PUMA publications for /tag/thesishttps://puma.ub.uni-stuttgart.de/tag/thesisPUMA RSS feed for /tag/thesis2024-03-29T14:24:39+01:00Entwicklung eines neuronalen Netzwerks zur Optimierung der Datenübertragungsqualität von Kleinsatellitenplattformenhttps://puma.ub.uni-stuttgart.de/bibtex/2c2e43b48393c0bb8d19ce351dd4a671f/analyticcompanalyticcomp2024-03-22T01:30:27+01:00thesis <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Cedric Holeczek" itemprop="url" href="/person/1af04130a1ca6708d9a868b7a10168d17/author/0"><span itemprop="name">C. Holeczek</span></a></span></span>. </span><span class="additional-entrytype-information"><em>Universität Stuttgart, </em><em><span itemprop="educationalUse">Master's Thesis</span>, </em>(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Fri Mar 22 01:30:27 CET 2024Entwicklung eines neuronalen Netzwerks zur Optimierung der Datenübertragungsqualität von KleinsatellitenplattformenMaster's Thesis2021thesis Kleinsatelliten sind heute eine zunehmend wichtige Möglichkeit für die Forschung, Experimente und Nutzlasten in Erdumlaufbahnen zu bringen. Bei diesen Satellitensystemen besteht einerseits ein wachsender Bedarf an Datenübertragung zu einer Bodenstation, andererseits gelten in der Raumfahrt verschiedene Einschränkungen bei Aufbau und Betrieb von Funkkommunikationssystemen. Heutige Funksysteme haben meist statische Transceiver-Konfigurationen und die genutzten Übertragungsraten entsprechen der Worst-case-Betrachtung für den jeweiligen Fall. Neuerdings werden adaptive Ansätze für den Betrieb von Funkverbindungen zwischen Satellit und Bodenstation beschrieben, bei denen die Transceiver-Konfiguration, gesteuert durch einen lernenden Algorithmus, im Betrieb verändert und den jeweiligen äusseren Einflüssen angepasst wird. Die Grundlage für die verwendeten Algorithmen ist das Reinforcement Learning Model. Ziel dieser Arbeit war die Weiterentwicklung eines für die Raumfahrtfunkkommunikation entwickelten Algorithmus und der Test dieses Algorithmus' innerhalb einer Simulationsumgebung, um die Auswirkungen der Veränderungen zu bewerten. Der modifizierte Algorithmus wurde in einer Software-Simulationsumgebung betrieben, welche die digitale Signalverarbeitung von Sender und Empfänger sowie die sich verändernden Übertragungsbedingungen während eines Satellitenüberfluges abbildete. Die Modifikation wurde mit dem Originalalgorithmus hinsichtlich der erreichbaren Datenübertragung verglichen. Unter anderem wurde die Anzahl der für das Lernen verwendeten Neuronalen Netze variiert. Weiterhin wurden verschiedene Hyperparameter variiert und die Auswirkungen auf die Datenübertragung untersucht. Eine Anpassung der Hyperparameter führte dabei zu einer Übertragung von 57,8% mehr Daten als bei der Baseline-Implementierung. Sowohl die Veränderung in der Architektur als auch die Reduzierung der parallel ausgeführten Neuronalen Netze führte zu leichten Performanzeinbußen von 4,0% und 3,3%. Die Ergebnisse der hier durchgeführten Software-Simulationen lassen sich jedoch nicht auf Hardware-Simulationsumgebungen oder tatsächliche Funkverbindungen übertragen. Die Implementierung der verschiedenen Algorithmusvarianten erlaubt es, diese Varianten zukünftig auch mit Hardware-Übertragungsstrecken zu testen und später ein solches System auf einem Kleinsatelliten zu erproben.Learning quantitative argumentation frameworks using sparse neural networks and swarm intelligence algorithmshttps://puma.ub.uni-stuttgart.de/bibtex/2d85d195da22a4f188d3834e164a37709/analyticcompanalyticcomp2024-03-22T01:30:09+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mohamad Wahed Bazo" itemprop="url" href="/person/1bf250db32dc28d8ac1815bf3ed23a0dc/author/0"><span itemprop="name">M. Bazo</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Fri Mar 22 01:30:09 CET 2024Learning quantitative argumentation frameworks using sparse neural networks and swarm intelligence algorithmsBachelor's Thesis2021thesis Argumentation Frameworks are an approach of formalizing arguments and their interrelations in a graph structure. They can be used to draw conclusions from this modelling of knowledge. Since argumentation is an important part of human reasoning, these graph structures can be considered easily interpretable, what makes this technology an interesting explainable artificial intelligence method. Although this is not their main purpose, Quantitative Argumentation Frameworks can be used to solve classification problems by following a new approach. This approach is based on constructing them out of multilayer perceptrons (MLP), based on the work of Potyka. In this thesis we were motivated to construct