIBS and chemiluminescence spectroscopy. It was reported that LIBS-based calibration, which carries spectroscopic information from a “point-like-volume,” provides better predictions of equivalence ratios compared to chemiluminescence-based calibration, which is essentially a “line-of-sight” measurement.

京都大学 / 新制・課程博士 / 博士(教育学) / 甲第24353号 / 教博第283号 / 新制||教||214(附属図書館) / 京都大学大学院教育学研究科教育科学専攻 / (主査)教授 MANALO Emmanuel, 教授 楠見 孝, 准教授 野村 理朗 / 学位規則第4条第1項該当 / Doctor of Philosophy (Education) / Kyoto University / DGAM

Forage quality is a principal factor in managing both herbivores and the landscapes they use. Nutrition varies across the landscape, and in turn, so do the distributions of these populations. With the rise of remote sensing technologies (i.e. satellites, unmanned aerial vehicles, and multi/hyperspectral sensors), comes the ability to index forage health and nutrition swiftly. However, no methodology has been developed which allows managers to use unmanned aerial systems to the fullest capacity. The following methodologies produce compelling evidence for predicting forage quality metrics (such as fiber, carbohydrates, and digestibility) using 5 measured bands of reflectance (Blue, Green, Red, Red Edge, and NIR), 3 derived vegetation indices (NDVI, EVI and VARI), and a variety of environmental factors (i.e. time and sun angles) in a LASSO framework. Fiber content, carbohydrates, and digestibility showed promising model performance in terms of goodness-of-fit (R2= 0.624, 0.637, and 0.639 respectively).

The ability to maintain balance is based on various processes of motor control in complex neural networks of subcortical and cortical brain structures. However, knowledge on brain processing during the execution of whole-body balance tasks is still limited. In the present study, we investigated brain activity during slacklining, a task with a high demand on balance capabilities, which is frequently used as supplementary training in various sports disciplines as well as for lower extremity prevention and rehabilitation purposes in clinical settings. We assessed hemodynamic response alterations in sensorimotor brain areas using functional near-infrared spectroscopy (fNIRS) during standing (ST) and walking (WA) on a slackline in 16 advanced slackliners. We expected to observe task-related differences between both conditions as well as associations between cortical activity and slacklining experience. While our results revealed hemodynamic response alterations in sensorimotor brain regions such as primary motor cortex (M1), premotor cortex (PMC), and supplementary motor cortex (SMA) during both conditions, we did not observe differential effects between ST and WA nor associations between cortical activity and slacklining experience. In summary, these findings provide novel insights into brain processing during a whole-body balance task and its relation to balance expertise. As maintaining balance is considered an important prerequisite in daily life and crucial in the context of prevention and rehabilitation, future studies should extend these findings by quantifying brain processing during task execution on a whole-brain level.

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This degree project studies implementation and comparison of different AI models to predict (1) solid biofuel elements including carbon, hydrogen, nitrogen, and oxygen as well as moisture content, ash content, and higher heating value (HHV) of the fuel and (2) flue gas compositions such as concentration of carbon dioxide, carbon monoxide, nitrogen, nitrogen oxides, and water content using near-infrared spectroscopy and chemometric approaches. The study executes these predictions by simulating the operation of a combined heat and power plant (CHP) that is equipped with carbon capture and storage (CCS). The focus of this study is to investigate the possibility of using Near-Infrared spectroscopy (NIRs) technology to predict the emissions from a CHP plant, which can further improve the performance of the CCS system by providing the necessary fuel data in real time. The acquired NIR data is used to develop the Artificial Intelligence (AI) models using several regression algorithms including Linear regression, Gaussian process, Support Vector Machine, Artificial Neural Network, Ensemble Trees, and Tree regression. NIR data has been pre-processed using Savitzky-Golay (SG) and Multi scatter correction (MSC) techniques. Highest accuracy has been achieved for moisture content of the fuel using Exponential Gaussian Process, where an RMSE of 2.5687 and an R2-value of 0.9 has been obtained. Indeed, only a handful of regression algorithms have shown reasonable accuracy when predicting the fuel elements, where the HHV of the fuel has been predicted poorly as none of the algorithms have been able to execute the prediction successfully which leads to negative values of R2. Prediction of flue gas composition has been done resulting in reasonable accuracies for CO2 fraction with values of 0.1051 and 0.6 for RMSE and R2 respectively. Furthermore, the computational time of the algorithms has been investigated, which indicates that some of the pre-processing techniques could improve the computational time for a certain regression model, but not for all of them. It is conclusively possible to predict fuel elements and flue gas compositions based on data acquired from NIR spectroscopy. However, great effort must be put into model development including data treatment and AI model calibration to achieve desirable precision and reliable results.

There is considerable evidence to suggest that object affordances (see Gibson, 1966) can serve to moderate volitional responses by “priming” the visuomotor system toward certain actions (e.g., Tucker & Ellis, 1998). Typically, these studies assume that shorter voluntary reaction time latencies reflect more efficient movement planning. Questions remain however, as to whether object affordances offer the same motor priming benefits in situations where the temporal window to initiate motor action precludes volitional movements (e.g., during an unexpected balance perturbation). The efficiency of balance reactions to a perturbation is dependent upon the ability for the motor system to generate short latency actions at the onset of instability. Due to the rapid nature of these actions, they are suggested to be regulated by information received prior to the perturbation. In this study, participants sat in a custom-built chair that delivered posterior perturbations and, on each trial, were presented with two of three types of stimuli within their reach (two graspable poles that varied in orientation and a flat non-graspable control). They were instructed to reach and grasp one of the poles at the moment of perturbation so as to mitigate the tilt. To assess cortical activity that may be indicative of motor planning in response to the perception of object affordances, changes in oxyhemoglobin (oxy-Hb) in the right and left premotor cortices were measured using a continuous wave fNIRS system. Results revealed a significant increase (F= 4.62, p= .043) in oxy-Hb in the right and left hemisphere (M = .023 µM) in response to objects that afford an optimal form of grasping action (mitigating excessive supination or pronation of the hand), compared to when no grasping opportunity was present (M = -.051 µM). These results suggest that affordances may be used to prime the system in the event of a balance threat. / Thesis / Master of Science (MSc)