In this thesis, a new trading strategy is proposed. By the help of quantile regression, the conditional distribution functions of stock market returns are estimated. Based on the knowledge of the distribution the strategy produced buying and selling signals which together with a weight function derived from exponential moving averages determines how much and when to buy or sell. The strategy performs better than the market in terms of absolute return and the Sharpe ratio in-sample, but it does not provide satisfactory results out-of-sample.

O planejamento de curto prazo em minas a céu aberto exige a definição de poligonais, que representam os sucessivos avanços de lavra. As poligonais, tradicionalmente, são desenhadas em um processo laborioso na tentativa de delinear como minério em qualidade e quantidade de acordo com os limites determinados. O minério delimitado deve apresentar a menor variabilidade em qualidade possível, com o objetivo de maximizar a recuperação na usina de processamento. Essa dissertação visa desenvolver um fluxo do trabalho para definir poligonais de curto prazo de forma automática, além disso, sequenciar todos os blocos de minério de cada polígono de modo a definir uma sequência interconectada lavrável de poligonais. O fluxo do trabalho foi aplicada à incerteza de teores, obtida através de simulações estocásticas. Algoritmos genéticos foram desenvolvidos em linguagem de programação Python e implementados na forma de plug-in no software geoestatístico Ar2GeMS. Múltiplas iterações são criadas para cada avanço individual, gerando regiões (ou poligonais). Então, a região que apresenta menor variabilidade de teores é selecionada. A distribuição de probabilidade dos teores dos blocos em cada avanço é comparada com a distribuição global de teores, calculada a partir de todos os blocos do corpo de minério. Os resultados mostraram que os teores dos blocos abrangidos pelas poligonais criadas dessa forma apresentam teores similares à distribuição de referência, permitindo o sequenciamento de lavra com distribuição de teores mais próximo possível da distribuição global. Modelos equiprováveis permitem avaliar a incerteza associada à solução proposta. / Open-pit short-term planning requieres the definition of polygons identifying the successive mining advances. These polygons are drawn in a labour intensive task attempting to delineate ore with the quantity and quality within established ranges. The ore delineated by the polygons should have the least possible quality variability among them, helping in maximizing ore recovery at the processing plant. This thesis aims at developíng a workflow for drawing short-term polygons automatically, sequencing all ore blocks within each polygon and leading to a mineable and connected sequence of polygons. This workflow is also tested under grade uncertainty obtained through multiple syochastic simulated models. For this, genetics algorithms were developed in Python programming language and pluged in Ar2GeMS geostatistical software. Multiple iterations were generated for each of the individual advances, generating regions or polygons, and selecting the regions of lower grade variability. The blocks probability distribution within each advance were compared to the global distribution, including all blocks within the ore body. Results show that the polygons generated are comprised by block grades similar to the ones from the reference distribution, leading to mining sequence as close as possible to the global maintaining a quasi-satationarity. Equally probable models provide the means to access the uncertainy in the solution provided.

O planejamento de curto prazo em minas a céu aberto exige a definição de poligonais, que representam os sucessivos avanços de lavra. As poligonais, tradicionalmente, são desenhadas em um processo laborioso na tentativa de delinear como minério em qualidade e quantidade de acordo com os limites determinados. O minério delimitado deve apresentar a menor variabilidade em qualidade possível, com o objetivo de maximizar a recuperação na usina de processamento. Essa dissertação visa desenvolver um fluxo do trabalho para definir poligonais de curto prazo de forma automática, além disso, sequenciar todos os blocos de minério de cada polígono de modo a definir uma sequência interconectada lavrável de poligonais. O fluxo do trabalho foi aplicada à incerteza de teores, obtida através de simulações estocásticas. Algoritmos genéticos foram desenvolvidos em linguagem de programação Python e implementados na forma de plug-in no software geoestatístico Ar2GeMS. Múltiplas iterações são criadas para cada avanço individual, gerando regiões (ou poligonais). Então, a região que apresenta menor variabilidade de teores é selecionada. A distribuição de probabilidade dos teores dos blocos em cada avanço é comparada com a distribuição global de teores, calculada a partir de todos os blocos do corpo de minério. Os resultados mostraram que os teores dos blocos abrangidos pelas poligonais criadas dessa forma apresentam teores similares à distribuição de referência, permitindo o sequenciamento de lavra com distribuição de teores mais próximo possível da distribuição global. Modelos equiprováveis permitem avaliar a incerteza associada à solução proposta. / Open-pit short-term planning requieres the definition of polygons identifying the successive mining advances. These polygons are drawn in a labour intensive task attempting to delineate ore with the quantity and quality within established ranges. The ore delineated by the polygons should have the least possible quality variability among them, helping in maximizing ore recovery at the processing plant. This thesis aims at developíng a workflow for drawing short-term polygons automatically, sequencing all ore blocks within each polygon and leading to a mineable and connected sequence of polygons. This workflow is also tested under grade uncertainty obtained through multiple syochastic simulated models. For this, genetics algorithms were developed in Python programming language and pluged in Ar2GeMS geostatistical software. Multiple iterations were generated for each of the individual advances, generating regions or polygons, and selecting the regions of lower grade variability. The blocks probability distribution within each advance were compared to the global distribution, including all blocks within the ore body. Results show that the polygons generated are comprised by block grades similar to the ones from the reference distribution, leading to mining sequence as close as possible to the global maintaining a quasi-satationarity. Equally probable models provide the means to access the uncertainy in the solution provided.

