Aircraft data acquisition

The corn blight project was different than previously planned missions in duration, coverage, and sensor configuration. The wide aerial coverage and single sensor configuration provided flexibility in mission operations that contributed to mission success. This project also provided a review of the data on a timely basis permitting sensor corrections to be made and evaluated in the field

Smart Building Data Collection and Ventilation System Energy Prediction

Data has the potential to transform our environments for the better if utilized to its full potential. A highly interesting use case of data is in relation to Smart Buildings, where IoT technology presents new possibilities. With appropriate collection and structuring of the available data, many new opportunities present themselves. In this thesis, a data gathering system is proposed for sensors in Arkivenes Hus. To illustrate the potential in the data, one specific problem is researched, namely that of indoor climate optimization and its effects on energy usage. The problem description and the development of the data system comprises identifying governing system equations using sparse identification of nonlinear dynamics, control strategy using model predictive control and various machine learning methods to predict energy usage. For a one day simulation, the proposed optimization strategy yields a 174.86% increase in energy usage. The conducted work indicates that the proposed model identification technique is unsuitable for the underlying data utilized in this work. The proposed model predictive control strategy and machine learning methods contain promising results

Smart Building Data Collection and Ventilation System Energy Prediction

Data has the potential to transform our environments for the better if utilized to its full potential. A highly interesting use case of data is in relation to Smart Buildings, where IoT technology presents new possibilities. With appropriate collection and structuring of the available data, many new opportunities present themselves. In this thesis, a data gathering system is proposed for sensors in Arkivenes Hus. To illustrate the potential in the data, one specific problem is researched, namely that of indoor climate optimization and its effects on energy usage. The problem description and the development of the data system comprises identifying governing system equations using sparse identification of nonlinear dynamics, control strategy using model predictive control and various machine learning methods to predict energy usage. For a one day simulation, the proposed optimization strategy yields a 174.86% increase in energy usage. The conducted work indicates that the proposed model identification technique is unsuitable for the underlying data utilized in this work. The proposed model predictive control strategy and machine learning methods contain promising results

Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction

Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.publishedVersionPeer reviewe

The effect of extended post-mortem ageing on the Warner–Brazler shear force of longissimus thoracis from beef heifers from two sire breeds, slaughtered at 20 or 25 mo of age

peer-reviewedwere examined. Spring-born Angus × Holstein-Friesian heifers (n = 48) and Belgian Blue × Holstein-Friesian heifers (n = 48) were slaughtered, within sire breed, at 20 or 25 mo of age. Approximately 48 h post-mortem, LT steaks (2.5 cm) were removed, and either stored at −20°C for chemical analysis or vacuum-packed, stored at 2°C for 7, 14 or 28 d post-mortem and then at −20°C pending Warner–Bratzler shear force (WBSF) analysis. Muscle from Angus-sired heifers had higher (P < 0.001) intramuscular fat (IMF) concentration, lower (P < 0.001) proportion of type IIX muscle fibres and higher (P < 0.001) proportion of type IIA and type I muscle fibres compared to muscle from Belgian Blue-sired heifers. Collagen characteristics did not differ between sire breeds. Later slaughter increased (P < 0.001) IMF concentration and decreased (P < 0.001) total and insoluble concentrations and collagen solubility. There were no interactions between the main effects for WBSF and no difference between sire breeds. Later slaughter and increasing the duration of ageing decreased (P < 0.05) WBSF. Based on threshold WBSF values in the literature, all samples would be considered tender (<39 N) after 7 d ageing. Untrained consumers are likely to detect the decrease in WBSF from 7 to 14 d ageing but not due to further ageing. Within the production system examined and based on WBSF data, extending LT ageing to 28 d is not necessary to ensure consumer satisfaction

An evaluation of tectonostratigraphy and hydrocarbon potential of the Ellingråsa Graben, Halten Terrace, offshore Mid-Norway

Ellingråsa Graben er en grabenstruktur lokalisert i den umiddelbare hengblokken til Bremstein Forkastningskompleks (BFC), i Norskehavet, 65°N. De første letebrønnene i Norskehavet ble boret og funnet tørre på denne strukturen, som siden har vært en lite beskrevet struktur i offentlig tilgjengelige publikasjoner. Denne oppgaven gir en oversikt over tektonostratigrafien og hydrokarbonpotensialet i Ellingråsa Graben. Et samspill mellom forkastningsaktivitet langs BFC i Jura og en evaporittenhet i Trias er observert, hvor listriske forkastninger som såler ut i saltintervallet, dannelse av assosiert hengblokkantiklinal og putedannelse i saltlaget er hovedtrekkene. Variasjon i sprang langs forkastningsstrøket, tykkelse på syn-rift sediment i hengblokken, erosjon av liggblokken og variasjon i strøkretning støtter en inndeling av BFC inn i 4 segmenter. En modell for dannelse av lukningen hvor brønn 6507/12-1 befinner seg er foreslått i denne opppgaven. Lukningen er foreslått å ha formet i et området med varierende strøkretning langs begge forkasntningene som avgrenser grabenen. Dette har fasilitert rollover av sedimenter i hengblokken mot hovedforkastningene i flere retninger, som har ført til dannelse av en kombinert antiklinal-horst struktur. Signifikant erosjon langs flankene av grabenen foregikk i Callovium. Inkonformiteten ved bunn Kritt er også erosiv over forkastningsskrenter. Selv om rift klimaks var fra Callovium til tidligst i Kritt, er det også tegn til syn-rift avsetninger i Tidlig Kritt. Reaktivering av forkastninger er også observert i Paleogen strata. De tørre brønnene i studieområdet kommer av umodne kildebergarter i området og mangel på langdistanse migrasjonsveier inn til de borede lukningene. Nærmere observasjon av erosjon og syn-rift sedimentasjon i studieområdet avslører mulige syn-rift sandsteiner i den ummidelbare hengblokken til BFC. Disse er forventet å være avsatt i et dypmarint miljø, men med et lokalt område av mulige grunn-marine syn-rift avsetninger ved en erodert intra-graben forkastning som er syntetisk til BFC, lokalisert sør i området. Dersom hydrokarboner har kommet inn i grabenen på dette dypet, kan disse sandsteinene utgjøre en migrasjonsrute som forbipasserer de borede, tørre prospektene. Slike syn-rift sandsteiner kan også representere reservoarer der de er helt eller delvis innkapslede i den omkringliggende skiferen slik at stratigrafiske feller blir dannet. Likevel er det en generell mangel på tegn til hydrokarboner i studieområdet, som sannsynliggjør at Ellingråsa Graben er plassert i en migrasjonsskygge

Smart Building Data Collection and Ventilation System Energy Prediction

Data has the potential to transform our environments for the better if utilized to its full potential. A highly interesting use case of data is in relation to Smart Buildings, where IoT technology presents new possibilities. With appropriate collection and structuring of the available data, many new opportunities present themselves. In this thesis, a data gathering system is proposed for sensors in Arkivenes Hus. To illustrate the potential in the data, one specific problem is researched, namely that of indoor climate optimization and its effects on energy usage. The problem description and the development of the data system comprises identifying governing system equations using sparse identification of nonlinear dynamics, control strategy using model predictive control and various machine learning methods to predict energy usage. For a one day simulation, the proposed optimization strategy yields a 174.86% increase in energy usage. The conducted work indicates that the proposed model identification technique is unsuitable for the underlying data utilized in this work. The proposed model predictive control strategy and machine learning methods contain promising results