Hyperspektral avbildning: algoritmiske fremskritt innen variabelt utvalg og anvendelser til trevitenskap

According to Beer’s Law there is a linear dependence between the absorbance of a material and the concentration of an absorbing species in the material. Thus, if one is interested in modeling the concentration of an absorbing species, it should be possible to do so by utilizing a linear model to describe the concentration of the species from a measurement of the absorbance of the material. This thesis is concerned with developing such models from hyperspectral measurements taken in the visible (vis) and near infrared (NIR) region of the electromagnetic spectrum. When developing such models, it is frequently the case that a majority of the wavelengths within a measured spectrum are not absorbed by the species of interest - and should therefore preferably be excluded from the developed model in order to optimize its performance. The process of identifying unnecessary wavelengths is often driven by trial and error, as such it tends to be time consuming and computationally demanding. During the work leading up to Paper I we discovered a conceptually very simple technique which allows calculations to be recycled when developing partial least squares (PLS) models from different combinations of wavelengths. The technique can greatly reduce the computational cost of ftting multiple regression models with various combinations of included/excluded wavelengths to a dataset. In Paper II we incorporate the fndings of Paper I into a genetic algorithm (GA) and demonstrate that the technique also can be used to simultaneously evaluate— in a computationally effcient manner—combinations of wavelengths which are preprocessed using different techniques. In Paper III and IV we develop models which solve wood science related issues. In Paper III samples of spruce (Picea abies) treated with a phosphorus-based fame retardant compound were scanned using a NIR hyperspectral camera. The resulting data was subsequently used to develop a PLS model which estimated the phosphorous content from the spectral signal. In Paper IV samples of thermally modified pine (Pinus sylvestris) were repeatedly scanned over time as they dried. The resulting time series sequences of hyperspectral NIR data was used to develop a regression model capable of estimating the moisture content of the pine from the spectra. In Paper V a generic method is developed for studying and summarizing hyperspectral time series sequences in terms of known and unknown variations. The main idea of the presented method is that spectral variations of known origin are removed from the data. The remaining residual data, containing variation of unknown origin, is then subjected to dimensionality reduction in order to identify new previously unknown variations in the data; variations which in the case of hyperspectral time series data may exhibit temporal as well as spatial patterns of interest. The developed concept was experimentally evaluated in Paper V on a piece of unmodified spruce (Picea abies) which was monitored using a vis-NIR hyperspectral camera as it dried over the course of 21 hours

Should we point them away from the sun? -A study in PV spectral tracking in a Scandinavian climate