Quantitative Argumentation Frameworks out of sparse MLPs. A swarm intelligence algorithm, namely Particle Swarm Optimization (PSO), was developed to search for sparse MLP models with specific characteristics that relate to performance and topology of the graphical structures. Models were implemented, tested, and evaluated on three different datasets. The implementation includes preprocessing of the datasets, parameter learning of MLPs based on backpropagation, and structure learning of the MLP graphical structures. The evaluation involves constructing fully connected MLPs and decision trees for comparison purposes. The resulting models achieved high performance and low complexity in their structure. The PSO algorithm also proved its efficiency in solving the structure learning.Gaze and voice driven hands free gaminghttps://puma.ub.uni-stuttgart.de/bibtex/2ee28263a02297c26ff792381a2baeeaf/analyticcompanalyticcomp2024-03-22T01:29:44+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mike Lauer" itemprop="url" href="/person/169198e7af201f44087f61d5ca127b8f9/author/0"><span itemprop="name">M. Lauer</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Fri Mar 22 01:29:44 CET 2024Gaze and voice driven hands free gamingBachelor's Thesis2021thesis Playing computer games is one of the most common and enjoyable activities in the modern lifestyle. However, people with motor impairments who cannot use mouse and keyboard are not able to interact with computing devices as required by the game design. In this regard, novel interaction techniques can enable hands-free control using gaze and voice as input modalities to assist people with motor and speech impairments. This is the first evaluation of a video game control method consisting of a combination of eye tracking and non-verbal voice commands (e.g. humming) applied in a 2D jump-and-run game environment involving essential spatio-temporal interactions. To assess interaction feasibility, the evaluation study consisted of both qualitative and quantitative measures. In addition, the feasibility was validated with a target user group of people with motor impairments. Overall, the results indicate a lower but competitive performance while increasing the fun factor.Extracting and segmenting high-variance references from PDF documents with BERThttps://puma.ub.uni-stuttgart.de/bibtex/2ba86189219ae8a06b24ac70c1c10ea23/analyticcompanalyticcomp2024-03-22T01:29:31+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hasan Evci" itemprop="url" href="/person/136be89fd78131324b4955c19d57dead1/author/0"><span itemprop="name">H. Evci</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Fri Mar 22 01:29:31 CET 2024Extracting and segmenting high-variance references from PDF documents with BERTBachelor's Thesis2021thesis The extraction and segmentation of references from scientific articles is a core task of modern digital libraries. Once references are extracted and segmented, the bibliographic information can be made publicly available and linked, enabling efficient literature study. However, references often vary in their structure and content. This makes the extraction and segmentation of references a challenging but valuable task. The purpose of this thesis is to investigate whether Bidirectional Encoder Representations from Transformers (BERT) is suitable for the extraction and segmentation of bibliographic references. Therefore, we follow a deep learning approach for the extraction and segmentation of references from PDF documents. We use a neural network architecture based on BERT, a deep language representation model that has significantly increased performance on many natural language processing tasks. Over the BERT output, we put a linear-chain Conditional Random Field. We experiment with different BERT models and input formats and also examine two approaches for reference extraction and segmentation. The experiments are evaluated on a challenging dataset that contains both English and German social science publications with highly varying references. Our results show that the best performing BERT models were pre-trained on similar data to the data that we used for the fine-tuning of the BERT models on the task of reference extraction and reference segmentation. Moreover, our findings show that long, context-based input sequences yield the best results. The extraction model identifies and extracts references with an average F1-score of 81.9%. References are segmented with an average F1-score of 93.6%. We show that our models compare well to one other previously published work. Our conclusion is that BERT is a suitable choice for