Laser scanners provide a three-dimensional sampled representation of the surfaces of objects. The spatial resolution of the data is much higher than that of conventional surveying methods. The data collected from different locations of a laser scanner must be transformed into a common coordinate system. If good a priori alignment is provided and the point clouds share a large overlapping region, existing registration methods, such as the Iterative Closest Point (ICP) or Chen and Medioni’s method, work well. In practical applications of laser scanners, partially overlapping and unorganised point clouds are provided without good initial alignment. In these cases, the existing registration methods are not appropriate since it becomes very difficult to find the correspondence of the point clouds. A registration method, the Geometric Primitive ICP with the RANSAC (GPICPR), using geometric primitives, neighbourhood search, the positional uncertainty of laser scanners, and an outlier removal procedure is proposed in this thesis. The change of geometric curvature and approximate normal vector of the surface formed by a point and its neighbourhood are used for selecting the possible correspondences of point clouds. In addition, an explicit expression of the position uncertainty of measurement by laser scanners is presented in this dissertation and this position uncertainty is utilised to estimate the precision and accuracy of the estimated relative transformation parameters between point clouds. The GP-ICPR was tested with both simulated data and datasets from close range and terrestrial laser scanners in terms of its precision, accuracy, and convergence region. It was shown that the GP-ICPR improved the precision of the estimated relative transformation parameters as much as a factor of 5. / In addition, the rotational convergence region of the GP-ICPR on the order of 10°, which is much larger than the ICP or its variants, provides a window of opportunity to utilise this automated registration method in practical applications such as terrestrial surveying and deformation monitoring.

In many machine learning problems and application domains, the data are naturally organized by groups. For example, a video sequence is a group of images, an image is a group of patches, a document is a group of paragraphs/words, and a community is a group of people. We call them the collective data. In this thesis, we study how and what we can learn from collective data. Usually, machine learning focuses on individual objects, each of which is described by a feature vector and studied as a point in some metric space. When approaching collective data, researchers often reduce the groups into vectors to which traditional methods can be applied. We, on the other hand, will try to develop machine learning methods that respect the collective nature of data and learn from them directly. Several different approaches were taken to address this learning problem. When the groups consist of unordered discrete data points, it can naturally be characterized by its sufficient statistics – the histogram. For this case we develop efficient methods to address the outliers and temporal effects in the data based on matrix and tensor factorization methods. To learn from groups that contain multi-dimensional real-valued vectors, we develop both generative methods based on hierarchical probabilistic models and discriminative methods using group kernels based on new divergence estimators. With these tools, we can accomplish various tasks such as classification, regression, clustering, anomaly detection, and dimensionality reduction on collective data. We further consider the practical side of the divergence based algorithms. To reduce their time and space requirements, we evaluate and find methods that can effectively reduce the size of the groups with little impact on the accuracy. We also proposed the conditional divergence along with an efficient estimator in order to correct the sampling biases that might be present in the data. Finally, we develop methods to learn in cases where some divergences are missing, caused by either insufficient computational resources or extreme sampling biases. In addition to designing new learning methods, we will use them to help the scientific discovery process. In our collaboration with astronomers and physicists, we see that the new techniques can indeed help scientists make the best of data.