O planejamento de curto prazo em minas a céu aberto exige a definição de poligonais, que representam os sucessivos avanços de lavra. As poligonais, tradicionalmente, são desenhadas em um processo laborioso na tentativa de delinear como minério em qualidade e quantidade de acordo com os limites determinados. O minério delimitado deve apresentar a menor variabilidade em qualidade possível, com o objetivo de maximizar a recuperação na usina de processamento. Essa dissertação visa desenvolver um fluxo do trabalho para definir poligonais de curto prazo de forma automática, além disso, sequenciar todos os blocos de minério de cada polígono de modo a definir uma sequência interconectada lavrável de poligonais. O fluxo do trabalho foi aplicada à incerteza de teores, obtida através de simulações estocásticas. Algoritmos genéticos foram desenvolvidos em linguagem de programação Python e implementados na forma de plug-in no software geoestatístico Ar2GeMS. Múltiplas iterações são criadas para cada avanço individual, gerando regiões (ou poligonais). Então, a região que apresenta menor variabilidade de teores é selecionada. A distribuição de probabilidade dos teores dos blocos em cada avanço é comparada com a distribuição global de teores, calculada a partir de todos os blocos do corpo de minério. Os resultados mostraram que os teores dos blocos abrangidos pelas poligonais criadas dessa forma apresentam teores similares à distribuição de referência, permitindo o sequenciamento de lavra com distribuição de teores mais próximo possível da distribuição global. Modelos equiprováveis permitem avaliar a incerteza associada à solução proposta. / Open-pit short-term planning requieres the definition of polygons identifying the successive mining advances. These polygons are drawn in a labour intensive task attempting to delineate ore with the quantity and quality within established ranges. The ore delineated by the polygons should have the least possible quality variability among them, helping in maximizing ore recovery at the processing plant. This thesis aims at developíng a workflow for drawing short-term polygons automatically, sequencing all ore blocks within each polygon and leading to a mineable and connected sequence of polygons. This workflow is also tested under grade uncertainty obtained through multiple syochastic simulated models. For this, genetics algorithms were developed in Python programming language and pluged in Ar2GeMS geostatistical software. Multiple iterations were generated for each of the individual advances, generating regions or polygons, and selecting the regions of lower grade variability. The blocks probability distribution within each advance were compared to the global distribution, including all blocks within the ore body. Results show that the polygons generated are comprised by block grades similar to the ones from the reference distribution, leading to mining sequence as close as possible to the global maintaining a quasi-satationarity. Equally probable models provide the means to access the uncertainy in the solution provided.

This thesis deals with the topic of high resolution spectral analysis. In the first part, selected methods are presented and afterwards compared based on the Matlab implementations. The problematics of reduction of crossterms in quadratic time–frequency distributions is also covered. The second part is focused on the implementation and optimization of the algorithm for real-time computation of smoothed Wigner distribution function.

This paper presents a method to reduce time complexity of the computation of higher–order tensor lines. The method can be applied to higher–order tensors and the spherical harmonics representation, both widely used in medical imaging. It is based on a gradient descend technique and integrates well into fiber tracking algorithms. Furthermore, the method improves the angular resolution in contrast to discrete sampling methods which is especially important to tractography, since there, small errors accumulate fast and make the result unusable. Our implementation does not interpolate derived directions but works directly on the interpolated tensor information. The specific contribution of this paper is a fast algorithm for tracking lines tensor fields of arbitrary order that increases angular resolution compared to previous approaches.

No description available.

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第23287号 / 人博第1002号 / 新制||人||236(附属図書館) / 2020||人博||1002(吉田南総合図書館) / 京都大学大学院人間・環境学研究科相関環境学専攻 / (主査)教授 内本 喜晴, 教授 田部 勢津久, 准教授 戸﨑 充男 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

For acquisition of narrow-beam free-space optical (FSO) terminals, a Global Positioning System (GPS) is typically required for coarse localization of the terminal. However, the GPS signal may be shadowed, or may not be present at all, especially in rough or unnameable terrains. In this study, we propose a lidar-assisted acquisition of an unmanned aerial vehicle (UAV) for FSO communications in a poor GPS environment. Such an acquisition system consists of a lidar subsystem and an FSO acquisition subsystem: The lidar system is used for coarse acquisition of the UAV, whereas, the FSO system is utilized for fine acquisition to obtain the UAV’s accurate position. This study investigates the optimal allocation of energy between the lidar and FSO systems to minimize the acquisition time. Here, we minimize the average acquisition time, and maximize the cumulative distribution function of acquisition time for a fixed threshold. We learn that an optimal value of the energy allocation factor exists that provides the best performance of the proposed system.

The local structure, atomic and magnetic structure correlated over a small length scale, of a material has a strong impact on material properties. Pair distribution function (PDF) analysis is a strong tool to investigate local structure and magnetism of this nature. This work outlines extensions to current one dimensional magnetic pair distribution functions (1D-mPDF) and the fitting of structures with symmetry breaking local atomic distortions. 1DmPDF analysis has been used to study local magnetic structure, but requires a rotational averaging of the correlations so directional information is lost, as in powder diffraction experiments. Three dimensional difference magnetic pair distribution function (3D-∆mPDF) analysis does not require this rotational averaging and preserves directional information. This is a useful tool in analyzing experimental data like single crystal neutron diffraction and studying locally anisoptropic magnetic structures. Here we present a technique and software tools to calculate the 3D-∆mPDF pattern of a given structure and give a brief analysis of the local magnetic structure of MnTe. Another problem in PDF analysis is the modeling of structures with symmetry breaking local atomic distortions. Symmetry-adapted distortion modes have been used for structural refinement in Rietveld refinement for at least 10 years; more more recently, this has also been applied to PDF data. We present here a detailed discussion of the use of symmetry-adapted modes for structural refinement using PDF data. We also outline new open-sourced software tools to apply this technique and show two analyses using symmetry-adapted structural modes.