Investigation of the physiological basis of yield differences in Norwegian spring wheat

Norway has a total land area of 324,000 km2 of which only 3% is arable. Moreover, the climate conditions allow a short growing season for agriculture. Despite these challenges, Government policies are directed towards increasing food production and sustainability. Wheat is a major contributor to the food and feed nutrition of the country. Furthermore, for the past 40 years, plant breeding has improved the yields of the Norwegian spring wheat cultivars and this study is set to find the physiological reasons why the new cultivars yield higher than the older ones. The experiment consisted of 24 spring wheat cultivars which represents the history of wheat breeding in Norway. The experiment took place at two locations (Ås and Staur) in the south eastern part of the country, between May and September 2017. Two nitrogen levels of fertilization were adopted in this study, 7.5kg/daa and 15kg/daa. Some of the physiological traits measured were chlorophyll content, light interception, plant height, harvest index and phenological phases (days to heading and days to maturity), above ground biomass and the yield components. Images were taken and analysed for canopy spectral reflectance indices and were compared with traditional data. Grain yield was found to be strongly correlated with the number of grains per square meter, grain weight and the length of grain filling. Light interception and chlorophyll content were poorly correlated to grain yield, but their relationship was responsible for a large part of the variation between the cultivars. Spectral indices like MERIS Terrestrial Chlorophyll index and NDVI were associated with Chlorophyll content and Light interception respectively. Future experiments should, therefore, focus much on the period from heading to maturity and collecting much data to help predict yields.Kun 3% av det totale landarealet (324,000 km2 ) i Norge er dyrkbar jord. I tillegg bidrar de klimatiske forholdene til en kort vekstsesong . Til tross for disse utfordringene er den statlige politikken å øke matproduksjon og bærekraft. Hvete er en hovedkilde til mat og fôr i landet. I løpet av de siste 40 årene har planteforedling forbedret avlingen til norske vårhvetesorter og denne studien har som mål å finne de fysiologiske forklaringene på hvorfor de nye sortene har høyere avling enn de eldre sortene. Forsøket besto av 24 vårhvetesorter som representerer historisk hveteforedling i Norge. Forsøket ble utført på to steder (Ås og Staur) i den sørøstlige delen av Norge mellom mai og september 2017. To nivåer av nitrogengjødsling ble brukt i studien, 7.5 kg/daa og 15 kg/daa. Noen av de fysiologiske egenskapene som ble målt var klorofyllinnhold, lysoppfanging (light interception), strålengde, kornprosent (harvest index), fenologisk stadium (dager til skyting og dager til modning), overjordisk biomasse og avlingskomponenter. Det ble tatt bilder, og analyser av bladverkets spektralrefleksjon ble utført og sammenlignet med tradisjonelle data. Det ble funnet at kornavling var sterkt korrelert med antall korn per kvadratmeter, kornvekt og lengden på kornfyllingsperioden. Studien viser også at kornavling har økt med årene. Spektrale indekser som MERIS terrestrisk klorofyllindeks og NDVI var assosiert med henholdsvis klorofyllinnhold og lysoppfanging. Framtidige forsøk bør fokusere på perioden fra skyting til modning og samle mye data for å kunne predikere avling.submittedVersionM-P

Reconfigurable Autopilot Design using Nonlinear Model Predictive Control: Application to High Performance and Autonomous Aircraft

The work presented in this thesis examines several aspects of Nonlinear ModelPredictive Control (NMPC) that display and confirm its promising potentials as apowerful reconfigurable control scheme. The effects of significant nonlinearities andthe intrinsically unstable nature of high performance fighter aircraft, among otherchallenges, have been shown to be well handled in the NMPC framework. Thiswork illustrates how complex control and stability augmentation measures (whichare normally realized through ad hoc mode switching strategies) can be formulatedand implemented as NMPC objectives and constraints. Further suggestions onrobustness strategies for model/plant mismatch and compensation for couplingeffects which are not properly accounted for, have been presented and examined inthis work. Results on fault tolerance of NMPC are also presented and discussed inthis thesis. In this direction, NMPC has been shown to have unique inherent faultdetection capabilities due to its effective utilization of feedback and its internalmodel predictions. Different types of actuator/control surface failures, includingextreme cases of total actuator failure are examined as test cases for the NMPCreconfigurable fault tolerant control scheme developed in this work. The NMPCautopilots are designed for an F-16 fighter aircraft, and the implementation andsimulations were done using ACADO nonlinear optimization solver, interfaced withthe MATLAB/Simulink environment

Reconfigurable Fault Tolerant Flight Control based on Nonlinear Model Predictive Control

