Reducing the need for communication in wireless sensor networks using machine learning and planning techniques

Wireless sensor networks are commonly used to remotely and automatically monitor environments.<br/>One of the main challenges in wireless sensor networks is to use the limited available energy as efficiently as possible, to ensure longevity of the network. For such networks to survive their intended deployment period no energy may be wasted on...

Multi-period adaptive fleet planning problem: with Approximate Dynamic Programming

This MSc thesis presents a stochastic modelling approach to the multi-period airline fleet planning problem. Approximate Dynamic Programming (ADP) is used to model the impact of demand uncertainty on fleet decisions. The proposed ADP algorithm applies local value function approximations resulting from Gaussian kernel regressions to estimate...

Automating Valuations for Real-Estate

As GeoPhy is developing its business model and looking into the future of automated valu- ation models (AVM), this project delivers a proof of concept of a system that automates the training, maintaining, and delivery of machine learning models for automated valuations. In order to achieve this goal, the situation and problem were first analysed...

Active Learning in Image Quality Assessment

A world without digital images is unthinkable in this era of information and communication technology. Billions of images are created, shared and ultimately enjoyed by users every day. However, digital images are sensitive to a wide variety of distortions during the delivery mechanism it goes through. Any of the distortions that can arise during...

Flowr: Enhancing Dynamic Market Audience Creation

Omnicom Media Group (OMG) is a company heavily involved in marketing and advertising. Our client is Annalect, a solutions provider that helps the marketers of OMG to make data actionable. OMG has processed cookie data to help their marketers set up advertisement campaigns. They buy this cookie data from a 3rd party. They also manage, however, a...

Transaction Monitoring

Being a bank, bunq deals with transaction fraud on a regular basis. All transactions that are handled by bunq are monitored for these cases of fraud by a transaction monitoring system. When this system flags a transaction as being possibly fraudulent, a bunq employee has to manually check this transaction. The problem with the current system is...

Facade labelling using neural networks

This report describes the process of the Bachelorproject(TI3806) done for ‘De Energiebespaarders’, a startup in Amsterdam striving to make homes more energy efficient through accessible advice and installation of insulation or solar panels. The goal of the project was to apply machine learning to improve their system for identifying house...

Intelligent Flapping Wing Control: Reinforcement Learning for the DelFly

Flight control of the DelFly is challenging, because of its complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a Proportional-Integral controller using Reinforcement Learning. Furthermore, a novel...

Machine learning based aircraft arrival / departure registrations

The aviation industry is vastly growing, as travelling by air is more common today than it ever was. However due too inefficiency and lack of communication of accurate flight information between airports, congestion and delays are occurring on a daily basis. While Collaborative Decision Making (CDM) is developed by Euro control to address this...

Scalable machine learning algorithms on a big data infrastructure

Two currently popular topics in computer science are machine learning and big data. Often the two are combined to obtain powerful machines with learning capabilities or high throughput data analysis programs among others. This research analyses which machine learning techniques qualify to be efficiently implemented on a scalable big data...

Evaluation of Machine Learning Algorithms for Outlier Detection in Clustered Code Fragments

Many software systems are designed to be long-lived due to the costs involved in developing new systems. Changes in these systems are inevitable due to constant modifications in requirements that are necessitated by the constantly changing nature of the business environment or detection of faults.To adapt their software to all these changing...

Personalised Gaming Using Learning Technology

This Bachelor Thesis is part of the Bachelor Computer Science and Engineering program at Delft University of Technology. During this project the team had to create an extension to an existing game framework. This extension consisted of improved artificial intelligence for the antagonist and a maze generator that could be re-used as a library....

Inferring Arithmetic Expressions from Data

We present a framework for learning arithmetic expressions from a set of observations. Our intention is to introduce a Bayesian method for what is known as equation discovery. Our method is based on measuring a degree of belief (posterior probability) for a set of hypothesized expressions to find those which best explain the observed data. This...

Een Semantisch Connectionistisch Redeneersysteem (SCORE)

Het onderzoek heeft zich gericht op de ontwikkeling van een Semantisch Connectionistisch Redeneersysteem (SCORE). SCORE kan de basis vormen voor een volwaardig expertsysteem. De, voor een expertsysteem van elementair belang zijnde, fundamentele inferentie principes, kennisacquisitie principes en kennisrepresentatievormen hebben in SCORE de...