reference extraction and reference segmentation.How users attend to online comments: an eye-tracking approachhttps://puma.ub.uni-stuttgart.de/bibtex/20a823fe06187e587861c7670254bca4b/analyticcompanalyticcomp2024-03-22T01:29:10+01:00thesis <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alona Liuzniak" itemprop="url" href="/person/16782596755ce3e1052056da2fff0a87c/author/0"><span itemprop="name">A. Liuzniak</span></a></span></span>. </span><span class="additional-entrytype-information"><em>Department of Analytical Computing, Institute for Parallel and Distributed Systems, Universität Stuttgart,, </em><em><span itemprop="educationalUse">Master's Thesis</span>, </em>(<em><span>2022<meta content="2022" itemprop="datePublished"/></span></em>)</span>Fri Mar 22 01:29:10 CET 2024How users attend to online comments: an eye-tracking approachMaster's Thesis2022thesis The modern world is highly influenced by user-contributed content such as user comments, which is one of the most popular forms of communication on social media. They help build a connection between content creators and content consumers, as well as a connection between users of a social platform, which makes them highly relevant for community interaction. However, researchers have not treated users' comment reading strategies in much detail. The existing accounts are limited to address solely an explicit comment reading approach, which is only able to track an active interaction, e.g. analyze attention by counting a number of clicks. This technique can not fully estimate the drawn attention, as 73% of people do not actively interact with comments. Eye-tracking plays a vital role in solving the task of analysing implicit attention, as it allows to estimate such crucial characteristics, as comment features, which drew the most attention, the amount of attention given or the order of seeing specific comments. The current research uses eye-tracking based attention analysis for investigating the phenomena of users' reading behaviour on the real YouTube interface. The present master thesis concentrates on analysing users' attention mechanisms and reading behaviour and finding a correlation between the comment features such as length, language, sparseness, comment position, number of likes, presence of replies, presence of video creator's like and authorised user label, which cannot be exhibited from a pure textual point of view. This work shows that number of likes and presence of answers contribute the most to the attention drawn by comments. The analysis revealed that the category of a video deeply influences the emotion and length of the popular comments: if people are looking for useful information, which applies to educational videos, they tend to pay attention to neutral and long comments, whereas while looking for entertainment, people most probably will notice short and positive comments. The two important findings from the gender analysis are that women tend to read longer comments, but skip a higher percentage of comments than men. It is hoped that this research will contribute to a deeper understanding of the features that draw the most attention, which can be exploited in content generation strategies and developing new ranking algorithms.Multistep prediction of vehicle states using transformershttps://puma.ub.uni-stuttgart.de/bibtex/2dee25f714d96e4513199bd08b7118c22/analyticcompanalyticcomp2024-03-22T01:28:40+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefan Bolz" itemprop="url" href="/person/16b564fb875aa899ba32f9da407418916/author/0"><span itemprop="name">S. Bolz</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Fri Mar 22 01:28:40 CET 2024Multistep prediction of vehicle states using transformersBachelor's Thesis2021thesis Multistep prediction is the prediction of states based on initial states and a series of control inputs. This paper focuses on developing transformer models for multistep prediction of vehicle states and testing different modifications of the transformer architecture using the example of the prediction of a ship simulation. Research in NLP promises advantages with regard to training time and prediction accuracy for the transformer architecture compared to a state-of-the-art LSTM model. The author also investigates whether positional encodings are useful in this scenario and if a transformer model can learn the order of the inputs without positional encodings.Multistep prediction of vehicle states using transformershttps://puma.ub.uni-stuttgart.de/bibtex/2dee25f714d96e4513199bd08b7118c22/joyjoy2024-03-20T22:28:51+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefan Bolz" itemprop="url" href="/person/16b564fb875aa899ba32f9da407418916/author/0"><span itemprop="name">S. Bolz</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Wed Mar 20 22:28:51 CET 2024Multistep prediction of vehicle states using transformersBachelor's Thesis2021thesis Multistep prediction is the prediction of states based on initial states and a series of control inputs. This paper focuses on developing transformer models for multistep prediction of vehicle states and testing different modifications of the transformer architecture using the example of the prediction of a ship simulation. Research in NLP promises advantages with regard to training time and prediction accuracy for the transformer architecture compared to a state-of-the-art LSTM model. The author also investigates whether positional encodings are useful in this scenario and if a transformer model can learn the order of the inputs without positional encodings.How users attend to online comments: an eye-tracking approachhttps://puma.ub.uni-stuttgart.de/bibtex/20a823fe06187e587861c7670254bca4b/joyjoy2024-03-20T22:16:07+01:00thesis <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alona Liuzniak" itemprop="url" href="/person/16782596755ce3e1052056da2fff0a87c/author/0"><span itemprop="name">A. Liuzniak</span></a></span></span>. </span><span class="additional-entrytype-information"><em>Department of Analytical Computing, Institute for Parallel and Distributed Systems, Universität Stuttgart,, </em><em><span itemprop="educationalUse">Master's Thesis</span>, </em>(<em><span>2022<meta content="2022" itemprop="datePublished"/></span></em>)</span>Wed Mar 20 22:16:07 CET 2024How users attend to online comments: an eye-tracking approachMaster's Thesis2022thesis The modern world is highly influenced by user-contributed content such as user comments, which is one of the most popular forms of communication on social media. They help build a connection between content creators and content consumers, as well as a connection between users of a social platform, which makes them highly relevant for community interaction. However, researchers have not treated users' comment reading strategies in much detail. The existing accounts are limited to address solely an explicit comment reading approach, which is only able to track an active interaction, e.g. analyze attention by counting a number of clicks. This technique can not fully estimate the drawn attention, as 73% of people do not actively interact with comments. Eye-tracking plays a vital role in solving the task of analysing implicit attention, as it allows to estimate such crucial characteristics, as comment features, which drew the most attention, the amount of attention given or the order of seeing specific comments. The current research uses eye-tracking based attention analysis for investigating the phenomena of users' reading behaviour on the real YouTube interface. The present master thesis concentrates on analysing users' attention mechanisms and reading behaviour and finding a correlation between the comment features such as length, language, sparseness, comment position, number of likes, presence of replies, presence of video creator's like and authorised user label, which cannot be exhibited from a pure textual point of view. This work shows that number of likes and presence of answers contribute the most to the attention drawn by comments. The analysis revealed that the category of a video deeply influences the emotion and length of the popular comments: if people are looking for useful information, which applies to educational videos, they tend to pay attention to neutral and long comments, whereas while looking for entertainment, people most probably will notice short and positive comments. The two important findings from the gender analysis are that women tend to read longer comments, but skip a higher percentage of comments than men. It is hoped that this research will contribute to a deeper understanding of the features that draw the most attention, which can be exploited in content generation strategies and developing new ranking algorithms.Extracting and segmenting high-variance references from PDF documents with BERThttps://puma.ub.uni-stuttgart.de/bibtex/2ba86189219ae8a06b24ac70c1c10ea23/joyjoy2024-03-20T22:13:08+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hasan Evci" itemprop="url" href="/person/136be89fd78131324b4955c19d57dead1/author/0"><span itemprop="name">H. Evci</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Wed Mar 20 22:13:08 CET 2024Extracting and segmenting high-variance references from PDF documents with BERTBachelor's