<p>Current methods of broadcast herbicide application cause a negative environmental and economic impact. Computer vision methods, specifically those related to object detection, have been reported to aid in site-specific weed management procedures to target apply herbicide on per-weed basis within a field. However, a major challenge to developing a weed detection system is the requirement for properly annotated training data to differentiate between weeds and crops under field conditions. This research involved creating an annotated database of weeds by using UAS-acquired imagery from corn and soybean research plots located in North-central Indiana. A total of 27,828 RGB; 108,398 multispectral; and 23,628 thermal images, were acquired using FLIR Duo Pro R sensor that was attached to a DJI Matrice 600 Pro UAS. An annotated database of 306 RGB images, organized into monocot and dicot weed classes, was used for network training. Two Deep Learning networks namely, DetectNet and You Only Look Once version 3 (YOLO ver3) were subjected to five training stages using four annotated image sets. The precision for weed detection ranged between 3.63-65.37% for monocot and 4.22-45.13% for dicot weed detection. This research has demonstrated a need for creating a large annotated weed database for improving precision of deep learning algorithms through better training of the network.</p>

In this dissertation, we investigate the macroscopic magnetic properties of bulk high-temperature superconductors (HTS) containing an array of artificial holes in view of enhancing their performances. The study involves a numerical modelling part and an experimental characterization part. In each part, novel concepts are highlighted and detailed. In particular, we develop a three-dimensional finite-element method (FEM) for calculating the magnetic field penetration in HTS where a single time-step is used in the case of a linearly varying applied magnetic field, and we probe the magnetic field in the volume of drilled samples with the help of microcoils inserted inside the holes. The thesis starts with an introductory chapter that describes the general concept of high-temperature superconductivity and particularly draws the attention on the interests and on the synthesis of drilled structures. Then, we detail the modelling tools that are used for evaluating the magnetic properties of drilled samples. Three models are taken into account: (1) the numerical Bean model which is a generalization of the Bean model for arbitrary cross sections where the samples are assumed to have an infinite height; (2) a 2D finite element model implemented in the open source solver GetDP for samples with an infinite height and assuming a power law relationship, that is characterized by a critical exponent n, between the electric field, E, and the current density, J; (3) a 3D finite element model with the same equations as those of model (2), but where these are solved in a three-dimensional sample with a finite height. For large values of n, both FEM models use the properties of a slow magnetic diffusion to reduce the number of time steps. In particular, the trapped flux can be calculated with only two time-steps: during the first step, the applied magnetic flux density is increased with a constant sweep rate to a maximum value, it then decreases to zero with the same sweep rate during the second step. The models are first used in simple geometries where they are compared to other available techniques. These are next applied to drilled samples. A systematic numerical study of the influence of the holes on the magnetic properties of the sample is reported. A single hole perturbs the critical current flow over an extended region that is bounded by a discontinuity line, where the direction of the current density changes abruptly. In samples with several holes and a given critical current density, we demonstrate that the trapped magnetic flux is maximized when the centre of each hole is positioned on one of the discontinuity lines produced by the neighbouring holes. For a cylindrical sample, we construct a polar triangular hole pattern that exploits this principle; in such a lattice, the trapped field is 20% higher than in a squared lattice, for which the holes do not lie on discontinuity lines. These results are experimentally validated. Two parallelepipedic samples are drilled with two different hole lattices. The trapped magnetic flux density of these samples is characterized by a Hall probe mapping before and after drilling holes. The sample in which the holes are aligned on the discontinuity lines exhibits the smallest magnetization drop that results from the hole drilling. Then, we resort to a novel experimental technique using microcoils inside the holes to characterize the local magnetic properties in the volume of drilled samples. In a given hole, three different penetration regimes can be observed when the sample is subjected to an AC magnetic field: (i) the shielded regime, where no magnetic flux threads the hole; (ii) the gradual penetration regime, where the amplitude of the magnetic field scales with the applied field; and (iii) the flux concentration regime, where the magnetic field exceeds that of the applied field. A comparison of the measurements with simple models assuming an infinite height shows that the holes may serve as a return path for the demagnetizing field lines. In the case of a pulsed field excitation, that measurement technique also allows us to estimate the trapped magnetic flux density in the volume of the sample and compare it with that on the surfaces. Moreover, the penetration of a magnetic pulse from hole to hole is described in the median plane and on the surface and the differences of penetration speeds are explained. Finally, we investigate the magnetic properties of drilled samples whose holes are filled with a ferromagnetic powder. To this aim, we use experimental techniques (Hall probe mapping techniques, together with measurements of the volume magnetization and of the levitation force between the HTS sample and a permanent magnet) and a numerical model (3D FEM) to characterize the modification of the magnetic properties resulting from the impregnation of the holes with AISI 410 ferromagnetic powder. Numerical results support the experimental observations and give clues to understand the mutual interaction between the HTS sample and the ferromagnetic powder inserted in its holes. In particular, the Hall probe mappings of the distribution of the trapped flux above the non-impregnated and impregnated samples reveal an increase of trapped flux after impregnation that is confirmed by simulations.

