Texte remanié de: Th. Ph. D.--Utrecht--Rijksuniversiteit te Utrecht, 1988. / Bibliogr. p. 233-242. Index.

In the human visual system, one of the most prominent functions of the extensive feedback from the higher brain areas within and outside of the visual cortex is attentional modulation. The feedback helps the brain to concentrate its resources on visual features that are relevant for recognition, i. e. it iteratively selects certain aspects of the visual scene for refined processing by the lower areas until the inference process in the higher areas converges to a single hypothesis about this scene. In order to minimize a number of required selection-refinement iterations, one has to find a short sequence of maximally informative portions of the visual input. Since the feedback is not static, the selection process is adapted to a scene that should be recognized. To find a scene-specific subset of informative features, the adaptive selection process on every iteration utilizes results of previous processing in order to reduce the remaining uncertainty about the visual scene. This phenomenon inspired us to develop a computational algorithm solving a visual classification task that would incorporate such principle, adaptive feature selection. It is especially interesting because usually feature selection methods are not adaptive as they define a unique set of informative features for a task and use them for classifying all objects. However, an adaptive algorithm selects features that are the most informative for the particular input. Thus, the selection process should be driven by statistics of the environment concerning the current task and the object to be classified. Applied to a classification task, our adaptive feature selection algorithm favors features that maximally reduce the current class uncertainty, which is iteratively updated with values of the previously selected features that are observed on the testing sample. In information-theoretical terms, the selection criterion is the mutual information of a class variable and a feature-candidate conditioned on the already selected features, which take values observed on the current testing sample. Then, the main question investigated in this thesis is whether the proposed adaptive way of selecting features is advantageous over the conventional feature selection and in which situations. Further, we studied whether the proposed adaptive information-theoretical selection scheme, which is a computationally complex algorithm, is utilized by humans while they perform a visual classification task. For this, we constructed a psychophysical experiment where people had to select image parts that as they think are relevant for classification of these images. We present the analysis of behavioral data where we investigate whether human strategies of task-dependent selective attention can be explained by a simple ranker based on the mutual information, a more complex feature selection algorithm based on the conventional static mutual information and the proposed here adaptive feature selector that mimics a mechanism of the iterative hypothesis refinement. Hereby, the main contribution of this work is the adaptive feature selection criterion based on the conditional mutual information. Also it is shown that such adaptive selection strategy is indeed used by people while performing visual classification.

This thesis focuses on analyzing the synchronization of time delays between mutually coupled phase-locked loops (PLLs) in a flat hierarchy. Mutual synchronization refers to decentralized synchronization where there is no primary or secondary unit or control source. Consequently, it is an inherently self-organizing system in which each unit has equal rights, making it a democratic system. In this research, a dynamic nonlinear time-domain model is used to describe mutually delayed coupled oscillators. The predictions of this model are evaluated against experimental measurements. The time-domain model is based on the Kuramoto model. The Kuramoto model describes a network of coupled oscillators. As a first impression, this Kuramoto model is first analyzed for understanding of the effects of time delays between oscillators. The time domain model is based on a conventional PLL architecture modified to allow mutual coupling. The modifications include a circuit section that sums and weights all incoming phase differences. Overall, the measured results of this research study are in good agreement with the theoretical predictions of the time-domain model. The analysis allows the identification of the transient dynamics and the mechanisms that lead to mutual coupling and the formation of synchronized states through