Conventional photovoltaic solar tracking is commonly done by aligning the surface normal of a PV module with the direction from which direct solar irradiation is coming from. While this tracking design has proven to perform well in sunny climates, solar tracking is altogether less common in northern climates where the direct solar irradiance is weaker. In this study a radiative transfer model is used to approximate the wavelength distribution of incident solar radiation reaching Visby on the island of Gotland in Sweden during 2011 to investigate if the site specific irradiation of a northern climate in combination with different PV materials optical properties can result in circumstances where there are more productive things to track than the direct solar irradiation. The results of the study indicates that the performance of a tracking system to some extent depends on the choice of PV cell material used in the system and that the spectral response of different materials often make them achieve optimum productivity at slightly different tilt/azimuth combinations. The study concludes that a conventional tracking system in Visby during 2011 in general would generate approximately 10 kWh/m² less electricity compared to a theoretically optimal tracking system.Conventional photovoltaic solar tracking is commonly done by aligning the surface normal of a PV module with the direction from which direct solar irradiation is coming from. While this tracking design has proven to perform well in sunny climates, solar tracking is altogether less common in northern climates where the direct solar irradiance is weaker. In this study a radiative transfer model is used to approximate the wavelength distribution of incident solar radiation reaching Visby on the island of Gotland in Sweden during 2011 to investigate if the site specific spectral irradiance of a northern climate in combination with different PV materials optical properties can result in circumstances where there are more productive things to track than the direct solar irradiation. When the performance of photovoltaic cells is determined, under so called Standard Test Conditions, the incident solar energy is assumed to have a spectral distribution regulated by a standardized AM1.5 spectra. AM1.5 stands for Air Mass 1.5 and is supposed to represent a situation where sunlight has to travel through 1.5 times as much atmosphere compared to the shortest possible atmospheric path at sea level (AM1). Expressed as solar elevation angle AM1.5 corresponds to roughly 42 degrees above the horizon. During large parts of the year in Scandinavia the solar elevation angle is often far lower than 42° and consequently the sunlight has to travel through far more atmosphere during these times than what is assumed in the Standard Test Conditions. This additional atmospheric length causes radiation attenuation that lowers the overall broadband irradiance reaching a surface on the earth, but it does not necessarily have to diminish it completely evenly across the electromagnetic spectrum. Different atmospheric constituents all have their own characteristic locations, their absorption bands, in the spectra where they cause radiation attenuation. Depending on how long the atmospheric length is and what chemicals are in the atmosphere, the solar irradiations spectral distribution will differ from what was assumed when the cell was performance rated. Therefore, simulating the annual energy output from cells without considering the site specific wavelength distribution of incident solar energy will to some extent yield an unrealistic forecast. In this study the solar spectra during every minute of a year was simulated using a radiative transfer model together with a numerical model of the atmosphere and its annual chemical variations. The spectrally resolved irradiation was then used to calculate a unique Spectral Mismatch Factor for every minute, tilt and azimuth for eight different semiconducting materials to determine how appropriate the site specific solar energy at different orientations is for conversion to electric current. Since different PV materials have different spectral responses, the light reaching a surface oriented towards one direction could very well be more suitable for one PV material than for another. In this study the following eight PV materials were evaluated; Amorphous silicon, Cadmium Telluride, Gallium Arsenide, Gallium Indium Phosphide, Monocrystalline Silicon, Multicrystalline Silicon, Copper Indium Gallium Selenide with Zinc Oxide and Inorganic and Nanostructured Photovoltaics. The calculated Spectral Mismatch Factor was then combined with real minute-by-minute measurements of the broadband irradiance reaching the site during the year and a weighted irradiance was calculated for every minute, tilt, azimuth and material type. The weighted irradiance, which is the product of Spectral Mismatch Factor and broadband irradiance, should provide a more realistic estimation of how appropriate the incident radiation is for electricity generation since it considers the underlying wavelength distribution of the solar energy. The results of the study shows that the materials Gallium Indium Phosphide and Amorphous silicon, due to their limited range in spectral response, could be spectrally unfavoured by the Scandinavian solar energy during parts of the year when the solar elevation angle is low. The results also indicates that for every material and every day of the simulated year, there are time steps were there are, at least in theory, better things to track other than the sun. During sunny days the optimum tracking orientation is however generally very close if not exactly the same as the orientation of direct solar irradiation regardless of material. The study concludes that if a tracking system were to be built that could identify the point at which maximum weighted irradiance occurs instead of orienting itself towards the sun, the annual electricity production could be increased by around 10 kWh/m²

Orders of magnitude speed increase in Partial Least Squares feature selection with new simple indexing technique for very tall data sets

Feature selection is a challenging combinatorial optimization problem that tends to require a large number of candidate feature subsets to be evaluated before a satisfying solution is obtained. Because of the computational cost associated with estimating the regression coefficients for each subset, feature selection can be an immensely time-consuming process and is often left inadequately explored. Here, we propose a simple modification to the conventional sequence of calculations involved when fitting a number of feature subsets to the same response data with partial least squares (PLS) model fitting. The modification consists in establishing the covariance matrix for the full set of features by an initial calculation and then deriving the covariance of all subsequent feature subsets solely by indexing into the original covariance matrix. By choosing this approach, which is primarily suitable for tall design matrices with significantly more rows than columns, we avoid redundant (identical) recalculations in the evaluation of different feature subsets. By benchmarking the time required to solve regression problems of various sizes, we demonstrate that the introduced technique outperforms traditional approaches by several orders of magnitude when used in conjunction with PLS modeling. In the supplementary material, we provide code for implementing the concept with kernel PLS regression.acceptedVersio

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.

