Searched for: contributor%3A%22Mohajerin+Esfahani%2C+P.+%28mentor%29%22
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SRIKRISHNA, ANAND (author)
Fuel consumption reduction in Hybrid Electric Vehicles (HEV) powertrains has been an important area of research over the past few decades. HEV powertrains have two energy sources : fuel and battery. The important task of splitting the energy/power demand between both these sources is performed by the Energy Management Systems (EMS). There are...
master thesis 2023
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Agarwal, Ritik (author)
HIsarna is a revolutionary step towards production of green steel. Due to the complex nature of the process the control of various facets of HIsarna is difficult. One of these facets is the slag-composition control, or more specifically slag-basicity. In this thesis an optimal sequential decision making strategy has been developed using the...
master thesis 2023
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Liao, Yucheng (author)
The Monitoring technique plays a vital role in ensuring the proper functioning of modern industrial systems that are highly sophisticated and automated. On two different applications, this thesis investigates two major categories of information redundancy monitoring techniques, model-based and data-driven.<br/><br/>The first application focuses...
master thesis 2023
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Mur Uribe, Pol (author)
This thesis introduces a new method, called Mixed Iteration, for controlling Markov Decision Processes when partial information is known about the dynamics of the Markov Decision Process. The algorithm uses sampling to calculate the expectation of partially known dynamics in stochastic environments. Its goal is to lower the number of iterations...
master thesis 2023
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Hassan, Pepijn (author)
This research explores the feasibility of building a large-scale setpoint tracking controller for the co-regulation of Electric Vehicle (EV) charging stations, aiming to coordinate charging with energy market dynamics and minimize the error between a power setpoint and the aggregated consumption of charging stations while capitalizing on...
master thesis 2023
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Feng, Jingru (author)
With the demand for more information on the building’s indoor climate, a massive amount of multi-sensors are mounted in buildings. Sensor anomalies in smart buildings lead to higher energy consumption and a less comfortable indoor climate. In the state of practice, rule-based approaches were proposed to detect and diagnose sensor anomalies in...
master thesis 2022
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Redel, Floor (author)
Ill-posed Linear Inverse Problems arise in various research domains, such as control engineering and image processing. Having a fast algorithm is a great benefit when working with high-dimensional signals, such as images. However, fast convergence and iterations with low computational complexity are challenging.<br/>In this master thesis report,...
master thesis 2022
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van Beek, Piet (author)
Optimizing delivery routes is a well-researched topic, however, most of the classical approaches do not incorporate preferences of drivers, as those approaches focus on minimizing the time or distance of the routes. As a result, the actual driven route of an experienced driver often deviates from the proposed route since the drivers have tacit...
master thesis 2022
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Swens, Bart (author)
Electricity grids worldwide are experiencing increased peak demands and decreasing simultaneity due to higher shares of Renewable Energy Sources (RES). It is expected that many grids will soon reach their limits. One solution to mitigate these issues is exploiting flexibility in e.g. electric vehicles. In this work, a shrinking horizon model...
master thesis 2022
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Dimanidis, Ioannis (author)
We propose a novel method combining elements of supervised- and Q-learning for the control of dynamical systems subject to unknown disturbances. By using the Inverse Optimization framework and in-hindsight information we can derive a causal parametric optimization policy that approximates a non-causal MPC expert. Furthermore, we propose a new...
master thesis 2021
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Rodopoulos, Charalampos (author)
In decision making problems, the ability to compute the optimal solution can pose a serious challenge. Dynamic Programming (DP) aims to provide a framework to deal with a category of such problems, namely ones that involve sequential decision making. By dividing the original control problem into sub-problems and solving it backwards in time,...
master thesis 2021
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Dankovic, Milan (author)
Inverse Learning is implemented in order to learn a control/decision policy (in the integer space) from an Expert Agent. The Learner Agent assumes that the Expert is acting minimizing an unknown cost function and tries to approximate it, through its own parametrized version of it. Learning can be performed in two different ways: offline ...
master thesis 2021
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Rademakers, Quinten (author)
Job scheduling is the process where jobs are arranged in a specific sequence. This process has always been a crucial subject for companies in numerous industries. A company could significantly improve its business performance if the scheduling process is not performed optimally. As companies and products become more and more data-driven, new...
master thesis 2021
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Vreugdenhil, Robbie (author)
We propose a novel approximation hierarchy for cardinality-constrained, convex quadraticprograms that exploits the rank-dominating eigenvectors of the quadratic matrix. Each levelof approximation admits a min-max characterization whose objective function can be op-timized over the binary variables analytically, while preserving convexity in the...
master thesis 2021
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Coehoorn, Jesse (author)
The Free Energy Principle, which underlies Active Inference (AI), is a way to explain human perception and behaviour. Previous literature has hinted at a relation between AI and Linear-Quadratic Gaussian (LQG) control, the latter being a textbook controller. AI and LQG are, however, defined with different settings in mind: LQG has access to...
master thesis 2021
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Silva Cruz Guerreiro Mestre, Francisca (author)
The rising number of electricity consumers poses a challenge to power generators and grid operators in maintaining a balanced grid. Peak shaving is a technique that consists of shifting electricity consumption from hours of high demand to times of low demand, and has been gaining popularity in recent years. It allows to reduce peak loads which...
master thesis 2020
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van Rooijen, Dennis (author)
This paper outlines a mathematical model for the creation of a robust timetable for the optimization of the lightrail network in Rotterdam and The Hague. A novel spread-based goal function focuses on minimizing wait time as opposed to delay. The situation is modeled as a Quadratic Stochastic Programming problem, and solved by employing Monte...
master thesis 2020
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Hajee, B. (author)
The built environment is becoming healthier, smarter and more energy efficient during the transition to green building activity. Multi-sensor networks are a promising component of this transition, provided that their measurements are reliable. The reliability of the data is a real concern for DWA, as they use it for control purposes as well as...
master thesis 2020
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Zattoni Scroccaro, Pedro (author)
In this thesis, we study Online Convex Optimization algorithms that exploit predictive and/or dynamical information about a problem instance. These features are inspired by recent developments in the Online Mirror Decent literature. When the Player's performance is compared with the best fixed decision in hindsight, we show that it is possible...
master thesis 2020
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Swart, Jeroen (author)
A time series is a series of data points indexed in time order. It can represent real world processes, such as demand for groceries, electricity usage and stock prices. Machine Learning (ML) models that accurately forecast these processes enable improved decision-making for reducing waste and increasing efficiency. Previous research has produced...
master thesis 2020
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