self-organized synchronization. The results show that the mutual coupling can self-organize its dynamics to synchronize even at time delays where the phenomenon of multistability of synchronized states occurs. A critical time delay beyond which a stable synchronized state cannot be achieved has been identified. The work also analyzes the dynamics and noise of synchronized states and finds that the dynamics near a synchronized state are correlated due to mutual coupling, leading to a reduction in noise. The noise correlation is affected by the direction of coupling, the number of nodes in the network, and the network topology. An improvement in phase noise of up to 14.42 dBc/Hz at 100 kHz offset from the carrier and 49.47ns delay was achieved using all-to-all coupling with four nodes. Furthermore, the hybrid approach, the combination of hierarchical and self-organizing synchronization architectures, is investigated. The dissertation presents an experimental study to understand how this affects a network of mutually delayed delay-coupled oscillators and whether the network of mutually coupled nodes can be abstracted as a secondary oscillator. A range in which the mutually delay-coupled network can be successfully synchronized by an external reference oscillator, depending on the synchronized state, is identified. In summary, this thesis provides valuable insights into the properties of mutually delay-coupled PLLs and their synchronization in flat hierarchies, and contributes to the understanding, design, and optimization of more practical networks of mutually delayed PLLs.:Abstract/Zusammenfassung Symbols and Abbreviations Previous Publications 1 Introduction 1.1 Classifications of Synchronization 1.2 A Historical Perspective on Mutual Synchronization 1.3 Extending the Understanding of Mutual Synchronization 1.4 Definitions and Methodologies 2 Model of Networks of Mutually Coupled PLLs 2.1 Coupled Oscillators – Kuramoto Model 2.1.1 Consequences of a Time Delay between Oscillators 2.1.2 Arbitrary Time Delays between Oscillators 2.2 Time-Domain Model of Delay-Coupled PLLs 2.2.1 Phase Detection 2.2.2 Loop-Filter 2.2.3 Voltage Controlled Oscillator 2.3 Prediction and Stability Analysis of Synchronized States 2.3.1 Assessing the Linear Stability of Synchronized States 2.3.2 Stability Consideration for Two Identical PLL Nodes 2.3.3 The Notion of Mode Locking 2.3.4 Effects of Heterogeneity on Synchronized States 2.4 Dynamics and Noise in Synchronized States 2.4.1 Gain and Phase Margin of a PLL Node 2.4.2 Phase Noise 2.5 Key Findings of the Theoretical Model 3 Design of Phase-Locked-Loops for Mutual Synchronization 3.1 PLL Nodes Dedicated for Mutual Synchronization 3.1.1 Phase Detection Circuitry 3.1.2 Adder Chain 3.2 Additional Circuitry for Implementing a Time Delay 4 Experimental Analysis of Mutually Time-Delayed Coupled PLLs 4.1 Synchronized States Including Oscillator Nonlinearity 4.2 Stability of Multistable Synchronized States 4.3 Critical Time Delay Between Two Coupled Nodes 4.4 Combining Hierarchical and Flat Synchronization Concepts 4.4.1 Entrainment of a Chain Network Topology 4.4.2 Entrainment of a Ring Network Topology 4.5 Heterogeneous Time Delays between Coupled PLLs 4.6 Phase Noise Analysis of Time Delay Coupled PLLs 4.6.1 Phase Noise for Two Mutually Coupled Nodes 4.6.2 The Impact of Coupling Directionality 4.6.3 Long Term Frequency Stability 4.6.4 Effect of Time Delay on Phase Noise 4.6.5 Network Topology Dependency on Phase Noise 5 Conclusion and Future Prospects Bibliography Own Publications – Periodicals Own Publications – Conference Proceedings Co-Authored Publications Other References List of Figures List of Tables Acknowledgement Curriculum Vitae / Diese Arbeit befasst sich mit der Analyse der Auswirkungen von Zeitverzögerungen auf die Synchronisation von gegenseitig gekoppelten Phasenregelschleifen (engl. phase-locked loop (PLL)) in einer flachen Hierarchie. Gegenseitige Synchronisation bezieht sich auf eine dezentrale Synchronisation, bei der es keine primäre oder sekundäre Einheit oder Steuerquelle gibt. Folglich ist es ein inhärent selbstorganisierendes System, in dem jede Einheit gleichberechtigt ist, was es zu einem demokratischen System macht. Für die Untersuchung wird ein dynamisches nichtlineares Zeitbereichsmodell verwendet, um gegenseitig verzögert gekoppelte Oszillatoren zu modellieren und die Vorhersagen dieses Modells anhand experimenteller Messungen zu bewerten. Dieses Zeitbereichsmodell basiert auf dem sogenannten Kuramoto-Modell, das ein Netzwerk gekoppelter Oszillatoren beschreibt. Um einen ersten Eindruck zu erhalten, wird zunächst dieses Kuramoto-Modell analysiert, um die Auswirkungen von Zeitverzögerungen