The Ellisras Basin, with huge coal resources, is fault-bounded along its southern and northern margins and is a graben-type deposit. The study area is situated in the south-western part of the Limpopo Province of the Republic of South Africa and is geologically located in the Ellisras Basin. In this area the basin is influenced by three major fault zones, the Eenzaamheid Fault delineating its southern limit, the Zoetfontein Fault near its northern limit and the Daarby Fault, with a down-throw of approximately 350 m towards the north-east. Sedimentological facies changes also influence the continuity of the coal zones, with deterioration in coal development. The exploration project was a collaboration between two of the large role players in the South African coal mining industry Sasol and Exxaro, for the purpose of identifying whether the coal in the Ellisras Basin could be used for gasification purposes in the Sasol process, and that enough resources exist on the farms on which the two companies have the exploration rights.. The prospecting method used at the Project area, situated 50 kilometer west of the town of Lephalale in the Limpopo Province of South Africa, comprises the drilling of cored exploration boreholes on a random spacing of ± 1 000 m x 1 000 m, together with infill percussion drilling. The use of slimline geophysical methods to log lithologies is a technique which has been used extensively in the mining industry over a number of years. At the Project area the correlation between the measured densities derived from the traditional method of air and water measurement and those derived from Vectar processed derived densities from geophysical logging is better than 95%. As a method of “fingerprinting” the various coal zones and samples it was decided to calculate the distribution of relative densities in the chosen geological intersection. The data was then used to portray geophysically derived relative density cumulative distribution line diagrams (GDCDD) of the various lithotypes on either a sample-by-sample or zone-by-zone basis. Using the classification method proposed, the various coal seams and zones can be correlated to a high degree and discrepancies easily identified. The lateral correlation between lithologies can be accurately described and substantiated, and this would convince a Competent Person that the method proposed is invaluable in classifying coal resources in the coal basins. / Thesis (PhD)--University of Pretoria, 2014. / lk2014 / Geology / PhD / Unrestricted

The project set out to construct a proof of concept for surface deformation in the Unity Engine using available assets and tools compatible with the Unity Engine, and via the proof of concept investigate which factors in a mesh deformation simulation that affects performance in terms of frames per second, memory usage and usability the most. This while looking into suitable data structures in the Unity Engine for handling expected data in a physics simulation of a surface deformation, such that of mining or scraping a cave wall. The project aims to answer these questions via testing and trail and error, performing tests while recording data which is plotted and discussed. To save time and start testing faster the usage of a premium assets called Digger Pro is used, allowing for quick set up of mesh manipulation inf both editor and play mode. Testing shows that one of the major factor for performance degradation is mesh resolu-tion, as it directly contributes to an increase in data points that needs to be kept track of and calculated. The Unity Engine and Digger PRO man-ages fairly well to stay above the targeted 30 frames per second limit even with medium level settings for meshes, all while maintaining acceptable memory usage levels. All this ties into the idea of an increased usage of digital twins in many different scenarios, and therefore the scientific community’s view on digital twins main challenges are summarized and discussed, hoping to shed further light on the current status of digital twin technology.

The thesis contains a proposal and implementation of an environment convenient for operation of network client applications which use common terminals. It also consists of implemented examples where the way of their usage is presented. The centralized way of communication is the basis. The approach is used also for the possibility of their joining with managing subsystems, i.e. unlimited ways of regulation of systems for real estate management, access to devices, user authority access to access data points to the devices. The environment is based on operation system Linux and database MySQL. Their realization is supposed on a server, in the network environment. This relates also to the overall security policy and this work also focused on social treatment of clients possibilities.

This thesis is about visualizing a kind of data that is trivial to process by computers but difficult to imagine by humans because nature does not allow for intuition with this type of information: high-dimensional data. Such data often result from representing observations of objects under various aspects or with different properties. In many applications, a typical, laborious task is to find related objects or to group those that are similar to each other. One classic solution for this task is to imagine the data as vectors in a Euclidean space with object variables as dimensions. Utilizing Euclidean distance as a measure of similarity, objects with similar properties and values accumulate to groups, so-called clusters, that are exposed by cluster analysis on the high-dimensional point cloud. Because similar vectors can be thought of as objects that are alike in terms of their attributes, the point cloud\''s structure and individual cluster properties, like their size or compactness, summarize data categories and their relative importance. The contribution of this thesis is a novel analysis approach for visual exploration of high-dimensional point clouds without suffering from structural occlusion. The work is based on implementing two key concepts: The first idea is to discard those geometric properties that cannot be preserved and, thus, lead to the typical artifacts. Topological concepts are used instead to shift away the focus from a point-centered view on the data to a more structure-centered perspective. The advantage is that topology-driven clustering information can be extracted in the data\''s original domain and be preserved without loss in low dimensions. The second idea is to split the analysis into a topology-based global overview and a subsequent geometric local refinement. The occlusion-free overview enables the analyst to identify features and to link them to other visualizations that permit analysis of those properties not captured by the topological abstraction, e.g. cluster shape or value distributions in particular dimensions or subspaces. The advantage of separating structure from data point analysis is that restricting local analysis only to data subsets significantly reduces artifacts and the visual complexity of standard techniques. That is, the additional topological layer enables the analyst to identify structure that was hidden before and to focus on particular features by suppressing irrelevant points during local feature analysis. This thesis addresses the topology-based visual analysis of high-dimensional point clouds for both the time-invariant and the time-varying case. Time-invariant means that the points do not change in their number or positions. That is, the analyst explores the clustering of a fixed and constant set of points. The extension to the time-varying case implies the analysis of a varying clustering, where clusters appear as new, merge or split, or vanish. Especially for high-dimensional data, both tracking---which means to relate features over time---but also visualizing changing structure are difficult problems to solve.

