1,919 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Method for prediction and control by uncertain microsatellite magnetic cleanliness based on calculation and compensation magnetic field spatial harmonics

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    Aim. Development of method for prediction and control the microsatellite magnetic cleanliness taking into account the uncertainties of the magnetic characteristics of the microsatellite, based on calculation the magnetic field spatial spherical harmonics in the area of the onboard magnetometer installation and using compensating multipoles. Methodology. Spatial spherical harmonics of microsatellite magnetic field in the area of the onboard magnetometer installation calculated as solution of nonlinear minimax optimization problem based on near field measurements for prediction far spacecraft magnetic field magnitude. Nonlinear objective function calculated as the weighted sum of squared residuals between the measured and predicted magnetic field. Values of the compensating dipoles, quadrupoles and octupoles and coordinates of them placement inside the spaceship for compensation of the dipoles, quadrupoles and octupoles components of the microsatellite initial magnetic field also calculated as solution of nonlinear minimax optimization problem. Both solutions of this nonlinear minimax optimization problems calculated based on particle swarm nonlinear optimization algorithms. Results. Results of prediction spacecraft far magnetic field magnitude based on spacecraft spatial spherical harmonics of the magnetic field using near field measurements and compensation of the dipoles, quadrupoles and octupoles components of the initial magnetic field with consideration of spacecraft magnetic characteristics uncertainty for ensuring the microsatellite magnetic cleanliness. Originality. The method for prediction and control by spacecraft magnetic cleanliness based on calculation spatial spherical harmonics of the magnetic field in the area of the onboard magnetometer installation using compensation of the dipoles, quadrupoles and octupoles components of the initial magnetic field with consideration of magnetic characteristics uncertainty is developed. Practical value. The important practical problem of ensuring the magnetic cleanliness of the «Sich-2» microsatellite family based on the spatial spherical harmonics of the magnetic field model using the compensation of the dipole, quadrupole and octupole components of the output magnetic field of the sensor for the kinetic parameters of the neutral component of the space plasma at the point of installation of the on-board magnetometer LEMI-016 by setting the compensating dipole, quadrupole and octupole with consideration of spacecraft magnetic characteristics uncertainty solved.Мета. Розробка методу прогнозування та управління магнітною чистотою мікросупутника на основі обчислення просторових сферичних гармонік магнітного поля в зоні встановлення бортового магнітометру з використанням компенсації сферичних гармонік вихідного магнітного поля та з урахуванням невизначеності магнітних характеристик. Методологія. Просторові сферичні гармоніки магнітного поля мікросупутника розраховані як рішення задачі нелінійної мінімаксної оптимізації на основі вимірювань ближнього магнітного поля для прогнозування величини дальнього магнітного поля. Нелінійна цільова функція обчислена в вигляді зваженої суми квадратів залишків між виміряним і прогнозованим магнітним полем. Величини компенсуючих диполів, квадруполів та октуполів та координати їх розташування в просторі мікросупутника для компенсації вихідного магнітного поля космічного апарату розраховані як рішення нелінійної задачі мінімаксної оптимізації. Рішення обох задач нелінійної мінімаксної оптимізації розраховані на основі алгоритмів нелінійної оптимізації роєм частинок. Результати. Результати прогнозування величини дальнього магнітного поля мікросупутника на основі обчислення просторових сферичних гармонік моделі магнітного поля в зоні встановлення бортового магнітометру з використанням вимірювань ближнього поля та компенсації дипольних, квадрупольних та октупольних компонент вихідного магнітного поля з урахуванням невизначеності магнітних характеристик для забезпечення магнітної чистоти мікросупутника. Оригінальність. Розроблено метод прогнозування та управління магнітною чистотою мікросупутника на основі обчислення просторових сферичних гармонік магнітного поля з використанням компенсації дипольних, квадрупольних та октупольних компонент вихідного магнітного поля та з урахуванням невизначеності магнітних характеристик. Практична цінність. Вирішено важливу практичну задачу забезпечення магнітної чистоти орбітального космічного апарату сімейства «Січ-2» на основі обчислення просторових сферичних гармонік моделі магнітного поля з використанням компенсації дипольних, квадрупольних та октупольних компонент вихідного магнітного поля датчика кінетичних параметрів нейтрального компонента космічної плазми в точці розташування бортового магнітометру LEMI-016 шляхом установки компенсуючих диполів, квадруполів та октуполів та з урахуванням невизначеності магнітних характеристик