zwischen den Oszillatoren zu verstehen. Das Zeitbereichsmodell basiert auf einer konventionellen PLL-Architektur, die modifiziert wurde, um eine gegenseitige Kopplung zu ermöglichen. Die Modifikationen beinhalten einen Schaltungsteil, der alle eingehenden Phasendifferenzen summiert und gewichtet. Die gemessenen Ergebnisse dieser Untersuchung stimmen insgesamt gut mit den theoretischen Vorhersagen des Zeitbereichsmodells überein. Die Analyse erlaubt es, die transiente Dynamik und die Mechanismen zu identifizieren, die zur gegenseitigen Synchronisation und zur Bildung synchronisierter Zustände durch selbstorganisierte Synchronisation führen. Die Ergebnisse zeigen, dass selbst bei Zeitverzögerungen, bei denen das Phänomen der Multistabilität synchronisierter Zustände auftritt, die gegenseitige Kopplung ihre Dynamik selbst organisieren kann, um sich zu synchronisieren. Die Untersuchung identifizierte eine kritische Zeitverzögerung, bei der kein stabiler synchronisierter Zustand erreicht werden kann. Die Arbeit analysiert auch die Dynamik und das Rauschen von synchronisierten Zuständen und stellt fest, dass die Dynamik in der Nähe eines synchronisierten Zustands aufgrund der gegenseitigen Kopplung korreliert ist, was zu einer Reduktion des Rauschens führt. Die Richtung der Kopplung und die Anzahl der Knoten im Netzwerk sowie die Netzwerktopologie beeinflussen die Korrelation des Rauschens. Eine Verbesserung des Phasenrauschens von bis zu 14.42 dBc/Hz bei einem Versatz von 100 kHz zum Träger und einer Verzögerung von 49.47 ns wurde durch eine globalen oder All-to-All-Kopplung mit vier Knoten erreicht. Des Weiteren wird der hybride Ansatz, die Kombination von hierarchischen und selbstorganisierenden Synchronisationsarchitekturen, untersucht. Die Arbeit präsentiert eine experimentelle Studie, um zu verstehen, wie dies ein Netzwerk von gegenseitig verzögert gekoppelten Oszillatoren beeinflusst und ob das Netzwerk von gegenseitig gekoppelten Knoten als sekundärer Oszillator abstrahiert werden kann. Dabei wird eine vom synchronisierten Zustand abhängige Domäne identifiziert, in der das wechselseitig gekoppelte Netzwerk durch einen externen Referenzoszillator erfolgreich synchronisiert werden kann. Insgesamt liefert diese wissenschaftliche Arbeit wertvolle Erkenntnisse über die Eigenschaften von gegenseitig verzögerungsgekoppelten PLLs und deren Synchronisation in einer flachen Hierarchie und trägt zum Verständnis, zum Entwurf und zur Optimierung von praktisch realisierten Netzwerken gegenseitig verzögerungsgekoppelter PLLs bei.:Abstract/Zusammenfassung Symbols and Abbreviations Previous Publications 1 Introduction 1.1 Classifications of Synchronization 1.2 A Historical Perspective on Mutual Synchronization 1.3 Extending the Understanding of Mutual Synchronization 1.4 Definitions and Methodologies 2 Model of Networks of Mutually Coupled PLLs 2.1 Coupled Oscillators – Kuramoto Model 2.1.1 Consequences of a Time Delay between Oscillators 2.1.2 Arbitrary Time Delays between Oscillators 2.2 Time-Domain Model of Delay-Coupled PLLs 2.2.1 Phase Detection 2.2.2 Loop-Filter 2.2.3 Voltage Controlled Oscillator 2.3 Prediction and Stability Analysis of Synchronized States 2.3.1 Assessing the Linear Stability of Synchronized States 2.3.2 Stability Consideration for Two Identical PLL Nodes 2.3.3 The Notion of Mode Locking 2.3.4 Effects of Heterogeneity on Synchronized States 2.4 Dynamics and Noise in Synchronized States 2.4.1 Gain and Phase Margin of a PLL Node 2.4.2 Phase Noise 2.5 Key Findings of the Theoretical Model 3 Design of Phase-Locked-Loops for Mutual Synchronization 3.1 PLL Nodes Dedicated for Mutual Synchronization 3.1.1 Phase Detection Circuitry 3.1.2 Adder Chain 3.2 Additional Circuitry for Implementing a Time Delay 4 Experimental Analysis of Mutually Time-Delayed Coupled PLLs 4.1 Synchronized States Including Oscillator Nonlinearity 4.2 Stability of Multistable Synchronized States 4.3 Critical Time Delay Between Two Coupled Nodes 4.4 Combining Hierarchical and Flat Synchronization Concepts 4.4.1 Entrainment of a Chain Network Topology 4.4.2 Entrainment of a Ring Network Topology 4.5 Heterogeneous Time Delays between Coupled PLLs 4.6 Phase Noise Analysis of Time Delay Coupled PLLs 4.6.1 Phase Noise for Two Mutually Coupled Nodes 4.6.2 The Impact of Coupling Directionality 4.6.3 Long Term Frequency Stability 4.6.4 Effect of Time Delay on Phase Noise 4.6.5 Network Topology Dependency on Phase Noise 5 Conclusion and Future Prospects Bibliography Own Publications – Periodicals Own Publications – Conference Proceedings Co-Authored Publications Other References List of Figures List of Tables Acknowledgement Curriculum Vitae