[pt] O aprendizado supervisionado é, hoje, o ramo do aprendizado de máquina central para a maioria das inovações nos negócios. A abordagem depende de ter grandes quantidades de dados rotulados, suficiente para ajustar funções com a precisão necessária. No entanto, pode ser caro obter dados rotulados ou criar os rótulos através de um processo de anotação. O aprendizado semisupervisionado (SSL) é usado para rotular com precisão os dados a partir de pequenas quantidades de dados rotulados utilizando técnicas de aprendizado não supervisionado. Uma técnica de rotulagem é a propagação de rótulos. Neste trabalho, usamos especificamente o algoritmo Consensus rate-based label propagation (CRLP). Este algoritmo depende do uma função de consenso para a propagação. Uma possível função de consenso é a matriz de co-associação que estima a probabilidade dos pontos i e j pertencem ao mesmo grupo. Neste trabalho, observamos que a matriz de co-associação contém informações valiosas para tratar esse tipo de problema. Quando nenhum dado está rotulado, é comum escolher aleatoriamente, com probabilidade uniforme, os dados a serem rotulados manualmente, a partir dos quais a propagação procede. Este trabalho aborda o problema de seleção de um conjunto de tamanho fixo de dados para serem rotulados manualmente que propiciem uma melhor precisão no algoritmo de propagação de rótulos. Três técnicas de seleção, baseadas em princípios de amostragem estocástica, são propostas: Stratified Sampling (SS), Probability (P), and Stratified Sampling - Probability (SSP). Eles são todos baseados nas informações embutidas na matriz de co-associação. Os experimentos foram realizados em 15 conjuntos de benchmarks e mostraram resultados muito interessantes. Não só, porque eles fornecem uma seleção mais equilibrada quando comparados a uma seleção aleatória, mas também melhoram os resultados de precisão na propagação de rótulos. Em outro contexto, essas estratégias também foram testadas dentro de um processo de aprendizagem ativa, obtendo também bons resultados. / [en] Supervised learning is, today, the branch of Machine Learning central to most business disruption. The approach relies on having amounts of labeled data large enough to learn functions with the required approximation. However, labeled data may be expensive, to obtain or to construct through a labeling process. Semi-supervised learning (SSL) strives to label accurately data from small amounts of labeled data and the use of unsupervised learning techniques. One labeling technique is label propagation. We use specifically the Consensus rate-based label propagation (CRLP) in this work. A consensus function is central to the propagation. A possible consensus function is a coassociation matrix that estimates the probability of data points i and j belong to the same group. In this work, we observe that the co-association matrix has valuable information embedded in it. When no data is labeled, it is common to choose with a uniform probability randomly, the data to manually label, from which the propagation proceeds. This work addresses the problem of selecting a fixed-size set of data points to label (manually), to improve the label propagation algorithm s accuracy. Three selection techniques, based on stochastic sampling principles, are proposed: Stratified Sampling (SP), Probability (P), and Stratified Sampling - Probability (SSP). They are all based on the information embedded in the co-association matrix. Experiments were carried out on 15 benchmark sets and showed exciting results. Not only because they provide a more balanced selection when compared to a uniform random selection, but also improved the accuracy results of a label propagation method. These strategies were also tested inside an active learning process in a different context, also achieving good results.