    Undergraduate Catalog of Studies, 2023-2024

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    Data-assisted modeling of complex chemical and biological systems

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    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    Undergraduate Catalog of Studies, 2022-2023

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    Using hydrological models and digital soil mapping for the assessment and management of catchments: A case study of the Nyangores and Ruiru catchments in Kenya (East Africa)

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    Human activities on land have a direct and cumulative impact on water and other natural resources within a catchment. This land-use change can have hydrological consequences on the local and regional scales. Sound catchment assessment is not only critical to understanding processes and functions but also important in identifying priority management areas. The overarching goal of this doctoral thesis was to design a methodological framework for catchment assessment (dependent upon data availability) and propose practical catchment management strategies for sustainable water resources management. The Nyangores and Ruiru reservoir catchments located in Kenya, East Africa were used as case studies. A properly calibrated Soil and Water Assessment Tool (SWAT) hydrologic model coupled with a generic land-use optimization tool (Constrained Multi-Objective Optimization of Land-use Allocation-CoMOLA) was applied to identify and quantify functional trade-offs between environmental sustainability and food production in the ‘data-available’ Nyangores catchment. This was determined using a four-dimension objective function defined as (i) minimizing sediment load, (ii) maximizing stream low flow and (iii and iv) maximizing the crop yields of maize and soybeans, respectively. Additionally, three different optimization scenarios, represented as i.) agroforestry (Scenario 1), ii.) agroforestry + conservation agriculture (Scenario 2) and iii.) conservation agriculture (Scenario 3), were compared. For the data-scarce Ruiru reservoir catchment, alternative methods using digital soil mapping of soil erosion proxies (aggregate stability using Mean Weight Diameter) and spatial-temporal soil loss analysis using empirical models (the Revised Universal Soil Loss Equation-RUSLE) were used. The lack of adequate data necessitated a data-collection phase which implemented the conditional Latin Hypercube Sampling. This sampling technique reduced the need for intensive soil sampling while still capturing spatial variability. The results revealed that for the Nyangores catchment, adoption of both agroforestry and conservation agriculture (Scenario 2) led to the smallest trade-off amongst the different objectives i.e. a 3.6% change in forests combined with 35% change in conservation agriculture resulted in the largest reduction in sediment loads (78%), increased low flow (+14%) and only slightly decreased crop yields (3.8% for both maize and soybeans). Therefore, the advanced use of hydrologic models with optimization tools allows for the simultaneous assessment of different outputs/objectives and is ideal for areas with adequate data to properly calibrate the model. For the Ruiru reservoir catchment, digital soil mapping (DSM) of aggregate stability revealed that susceptibility to erosion exists for cropland (food crops), tea and roadsides, which are mainly located in the eastern part of the catchment, as well as deforested areas on the western side. This validated that with limited soil samples and the use of computing power, machine learning and freely available covariates, DSM can effectively be applied in data-scarce areas. Moreover, uncertainty in the predictions can be incorporated using prediction intervals. The spatial-temporal analysis exhibited that bare land (which has the lowest areal proportion) was the largest contributor to erosion. Two peak soil loss periods corresponding to the two rainy periods of March–May and October–December were identified. Thus, yearly soil erosion risk maps misrepresent the true dimensions of soil loss with averages disguising areas of low and high potential. Also, a small portion of the catchment can be responsible for a large proportion of the total erosion. For both catchments, agroforestry (combining both the use of trees and conservation farming) is the most feasible catchment management strategy (CMS) for solving the major water quantity and quality problems. Finally, the key to thriving catchments aiming at both sustainability and resilience requires urgent collaborative action by all stakeholders. The necessary stakeholders in both Nyangores and Ruiru reservoir catchments must be involved in catchment assessment in order to identify the catchment problems, mitigation strategies/roles and responsibilities while keeping in mind that some risks need to be shared and negotiated, but so will the benefits.:TABLE OF CONTENTS DECLARATION OF CONFORMITY........................................................................ i DECLARATION OF INDEPENDENT WORK AND CONSENT ............................. ii LIST OF PAPERS ................................................................................................. iii ACKNOWLEDGEMENTS ..................................................................................... iv THESIS AT A GLANCE ......................................................................................... v SUMMARY ............................................................................................................ vi List of Figures......................................................................................................... x List of Tables........................................................................................................... x ABBREVIATION..................................................................................................... xi PART A: SYNTHESIS 1. INTRODUCTION ............................................................................................... 1 1.1 Catchment management ...................................................................................1 1.2 Tools to support catchment assessment and management ..............................4 1.3 Catchment management strategies (CMSs)......................................................9 1.4 Concept and research objectives.......................................................................11 2. MATERIAL AND METHODS................................................................................15 2.1. STUDY AREA ..................................................................................................15 2.1.1. Nyangores catchment ...................................................................................15 2.1.2. Ruiru reservoir catchment .............................................................................17 2.2. Using SWAT conceptual model and land-use optimization ..............................19 2.3. Using soil erosion proxies and empirical models ..............................................21 3. RESULTS AND DISCUSSION..............................................................................24 3.1. Assessing multi-metric calibration performance using the SWAT model...........25 3.2. Land-use optimization using SWAT-CoMOLA for the Nyangores catchment. ..26 3.3. Digital soil mapping of soil aggregate stability ..................................................28 3.4. Spatio-temporal analysis using the revised universal soil loss equation (RUSLE) 29 4. CRITICAL ASSESSMENT OF THE METHODS USED ......................................31 4.1. Assessing suitability of data for modelling and overcoming data challenges...31 4.2. Selecting catchment management strategies based on catchment assessment . 35 5. CONCLUSION AND RECOMMENDATIONS ....................................................36 6. REFERENCES ............................ .....................................................................38 PART B: PAPERS PAPER I .................................................................................................................47 PAPER II ................................................................................................................59 PAPER III ...............................................................................................................74 PAPER IV ...............................................................................................................8