We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease

Much faster cross‐validation in PLSR‐modelling by avoiding redundant calculations

publishedVersio

Hyperspectral NIR time series imaging used as a new method for estimating the moisture content dynamics of thermally modified Scots pine

The purpose of this research is to develop a method for estimating the spatially and temporally resolved moisture content of thermally modified Scots pine (Pinus sylvestris) using remote sensing. Hyperspectral time series imaging in the NIR wavelength region (953–2516 nm) was used to gather information about the absorbance of eight thermally modified pine samples each minute as they dried during a period of approximately 20 h. After preprocessing the collected spectral data and identifying an appropriate wavelength selection, partial least squares regression (PLS) was used to map the absorbance data of each pine sample to a distribution of moisture contents within the samples at different time steps during the drying process. To enable separate studying and comparison of the drying dynamics taking place within the early- and latewood regions of the pine samples, the collected images were spatially segmented to separate between early- and latewood pixels. The results of the study indicate that the 1966–2244 nm region of a NIR spectrum, when preprocessed with extended multiplicative scatter correction and first order derivation, can be used to model the average moisture content of thermally modified pine using PLS. The methods presented in this paper allows for estimation and visualization of the intrasample spatial distribution of moisture in thermally modified pine wood.publishedVersio

Estimation of phosphorus-based flame retardant in wood by hyperspectral imaging—a new method

It is recognised that flame retardant chemicals degrade and leach out of flame-protected wood claddings when exposed to natural weathering. However, the ability to survey the current state of a flame retardant treatment applied to a wood cladding, an arbitrary length of time after the initial application, is limited today. In this study, hyperspectral imaging in the near infrared to short-wavelength infrared region is used to quantify the amount of flame retardant present on wooden surfaces. Several sets of samples were treated with various concentrations of a flame retardant chemical and scanned with a push broom hyperspectral camera. An inductively coupled plasma (ICP) spectroscopy analysis of the outermost layer of the treated samples was then carried out in order to determine each sample’s phosphorus content, the active ingredient in the flame retardant. Spectra from the hyperspectral images were pre-processed with extended multiplicative scatter correction, and the phosphorus content was modelled using a partial least squares (PLS) regression model. The PLS regression yielded robust predictions of surface phosphorus content with a coefficient of determination, R2, between 0.8 and 0.9 on validation data regardless of whether the flame retardant chemical had been applied to the surface of the wood or pressure-impregnated into it. The result from the study indicates that spectral imaging around the 2400–2531nm wavelength region is favourable for quantifying the amount of phosphorus-based flame retardant contained in the outermost layer of non-coated wooden claddings. The results also reveal that the uptake of phosphorus-based flame retardant does not occur uniformly throughout the wood surface, but is to a larger extent concentrated in the earlywood regions than in the latewood.publishedVersio

Monitoring and simulation of diurnal surface conditions of a wooden facade

The hygrothermal surface conditions of a façade is important for the degradation of the façade material as well as for the energy budget of the building. The distribution of short term variations of the temperature and moisture in the façade is often neglected in degradation studies that will typically treat the whole façade equally. The moisture and temperature variations are especially important in porous building materials where water dependent biological and physical processes are the main degrading factors. In this study the diurnal cycle of the surface temperature is measured with conventional temperature probes as well as with infra-red camera. In addition, the moisture content of the wooden façade is measured with resistive measurement technique. The measurements are used to validate simulations of the spatial temperature and moisture variations on the façade. The study shows large variations of surface conditions on the façade and that the simulations reproduces the measurements within a high degree of accuracy.publishedVersio

Synthetic generation of passive infrared motion sensor data using a game engine

Quantifying the number of occupants in an indoor space is useful for a wide variety of applications. Attempts have been made at solving the task using passive infrared (PIR) motion sensor data together with supervised learning methods. Collecting a large labeled dataset containing both PIR motion sensor data and ground truth people count is however time-consuming, often requiring one hour of observation for each hour of data gathered. In this paper, a method is proposed for generating such data synthetically. A simulator is developed in the Unity game engine capable of producing synthetic PIR motion sensor data by detecting simulated occupants. The accuracy of the simulator is tested by replicating a real-world meeting room inside the simulator and conducting an experiment where a set of choreographed movements are performed in the simulated environment as well as the real room. In 34 out of 50 tested situations, the output from the simulated PIR sensors is comparable to the output from the real-world PIR sensors. The developed simulator is also used to study how a PIR sensor’s output changes depending on where in a room a motion is carried out. Through this, the relationship between sensor output and spatial position of a motion is discovered to be highly non-linear, which highlights some of the difficulties associated with mapping PIR data to occupancy count.