Searched for: subject:"machine%5C+learning"
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Brouwer, Wietske (author), van Hamel, Amber (author), van Haren, Mick (author), Kindermann, Paulina (author), Verboeket, Rik (author)
The Kathmandu Valley in Nepal is facing the combined effects of population growth, rapid urbanization, economic development, and climate change. This results in serious water management challenges: growing freshwater demands, declining water tables, drying of streams, and deteriorating water quality. Insufficient surface water supplies have led...
student report 2020
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Gómez Pérez, Pablo (author)
In this thesis, a new method to approximate the cost function of Low-Thrust, Multiple-Gravity-Assist interplanetary trajectories using a Machine Learning surrogate is proposed. This method speeds up the optimization process without fine tuning of the surrogate parameters for every individual case. The computational cost of obtaining training...
master thesis 2020
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Bekker, Bernard (author)
Large cities in the Netherlands, like Rotterdam, have hundreds of playgrounds, but local governments have little information on how children and adults use them. Usage data can help create playgrounds that better fit with the residents' needs by identifying what elements of a playground are the most popular, and which do not get used. The AMS...
master thesis 2020
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Joosse, Corniël (author)
Absence seizures have a real-life impact on epileptic subjects, as day-to-day tasks can by suddenly interrupted making for dangerous situations. Though a lot of work has been done on seizure detection, to limit the impact on epileptic patients, the true necessity lies in timely prediction of seizures before they manifest. Various attempts have...
master thesis 2020
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Ruijs, Ewout (author)
The increased implementation of digitalisation all over the world has led to an exponential growth of available data across various industries. Consequently, there is a large growth of Machine Learning (ML) techniques being applied to process data. Predictive maintenance is a digital strategy using condition-based monitoring techniques to track...
master thesis 2020
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Zabłocki, Bartosz (author)
When driving a car, people can usually predict the intention of other road users with high confidence. They can spot small variations in a recent trajectory, take into account road infrastructure, traffic rules and other factors, which influence a future trajectory. As a result, they can interact with other drivers smoothly and safely. Even...
master thesis 2020
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Jhamb, Shubham (author)
The scientific community is consistently focused on identifying new sources of energy, which can reduce the consequences of climate change and depleting natural resources. Organic Rankine Cycle (ORC) based power systems have been touted as one of the promising technologies of extracting thermal energy from waste-heat and renewable sources such...
master thesis 2020
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Harms, Tim (author)
Safety occurrences in the aviation industry are nowadays commonly regarded as the outcome of a complex system. Due to this systemic view on safety airlines pursue to understand this complex, underlying system and aim to proactively act upon the occurrence of these events. The most prevalent implementation of flight safety event detection is...
master thesis 2020
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Khaleghi, Aaron (author)
The consumer lending domain has increasingly leveraged Artificial Intelligence (AI) to make loan approval processes more efficient and to make use of larger amount of information to predict their applicants’ repayment ability. Over time, however, valid concerns have been raised about whether decisions made about individuals using these data...
master thesis 2020
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Jooste, Nicael (author)
In this study, an unsupervised classification approach is used to investigate and characterize the spatial and temporal variability of MetOp-A ASCAT backscatter (σ◦) and the TUW SMR vegetation parameters across mainland France between 2007 and 2017. Currently, soil moisture data is retrieved from ASCAT backscatter measurements using the TU Wien...
master thesis 2020
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van den Munckhof, Gijs (author)
The objective of this study is find out whether maximum daily discharge of the Geul and Rur catchments can be forecast using machine learning (ML) methods, and if so, to what extent. In addition, these ML models are compared to a conceptual model to see which performs better. A second objective is to test whether soil moisture content (SMC) and...
master thesis 2020
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Kharagjitsing, Manish (author)
The TU-Wien developed a soil moisture retrieval algorithm that uses the incidence angle dependence of backscatter to obtain soil moisture estimates (Wagner et al., 1999). The core of this algorithm is a second order Taylor expansion with which the backscatter is normalized at a reference angle. Studies have shown that the first and second order...
master thesis 2020
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Tollenaar, Veronica (author)
Meteorites contain information on the formation and evolution of the Solar System. Antarctica is the most productive region for collecting meteorites, as the visually contrasting meteorites are easily detectable and tend to concentrate at specific areas exposing blue ice. Blue ice areas act as meteorite stranding surfaces (MSSs) if the flow of...
master thesis 2020
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Ament, Tjalling (author)
To optimize the exploitation of oil and gas reservoirs both on- and offshore, Biodentfiy has developed a method to predict prospectivity of hydrocarbons before drilling. This method uses microbiological DNA analysis of shallow soil or seabed samples to detect vertical upward microseepage from hydrocarbon accumulations, which change the...
master thesis 2020
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Michielsen, Lieke (author)
Since the revolution of single-cell RNA-sequencing, the number of available datasets has increased enormously. In these datasets, cell identification is mainly done manually, which is subjective and time-consuming. As a consequence, most datasets are annotated at a different resolution. This is not surprising as cell types form a hierarchy, but...
master thesis 2020
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Konatala, Ramesh (author)
Online Adaptive Flight Control is interesting in the context of growing complexity of aircraft systems and their adaptability requirements to ensure safety. An Incremental Approximate Dynamic Programming (iADP) controller combines reinforcement learning methods, optimal control and Online identified incremental model to achieve optimal adaptive...
master thesis 2020
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Park, MyeongJung (author)
Since building a machine learning model costs a lot while following 9 stages, the automated machine learning model creation became a crucial role in a large-scale context. At the same time, a monitoring system became an essential factor for machine learning models. This thesis presents the monitoring system for machine learning models at ING in...
master thesis 2020
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Demetriades, Valerie (author)
Waterschap Brabantse Delta (WBD) has the intention to implement measures that enhance the baseflow. Baseflow consists of the groundwater flow and a small part of the interflow. During dry periods, streams are dependent on the baseflow. Enhancing the baseflow has a proper effect on the ecologically relevant quality of waters, and is therefore...
master thesis 2020
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Scavuzzo Montana, Lara (author)
Mixed Integer Linear Programming (MILP) is a generalization of classical linear programming where we restrict some (or all) variables to take integer values. Numerous real-world problems can be modeled as MILPs, such as production planning, scheduling, network design optimization and many more. MILPs are, in fact, NP-hard. State-of-the-art...
master thesis 2020
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Schönfeld, Mariette (author)
Machine learning has been a computer sciences buzzword for years. The technology has a lot of potential and a huge number of applications that spoke to people with and without knowledge of computer sciences. Image, text and speech recognition, social profiling, computergames, everything seemed possible. Machine learning is not as much in the...
bachelor thesis 2020
Searched for: subject:"machine%5C+learning"
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