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    An Intelligent Time and Performance Efficient Algorithm for Aircraft Design Optimization

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    Die Optimierung des Flugzeugentwurfs erfordert die Beherrschung der komplexen Zusammenhänge mehrerer Disziplinen. Trotz seiner Abhängigkeit von einer Vielzahl unabhängiger Variablen zeichnet sich dieses komplexe Entwurfsproblem durch starke indirekte Verbindungen und eine daraus resultierende geringe Anzahl lokaler Minima aus. Kürzlich entwickelte intelligente Methoden, die auf selbstlernenden Algorithmen basieren, ermutigten die Suche nach einer diesem Bereich zugeordneten neuen Methode. Tatsächlich wird der in dieser Arbeit entwickelte Hybrid-Algorithmus (Cavus) auf zwei Hauptdesignfälle im Luft- und Raumfahrtbereich angewendet: Flugzeugentwurf- und Flugbahnoptimierung. Der implementierte neue Ansatz ist in der Lage, die Anzahl der Versuchspunkte ohne große Kompromisse zu reduzieren. Die Trendanalyse zeigt, dass der Cavus-Algorithmus für die komplexen Designprobleme, mit einer proportionalen Anzahl von Prüfpunkten konservativer ist, um die erfolgreichen Muster zu finden. Aircraft Design Optimization requires mastering of the complex interrelationships of multiple disciplines. Despite its dependency on a diverse number of independent variables, this complex design problem has favourable nature as having strong indirect links and as a result a low number of local minimums. Recently developed intelligent methods that are based on self-learning algorithms encouraged finding a new method dedicated to this area. Indeed, the hybrid (Cavus) algorithm developed in this thesis is applied two main design cases in aerospace area: aircraft design optimization and trajectory optimization. The implemented new approach is capable of reducing the number of trial points without much compromise. The trend analysis shows that, for the complex design problems the Cavus algorithm is more conservative with a proportional number of trial points in finding the successful patterns
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