Thesis2021thesis The extraction and segmentation of references from scientific articles is a core task of modern digital libraries. Once references are extracted and segmented, the bibliographic information can be made publicly available and linked, enabling efficient literature study. However, references often vary in their structure and content. This makes the extraction and segmentation of references a challenging but valuable task. The purpose of this thesis is to investigate whether Bidirectional Encoder Representations from Transformers (BERT) is suitable for the extraction and segmentation of bibliographic references. Therefore, we follow a deep learning approach for the extraction and segmentation of references from PDF documents. We use a neural network architecture based on BERT, a deep language representation model that has significantly increased performance on many natural language processing tasks. Over the BERT output, we put a linear-chain Conditional Random Field. We experiment with different BERT models and input formats and also examine two approaches for reference extraction and segmentation. The experiments are evaluated on a challenging dataset that contains both English and German social science publications with highly varying references. Our results show that the best performing BERT models were pre-trained on similar data to the data that we used for the fine-tuning of the BERT models on the task of reference extraction and reference segmentation. Moreover, our findings show that long, context-based input sequences yield the best results. The extraction model identifies and extracts references with an average F1-score of 81.9%. References are segmented with an average F1-score of 93.6%. We show that our models compare well to one other previously published work. Our conclusion is that BERT is a suitable choice for reference extraction and reference segmentation.Gaze and voice driven hands free gaminghttps://puma.ub.uni-stuttgart.de/bibtex/2ee28263a02297c26ff792381a2baeeaf/joyjoy2024-03-20T21:56:18+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mike Lauer" itemprop="url" href="/person/169198e7af201f44087f61d5ca127b8f9/author/0"><span itemprop="name">M. Lauer</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Wed Mar 20 21:56:18 CET 2024Gaze and voice driven hands free gamingBachelor's Thesis2021thesis Playing computer games is one of the most common and enjoyable activities in the modern lifestyle. However, people with motor impairments who cannot use mouse and keyboard are not able to interact with computing devices as required by the game design. In this regard, novel interaction techniques can enable hands-free control using gaze and voice as input modalities to assist people with motor and speech impairments. This is the first evaluation of a video game control method consisting of a combination of eye tracking and non-verbal voice commands (e.g. humming) applied in a 2D jump-and-run game environment involving essential spatio-temporal interactions. To assess interaction feasibility, the evaluation study consisted of both qualitative and quantitative measures. In addition, the feasibility was validated with a target user group of people with motor impairments. Overall, the results indicate a lower but competitive performance while increasing the fun factor.Learning quantitative argumentation frameworks using sparse neural networks and swarm intelligence algorithmshttps://puma.ub.uni-stuttgart.de/bibtex/2d85d195da22a4f188d3834e164a37709/joyjoy2024-03-20T21:42:02+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mohamad Wahed Bazo" itemprop="url" href="/person/1bf250db32dc28d8ac1815bf3ed23a0dc/author/0"><span itemprop="name">M. Bazo</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Wed Mar 20 21:42:02 CET 2024Learning quantitative argumentation frameworks using sparse neural networks and swarm intelligence algorithmsBachelor's Thesis2021thesis Argumentation Frameworks are an approach of formalizing arguments and their interrelations in a graph structure. They can be used to draw conclusions from this modelling of knowledge. Since argumentation is an important part of human reasoning, these graph structures can be considered easily interpretable, what makes this technology an interesting explainable artificial intelligence method. Although this is not their main purpose, Quantitative Argumentation Frameworks can be used to solve classification problems by following a new approach. This approach is based on constructing them out of multilayer perceptrons (MLP), based on the work of Potyka. In this thesis we were motivated