Co-generation is an efficient energy system that generates steam and electricity simultaneously. In ordinary operation, fuel cost accounts for more than 60% of the operational cost. As a result, the boiler efficiency and optimization level of co-generation are both high. To achieve further energy conservation, objectives of this thesis are to find the Profit-maximizing dispatch and efficiency enhancing strategy of the co-generation systems under deregulation. In a coexistent environment of both Bilateral and Poolco-based power market, there are bid-based spot dispatch, and purchases and sales agreement-based contract dispatch. For profit-maximizing dispatch, the steam of boilers, fuels and generation output will be obtained by using the SQP(Sequential Quadratic Programming ) method. In order to improve the boiler efficiency, this thesis utilizes artificial neural networks(ANN) and evolutionary programming(EP) methods to search for the optimal operating conditions of boilers. A co-generation system (back-pressure type and extraction type) is used to illustrate the effectiveness of the proposed method.

Thesis (M.S.)--University of Michigan, 1951.

Faculty of Engineering and Built Enviroment School of Civil and Enviromental Engineering 0105772w imasengo@yahoo.com / The problem of missing data, insufficient length of hydrological data series and poor quality is common in developing countries. This problem is much more prevalent in developing countries than it is in developed countries. This situation can severely affect the outcome of the water systems managers’ decisions (e.g. reliability of the design, establishment of operating policies for water supply, etc). Thus, numerous data interpolation (infilling) techniques have evolved in hydrology to deal with the missing data. The current study presents merely a methodology by combining different approaches and coping with missing (limited) hydrological data using the theories of entropy, artificial neural networks (ANN) and expectation-maximization (EM) techniques. This methodology is simply formulated into a model named ENANNEX model. This study does not use any physical characteristics of the catchment areas but deals only with the limited information (e.g. streamflow or rainfall) at the target gauge and its similar nearby base gauge(s). The entropy concept was confirmed to be a versatile tool. This concept was firstly used for quantifying information content of hydrological variables (e.g. rainfall or streamflow). The same concept (through directional information transfer index, i.e. DIT) was used in the selection of base/subject gauge. Finally, the DIT notion was also extended to the evaluation of the hydrological data infilling technique performance (i.e. ANN and EM techniques). The methodology was applied to annual total rainfall; annual mean flow series, annual maximum flows and 6-month flow series (means) of selected catchments in the drainage region D “Orange” of South Africa. These data regimes can be regarded as useful for design-oriented studies, flood studies, water balance studies, etc. The results from the case studies showed that DIT is as good index for data infilling technique selection as other criteria, e.g. statistical and graphical. However, the DIT has the feature of being non-dimensionally informational index. The data interpolation iii techniques viz. ANNs and EM (existing methods applied and not yet applied in hydrology) and their new features have been also presented. This study showed that the standard techniques (e.g. Backpropagation-BP and EM) as well as their respective variants could be selected in the missing hydrological data estimation process. However, the capability for the different data interpolation techniques of maintaining the statistical characteristics (e.g. mean, variance) of the target gauge was not neglected. From this study, the relationship between the accuracy of the estimated series (by applying a data infilling technique) and the gap duration was then investigated through the DIT notion. It was shown that a decay (power or exponential) function could better describe that relationship. In other words, the amount of uncertainty removed from the target station in a station-pair, via a given technique, could be known for a given gap duration. It was noticed that the performance of the different techniques depends on the gap duration at the target gauge, the station-pair involved in the missing data estimation and the type of the data regime. This study showed also that it was possible, through entropy approach, to assess (preliminarily) model performance for simulating runoff data at a site where absolutely no record exist: a case study was conducted at Bedford site (in South Africa). Two simulation models, viz. RAFLER and WRSM2000 models, were then assessed in this respect. Both models were found suitable for simulating flows at Bedford.