(NMPC) is shown to have potentials for reconfigurable fault tolerant control of highly nonlinear, intrinsically unstable, high performance aircraft. Results on fault tolerance of NMPC autopilots were obtained for an F-16 fighter aircraft model, without the implementation of any prestabilizing controllers. It has been shown that NMPC has inherent fault detection capabilities due to its effective utilization of feedback and its internal model predictions. Actuator (control surface) faults, including extreme cases of total actuator failure are examined as test cases for the NMPC reconfigurable fault tolerant control scheme developed in this work. The NMPC autopilots implementation and simulations were done using the ACADO nonlinear optimization solver. I

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Proactive Collision Avoidance for ASVs using A Dynamic Reciprocal Velocity Obstacles Method

We propose a collision avoidance method that incorporates the interactive behavior of agents and is proactive in dealing with the uncertainty of the future behavior of obstacles. The proposed method considers interactions that will be experienced by an autonomous surface vessel (ASV) in an environment governed by the international regulations for preventing collisions at sea (COLREGs). Our approach aims at encouraging dynamic obstacles to cooperate according to COLREGs. Therefore, we propose a strategy for assessing the cooperative behavior of obstacles, and the result of the assessment is used to adapt collision avoidance decisions within the Reciprocal Velocity Obstacles (RVO) framework. Moreover, we propose a predictive approach to solving known limitations of the RVO framework, and we present computationally feasible extensions that enable the use of complex dynamic models and objectives suitable for ASVs. We demonstrate the performance and potentials of our method through a simulation study, and the results show that the proposed method leads to proactive and more predictable ASV behavior compared with both Velocity Obstacles (VO) and RVO, especially when obstacles cooperate by following COLREGs.

Structure Exploitation of Practical MPC Formulations for speeding up First-Order Methods

This paper presents structure exploitation techniques that lead to faster convergence of first-order methods for practical Model Predictive Control (MPC) formulations. We exploit the special structure of output box constraints as well as input bound and rate constraints. The output constraints are included in the MPC objective as exact penalty functions, in order to avoid feasibility issues due to e.g. plant-model mismatch. Observations from a new derivation of exact penalty functions enable us to formulate exact penalty functions that do not require additional auxiliary variables if first-order solution methods are used. We use the dual fast gradient method to illustrate the effectiveness of our approach. An average speedup of ×8 and a worst case speed-up of ×6 were obtained, compared with the fastest state-of-the-art first-order method for a subsea separation process. Moreover, hardware-in-theloop simulations using an ANSI C implementation on a PLC reveal that our first-order solver outperforms the fastest secondorder solver deployed for the subsea separation process

Neural network approach to sea-level modeling case study of a storm surge in the gulf of trieste in early december 2008

Na mnogih območjih so tablice plimovanja priročno orodje za napovedovanje morskega vodostaja. Sektor za hidrološke prognoze Agencije RS za okolje za napovedovanje morskega vodostaja v Tržaškem zalivu pogosto uporablja tablice plimovanja skupaj s harmonično analizo. Meteorološki vplivi, kot so gradient zračnega tlaka, veter in lastno nihanje morja vzdolž glavne osi Jadrana, so se mnogokrat izkazali kot pomembni dejavniki, ki vplivajo na višino vodostaja v Tržaškem zalivu. Ti dejavniki so v harmonične analize vključeni le posredno. Poleg tega za uporabne kratkoročne prognoze s pomočjo harmonične analize potrebujemo veliko število natančno umerjenih modelskih parametrov. Novejše raziskave so pokazale, da lahko uporaba umetnih nevronskih mrež bistveno izboljša napovedovanje vodostajev, če le vnesemo ustrezne vhodne spremenljivke (npr. predhodni vodostaj, zračni tlak, hitrost in smer vetra, tablice plimovanja itd.) Na dogodku neurnega vala (ang. storm surge) in poplavljanja morja na slovenski obali v začetku decembra 2008 smo izdelali analizo s pomočjo umetne nevronske mreže. Rezultati uporabe nevronskih mrež so dobro primerljivi s trenutno uporabljanimi konvencionalnimi metodami napovedovanja višine morskih gladin.Tide tables can be a useful tool for sea-level forecasting in many areas. Slovenian operational service for hydrological forecasts at the Environmental Agency of the Republic of Slovenia frequently deploys tide tables alongside least square harmonic analysis to predict maximum sea levels in the Gulf of Trieste. Meteorological influences such as pressure gradient, wind stress and induced basin eigenoscillations (seiches) along the main axis of the Adriatic basin have repeatedly been proven as important factors influencing the sea level in the Gulf of Trieste. They are, however, only indirectly included in the harmonic analysis which in itself requires a large number of carefully tuned model parameters in order to make useful short-range forecasts. A number of recent reports show that an artificial neural network (ANN) can greatly improve sea level forecasts, providing we supply it with suitable input variables (ie. previous water levels, air pressure, wind speed, wind direction, tide charts etc.) We report on an ANN-based analysis of the recent storm surge and flooding events at the Slovenian coast in the beginning of December 2008. The ANN model compares favourably with the currently used conventional forecasting methods

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