to construct Quantitative Argumentation Frameworks out of sparse MLPs. A swarm intelligence algorithm, namely Particle Swarm Optimization (PSO), was developed to search for sparse MLP models with specific characteristics that relate to performance and topology of the graphical structures. Models were implemented, tested, and evaluated on three different datasets. The implementation includes preprocessing of the datasets, parameter learning of MLPs based on backpropagation, and structure learning of the MLP graphical structures. The evaluation involves constructing fully connected MLPs and decision trees for comparison purposes. The resulting models achieved high performance and low complexity in their structure. The PSO algorithm also proved its efficiency in solving the structure learning.Entwicklung eines neuronalen Netzwerks zur Optimierung der Datenübertragungsqualität von Kleinsatellitenplattformenhttps://puma.ub.uni-stuttgart.de/bibtex/2c2e43b48393c0bb8d19ce351dd4a671f/joyjoy2024-03-20T21:11:12+01:00thesis <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Cedric Holeczek" itemprop="url" href="/person/1af04130a1ca6708d9a868b7a10168d17/author/0"><span itemprop="name">C. Holeczek</span></a></span></span>. </span><span class="additional-entrytype-information"><em>Universität Stuttgart, </em><em><span itemprop="educationalUse">Master's Thesis</span>, </em>(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Wed Mar 20 21:11:12 CET 2024Entwicklung eines neuronalen Netzwerks zur Optimierung der Datenübertragungsqualität von KleinsatellitenplattformenMaster's Thesis2021thesis Kleinsatelliten sind heute eine zunehmend wichtige Möglichkeit für die Forschung, Experimente und Nutzlasten in Erdumlaufbahnen zu bringen. Bei diesen Satellitensystemen besteht einerseits ein wachsender Bedarf an Datenübertragung zu einer Bodenstation, andererseits gelten in der Raumfahrt verschiedene Einschränkungen bei Aufbau und Betrieb von Funkkommunikationssystemen. Heutige Funksysteme haben meist statische Transceiver-Konfigurationen und die genutzten Übertragungsraten entsprechen der Worst-case-Betrachtung für den jeweiligen Fall. Neuerdings werden adaptive Ansätze für den Betrieb von Funkverbindungen zwischen Satellit und Bodenstation beschrieben, bei denen die Transceiver-Konfiguration, gesteuert durch einen lernenden Algorithmus, im Betrieb verändert und den jeweiligen äusseren Einflüssen angepasst wird. Die Grundlage für die verwendeten Algorithmen ist das Reinforcement Learning Model. Ziel dieser Arbeit war die Weiterentwicklung eines für die Raumfahrtfunkkommunikation entwickelten Algorithmus und der Test dieses Algorithmus' innerhalb einer Simulationsumgebung, um die Auswirkungen der Veränderungen zu bewerten. Der modifizierte Algorithmus wurde in einer Software-Simulationsumgebung betrieben, welche die digitale Signalverarbeitung von Sender und Empfänger sowie die sich verändernden Übertragungsbedingungen während eines Satellitenüberfluges abbildete. Die Modifikation wurde mit dem Originalalgorithmus hinsichtlich der erreichbaren Datenübertragung verglichen. Unter anderem wurde die Anzahl der für das Lernen verwendeten Neuronalen Netze variiert. Weiterhin wurden verschiedene Hyperparameter variiert und die Auswirkungen auf die Datenübertragung untersucht. Eine Anpassung der Hyperparameter führte dabei zu einer Übertragung von 57,8% mehr Daten als bei der Baseline-Implementierung. Sowohl die Veränderung in der Architektur als auch die Reduzierung der parallel ausgeführten Neuronalen Netze führte zu leichten Performanzeinbußen von 4,0% und 3,3%. Die Ergebnisse der hier durchgeführten Software-Simulationen lassen sich jedoch nicht auf Hardware-Simulationsumgebungen oder tatsächliche Funkverbindungen übertragen. Die Implementierung der verschiedenen Algorithmusvarianten erlaubt es, diese Varianten zukünftig auch mit Hardware-Übertragungsstrecken zu testen und später ein solches System auf einem Kleinsatelliten zu erproben.On the Stability of Motion in Non-smooth Mechanical Systemshttps://puma.ub.uni-stuttgart.de/bibtex/234d7950c70875b50bc05665c39bfec97/inminm2022-03-22T15:26:36+01:00from:rleine imported leine thesis <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. I Leine" itemprop="url" href="/person/1907a3b749a509a496486246aa53ac2dd/author/0"><span itemprop="name">R. Leine</span></a></span></span>. </span><span class="additional-entrytype-information"><em>ETH Zurich, </em><em><span itemprop="educationalUse">Habilitation thesis</span>, </em>(<em><span>June 2006<meta content="June 2006" itemprop="datePublished"/></span></em>)</span>Tue Mar 22 15:26:36 CET 2022junOn the