<p> The phenomenon of genetic recombination is of fundamental importance to the evolution and adaptation of species, and is a valuable laboratory aid to the biological scientist. Probable mechanisms of control of recombination are largely unknown, due partly to the difficulty of obtaining artificial mutants affecting the process. The studies reported here avoid this difficulty by the use of different factors controlling recombination which occur naturally in the species Neurospora crassa and N. sitophila. Studies of hybrid N. crassa strains carrying factors from N. sitophila are described, and some models for the control of genetic recombination are discussed. </p> / Thesis / Master of Science (MSc)

Artifical intelligence is a part of the progress of the digitalization in the society. Language models such as ChatGPT have the ability to answer questions posed by the user as well as to produce texts that resemble human writing. However, the use of language models is not without risk. They can be used as a tool to encourage cheating on school assignments or to contribute to disinformation. A consequence of the development of AI is that knowledge is obtained in different ways compared to the past, which has to come to transform the traditional teaching methods in schools. The purpose of the study is to investigate how AI affects the tuition in social studies and also what experience social studies teachers have of AI in connection with teaching. The study focuses in upper secondary school in Sweden. The empirical material has been collected through semi-structured interviews. The results of the study show that the teachers see both opportunities and challenges with AI in teaching. AI can be used as a resource by both teachers and students according to the teachers participating in this study. However the result shows that AI also can have an negative impact on the students learning process. Regardless, teachers have been forced to make changes in their teaching.

No description available.

Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process.

Aus der Einführung: "Stehen wir am Rande einer bio-nanotechnologischen getriebenen Revolution, die unsere Art zu leben, zu arbeiten und miteinander umzugehen grundlegend verändern wird? Welchem gesellschaftspolitischen, wirtschaftlichen und technologischen Wandel haben wir uns zu stellen? Langfristige Entwicklungszyklen (Kondratieff, Schumpeter) führen zur nachhaltigen Weiterentwicklung der Zivilisation. Mittelfristige Entwicklungen wie die Trends Globalisierung, Urbanisierung, Digitalisierung (Miniaturisierung) und Humanisierung (Individualisierung), die immer stärker unser Umfeld und Handeln beeinflussen führen zu ganzheitlichen, weltumspannenden Grundtendenzen der gesellschaftlichen Weiterentwicklung. Die technologischen "Enabler" Computing, Biotechnology, Artifical Intelligence, Robotik, Nanotechnology, Additive Manufacturing und Design Thinking wirken beschleunigend auf die gesellschaftlichen Entwicklungen ein. Die technologischen Möglichkeiten beschleunigen sowohl gesellschaftspolitische Zyklen und zivilisatorische Anpassungen. Durch rasanten technologischen, wissenschaftlichen Fortschritt, zunehmende Globalisierungswirkungen, beschleunigte Urbanisierung und aber auch politischer Interferenzen sind die Veränderungsparameter eines dynamischen Geschäftsumfelds immer schnellere Transformationen ausgesetzt. Alle diese Richtungen zeigen das unsere gesellschaftliche Entwicklung inzwischen stark durch die Technik getrieben ist. Ob dies auch heißt, dass wir den Punkt der Singularität (Kurzweil) absehbar erreichen ist dennoch noch offen. ..."