Searched for: subject:"artificial%5C+neural%5C+networks"
(1 - 20 of 64)

Pages

document
Saz Ulibarrena, Veronica (author)
The use of low-thrust propulsion for interplanetary missions requires the implementation of new methods for the preliminary design of their trajectories. This thesis proposes a method using the Monotonic Basin Hopping global optimization algorithm to find feasible trajectories with optimum use of the mass of fuel for the case in which the...
master thesis 2021
document
van der Woude, Mark (author)
Widespread usage of Micro Aerial Vehicles (MAVs) has led to various airspace safety breaches, including near mid-air collisions with other aircraft. To ensure safe integration into general aviation, it is paramount that MAVs are equipped with an autonomous detect and avoid system when flying beyond the visual line-of-sight of the operator. The...
master thesis 2021
document
van den Haak, Daniel (author)
Lane change decision-making is an important challenge for automated vehicles, urging the need for high performance algorithms that are able to handle complex traffic situations. Deep reinforcement learning (DRL), a machine learning method based on artificial neural networks, has recently become a popular choice for modelling the lane change...
master thesis 2021
document
Becker, Midas (author)
<br/>Being a safe and healthy alternative for polluting and space-inefficient motorised vehicles, cycling can strongly improve living conditions in urban areas. Idling in front of traffic lights is seen as one of the major inconveniences of commuting by bicycle. By giving personalised speed advice, the probability of catching a green light can...
master thesis 2021
document
Brederveld, J.E.C. (author)
Various machine learning algorithms have been applied to find optimal lowthrust<br/>satellite trajectories, however, no fair comparison of their accuracy has been made yet. In this paper, two common and promising supervised machine learning algorithms are compared for their regression capacities:<br/>the artificial neural network and the...
master thesis 2021
document
Salvador, Beatriz (author), Oosterlee, C.W. (author), van der Meer, R. (author)
Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs). The classical problem of pricing European and American financial options, based on the corresponding PDE formulations, is studied here. Instead of using numerical techniques based on finite element or difference methods, we...
journal article 2021
document
Imani, Maryam (author), Hasan, Md Mahmudul (author), Bittencourt, Luiz Fernando (author), McClymont, Kent (author), Kapelan, Z. (author)
Resilience-informed water quality management embraces the growing environmental challenges and provides greater accuracy by unpacking the systems' characteristics in response to failure conditions in order to identify more effective opportunities for intervention. Assessing the resilience of water quality requires complex analysis of...
journal article 2021
document
Cheng, L. (author), Xin, H. (author), Groves, R.M. (author), Veljkovic, M. (author)
Acoustic emission (AE) is often used for structural health monitoring (SHM) in the wide field of engineering structures and one of its most beneficial attributes is the ability to localize the damage/crack based on the AE events. The vast majority of ongoing work on AE monitoring focues on geometrically simple structures or a confined area,...
journal article 2021
document
Rosin, T. R. (author), Romano, M. (author), Keedwell, E. (author), Kapelan, Z. (author)
Combined Sewer Overflows (CSOs) are a major source of pollution and urban flooding, spilling untreated wastewater directly into water bodies and the surrounding environment. If overflows can be predicted sufficiently in advance, then techniques are available for mitigation. This paper presents a novel bi-model committee evolutionary...
journal article 2021
document
Nadi Najafabadi, A. (author), Sharma, Salil (author), Snelder, M. (author), Bakri, Taoufik (author), van Lint, J.W.C. (author), Tavasszy, Lorant (author)
Short-term traffic prediction is an important component of traffic management systems. Around logistics hubs such as seaports, truck flows can have a major impact on the surrounding motorways. Hence, their prediction is important to help manage traffic operations. However, The link between short-term dynamics of logistics activities and the...
journal article 2021
document
Sun, B. (author), van Kampen, E. (author)
The scarcity of information regarding dynamics and full-state feedback increases the demand for a model-free control technique that can cope with partial observability. To deal with the absence of prior knowledge of system dynamics and perfect measurements, this paper develops a novel intelligent control scheme by combining global dual...
journal article 2021
document
Tsekos, C. (author), Tandurella, S. (author), de Jong, W. (author)
As the push towards more sustainable ways to produce energy and chemicals intensifies, efforts are needed to refine and optimize the systems that can give an answer to these needs. In the present work, the use of neural networks as modelling tools for lignocellulosic biomass pyrolysis main products yields estimation was evaluated. In order to...
journal article 2021
document
Nguyen, Hoa Minh (author), Rueda, José L. (author), Lekic, A. (author), Pham, Hoan Van (author)
The paper presents an approach for online centralized control in active distribution networks. It combines a proportional integral (PI) control unit with a corrective control unit (CCU), based on the principle of Model Predictive Control (MPC). The proposed controller is designed to accommodate the increasing penetration of distributed...
journal article 2021
document
Alwosheel, A.S.A. (author), van Cranenburgh, S. (author), Chorus, C.G. (author)
Artificial Neural Networks (ANNs) are rapidly gaining popularity in transportation research in general and travel demand analysis in particular. While ANNs typically outperform conventional methods in terms of predictive performance, they suffer from limited explainability. That is, it is very difficult to assess whether or not particular...
journal article 2021
document
Arora, Apoorva (author)
In this thesis, we design and assess a multi-slice resource allocation framework that is based on machine learning techniques (subset of artificial intelligence techniques). The proposed framework employs two machine learning techniques namely, artificial neural networks and reinforcement learning for resource management in sliced RAN....
master thesis 2020
document
Kraaijeveld, Jelmer (author)
This thesis outlines the use of measured data collected using the bGrid system to estimate the number of people in two rooms in the Microsoft office at Schiphol. The main objective is to derive a correlation that transforms the data into a specific number of people. The bGrid system consists of a network of sensor nodes that measure CO2...
master thesis 2020
document
Vermond, Lukas (author)
In an effort to make DNA sequencing more accessible and affordable, Oxford Nanopore Technologies developed the MinION: A portable cellphone sized DNA sequencing device. Translating the information from this device to a nucleotide sequence is called basecalling, and is done with the aid of artificial neural networks. In this thesis, we accelerate...
master thesis 2020
document
Erkan, K.E. (author)
This thesis is about pricing European options using a Fourier-based numerical method called the COS method under the rough Heston model. Besides examining the efficiency and accuracy of the COS method for pricing options under the rough Heston model, it is also investigated if the rough Heston model produces the advantages of the so-called rough...
master thesis 2020
document
Fortich Mora, Fredy (author)
As urbanization increases around the world, high-rise buildings will continue to become a more prevailing typology, nonetheless, due in part to cumbersome computational simulations, rarely do designers have enough information during the early stages of design, which is the time when their choices affect the most the efficiency of their building....
master thesis 2020
document
Sun, B. (author), van Kampen, E. (author)
A novel adaptive dynamic programming method, called incremental model-based global dual heuristic programming, is proposed to generate a self-learning adaptive flight controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online local dynamics identification, instead of the...
journal article 2020
Searched for: subject:"artificial%5C+neural%5C+networks"
(1 - 20 of 64)

Pages