Stability of Motion in Non-smooth Mechanical SystemsHabilitation thesis2006from:rleine imported leine thesis Bifurcations in Discontinuous Mechanical Systems of Filippov-Typehttps://puma.ub.uni-stuttgart.de/bibtex/22a30cfcb274cceb4aa91447655f62cd8/inminm2022-03-22T15:26:36+01:00from:rleine imported leine thesis <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. I. Leine" itemprop="url" href="/person/161252e86223003e47df9f9a7b016b29a/author/0"><span itemprop="name">R. Leine</span></a></span></span>. </span><span class="additional-entrytype-information"><em>Eindhoven University of Technology, </em>(<em><span>June 2000<meta content="June 2000" itemprop="datePublished"/></span></em>)</span>Tue Mar 22 15:26:36 CET 2022junBifurcations in Discontinuous Mechanical Systems of Filippov-Type2000from:rleine imported leine thesis Characteristics of indoor/outdoor particulate pollution in urban and rural residential environment of Pakistanhttps://puma.ub.uni-stuttgart.de/bibtex/2ab6d2fca94f3844e3f38600c84d59e21/viditmadanviditmadan2018-02-15T11:29:36+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="I. Colbeck" itemprop="url" href="/person/1ff8add37c24c5d0bc5c0c51b7afb708b/author/0"><span itemprop="name">I. Colbeck</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Z. A. Nasir" itemprop="url" href="/person/1ff8add37c24c5d0bc5c0c51b7afb708b/author/1"><span itemprop="name">Z. Nasir</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Z. Ali" itemprop="url" href="/person/1ff8add37c24c5d0bc5c0c51b7afb708b/author/2"><span itemprop="name">Z. Ali</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Indoor Air</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">20 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">40--51</span></em> </span>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)</span>Thu Feb 15 11:29:36 CET 2018Indoor Air140--51Characteristics of indoor/outdoor particulate pollution in urban and rural residential environment of Pakistan202010thesis Abstract Particulate pollution has emerged as a serious environmental health concern in Pakistan. The use of biomass fuels in traditional stoves produces high levels of indoor air pollutants. In Pakistan, 94% of rural and 58% of urban households depend on biomass fuel. This study investigates variations in indoor/outdoor concentrations of particulate matter during various activities for three different micro-environments in Pakistan. At a rural site, the average indoor/outdoor ratios for PM10, PM2.5, and PM1, in kitchens using biomass fuels were 3.80, 4.36, and 4.11, respectively. A large variation was recorded in the mass concentration of particulate matter during cooking with concentrations in the range 4000–8555 μg/m3. In a living room at a rural site, the average indoor/outdoor ratios for PM10, PM2.5, and PM1 were 1.74, 2.49, and 3.01, respectively. At the urban site, the average indoor/outdoor ratios for the same size fractions were 1.71, 2.88, and 3.47, respectively. Cooking, cleaning and smoking were identified as principal contributors to the high indoor levels of particulate matter. This study showed considerably high concentrations of particulate matter, particularly in kitchens using biomass fuels, as compared to living areas. Thus women and children face the greatest exposure due to the amount of time they spend in the kitchen.Practical ImplicationsIn the developing world, particulate air pollution, both indoor and outdoor, is a substantial health hazard to the public. The very high concentrations of particulate matter in both rural and urban sites, particularly in kitchens using biomass fuels emphasize the severity of this issue in Pakistan. Women and children are extensively at risk due to amount of time spent in kitchens. This state of affairs calls for a large-scale intervention to reduce the exposure to indoor air pollution.onlinelibrary.wiley.com/doi/10.1111/j.1600-0668.2009.00624.x/epdfThe impact of occupants’ behaviours on building energy analysis: A research reviewhttps://puma.ub.uni-stuttgart.de/bibtex/2253e2f96c47fc4b3367e0113602f1d1a/viditmadanviditmadan2018-02-09T17:56:23+01:00thesis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Elham Delzendeh" itemprop="url" href="/person/1f5817f5bd1df8154b4efd86cc6f8bc6c/author/0"><span itemprop="name">E. Delzendeh</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Song Wu" itemprop="url" href="/person/1f5817f5bd1df8154b4efd86cc6f8bc6c/author/1"><span itemprop="name">S. Wu</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Angela Lee" itemprop="url" href="/person/1f5817f5bd1df8154b4efd86cc6f8bc6c/author/2"><span itemprop="name">A. Lee</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ying Zhou" itemprop="url" href="/person/1f5817f5bd1df8154b4efd86cc6f8bc6c/author/3"><span itemprop="name">Y. Zhou</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Renewable and Sustainable Energy Reviews</span>, </em> </span>(<em><span>2017<meta content="2017" itemprop="datePublished"/></span></em>)</span>Fri Feb 09 17:56:23 CET 2018Renewable and Sustainable Energy Reviews1061 - 1071The impact of occupants’ behaviours on building energy analysis: A research review802017thesis Over the past 15 years, the evaluation of energy demand and use in buildings has become increasingly acute due to growing scientific and political pressure around the world in response to climate change. The estimation of the use of energy in buildings is therefore a critical process during the design stage. This paper presents a review of the literature published in leading journals through Science Direct and Scopus databases within this research domain to establish research trends, and importantly, to identify research gaps for future investigation. It has been widely acknowledged in the literature that there is an alarming performance gap between the predicted and actual energy consumption of buildings (sometimes this has been up to 300% difference). Analysis of the impact of occupants’ behaviour has been largely overlooked in building energy performance analysis. In short, energy simulation tools utilise climatic data and physical/ thermal properties of building elements in their calculations, and the impact of occupants is only considered through means of fixed and scheduled patterns of behaviour. This research review identified a number of areas for future research including: larger scale analysis (e.g. urban analysis); interior design, in terms of space layout, and fixtures and fittings on occupants’ behaviour; psychological cognitive behavioural methods; and the integration of quantitative and qualitative research findings in energy simulation tools to name but a few.The impact of occupants’ behaviours on building energy analysis: A research review - ScienceDirectCoupling free flow and flow in porous media in biological and technical applicationshttps://puma.ub.uni-stuttgart.de/bibtex/2d55479d170fa2703db579401428b6683/orlinmitovorlinmitov2017-04-20T13:09:48+02:00hlrs ipv test thesis <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Katherina Baber" itemprop="url" href="/person/1163c31b050b170dc09d733e4b49d52b7/author/0"><span itemprop="name">K. Baber</span></a></span></span>. </span><span class="additional-entrytype-information"><em>Stuttgart, </em><em><span itemprop="educationalUse">Doktorarbeit</span>, </em>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)</span>Thu Apr 20 13:09:48 CEST 2017StuttgartMitteilungen / Institut für Wasser- und Umweltsystemmodellierung, Universität StuttgartCoupling free flow and flow in porous media in biological and technical applicationsDoktorarbeit2362014hlrs ipv test thesis Automatic detection of edit wars in Wikipediahttps://puma.ub.uni-stuttgart.de/bibtex/2b1fa695d355a4e8c83ac08f1e61f711c/hoppedhopped2015-10-28T14:07:39+01:00hlrs information_retrieval myown thesis wikipedia <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dennis Hoppe" itemprop="url" href="/person/1a9597a11a5213b446a2db16d546ecadc/author/0"><span itemprop="name">D. Hoppe</span></a></span></span>. </span><span class="additional-entrytype-information"><em>Bauhaus-Universität Weimar, Fakultät Medien, Medieninformatik, </em><em><span itemprop="educationalUse">Bachelor's Thesis</span>, </em>(<em><span>February 2008<meta content="February 2008" itemprop="datePublished"/></span></em>)</span>Wed Oct 28 14:07:39 CET 2015febAutomatic detection of edit wars in WikipediaBachelor's Thesis2008hlrs information_retrieval myown thesis wikipedia Cluster Labeling: Paradigms and Validationhttps://puma.ub.uni-stuttgart.de/bibtex/24273508760d823c7932798eb87b1c7ed/hoppedhopped2015-10-28T14:06:19+01:00data_mining hlrs myown thesis <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dennis Hoppe" itemprop="url" href="/person/10f3fc7640fbe103d4a255d333aa0ba5a/author/0"><span itemprop="name">D. Hoppe</span></a></span></span>. </span><span class="additional-entrytype-information"><em>Bauhaus-Universität Weimar, Fakultät Medien, Medieninformatik, </em><em><span itemprop="educationalUse">Master's Thesis</span>, </em>(<em><span>June 2010<meta content="June 2010" itemprop="datePublished"/></span></em>)</span>Wed Oct 28 14:06:19 CET 2015Master's ThesisjunCluster Labeling: Paradigms and ValidationMaster's Thesis2010data_mining hlrs myown thesis