In the human visual system, one of the most prominent functions of the extensive feedback from the higher brain areas within and outside of the visual cortex is attentional modulation. The feedback helps the brain to concentrate its resources on visual features that are relevant for recognition, i. e. it iteratively selects certain aspects of the visual scene for refined processing by the lower areas until the inference process in the higher areas converges to a single hypothesis about this scene. In order to minimize a number of required selection-refinement iterations, one has to find a short sequence of maximally informative portions of the visual input. Since the feedback is not static, the selection process is adapted to a scene that should be recognized. To find a scene-specific subset of informative features, the adaptive selection process on every iteration utilizes results of previous processing in order to reduce the remaining uncertainty about the visual scene. This phenomenon inspired us to develop a computational algorithm solving a visual classification task that would incorporate such principle, adaptive feature selection. It is especially interesting because usually feature selection methods are not adaptive as they define a unique set of informative features for a task and use them for classifying all objects. However, an adaptive algorithm selects features that are the most informative for the particular input. Thus, the selection process should be driven by statistics of the environment concerning the current task and the object to be classified. Applied to a classification task, our adaptive feature selection algorithm favors features that maximally reduce the current class uncertainty, which is iteratively updated with values of the previously selected features that are observed on the testing sample. In information-theoretical terms, the selection criterion is the mutual information of a class variable and a feature-candidate conditioned on the already selected features, which take values observed on the current testing sample. Then, the main question investigated in this thesis is whether the proposed adaptive way of selecting features is advantageous over the conventional feature selection and in which situations. Further, we studied whether the proposed adaptive information-theoretical selection scheme, which is a computationally complex algorithm, is utilized by humans while they perform a visual classification task. For this, we constructed a psychophysical experiment where people had to select image parts that as they think are relevant for classification of these images. We present the analysis of behavioral data where we investigate whether human strategies of task-dependent selective attention can be explained by a simple ranker based on the mutual information, a more complex feature selection algorithm based on the conventional static mutual information and the proposed here adaptive feature selector that mimics a mechanism of the iterative hypothesis refinement. Hereby, the main contribution of this work is the adaptive feature selection criterion based on the conditional mutual information. Also it is shown that such adaptive selection strategy is indeed used by people while performing visual classification.:1. Introduction 2. Conventional feature selection 3. Adaptive feature selection 4. Experimental investigations of ACMIFS 5. Information-theoretical strategies of selective attention 6. Discussion Appendix Bibliography

Feature selection or variable selection is an important step in different machine learning tasks. In a traditional approach, users specify the amount of features, which shall be selected. Afterwards, algorithm select features by using scores like the Joint Mutual Information (JMI). If users do not know the exact amount of features to select, they need to evaluate the full learning chain for different feature counts in order to determine, which amount leads to the lowest training error. To overcome this drawback, we extend the JMI score and mitigate the flaw by introducing a stopping criterion to the selection algorithm that can be specified depending on the learning task. With this, we enable developers to carry out the feature selection task before the actual learning is done. We call our new score Historical Joint Mutual Information (HJMI). Additionally, we compare our new algorithm, using the novel HJMI score, against traditional algorithms, which use the JMI score. With this, we demonstrate that the HJMI-based algorithm is able to automatically select a reasonable amount of features: Our approach delivers results as good as traditional approaches and sometimes even outperforms them, as it is not limited to a certain step size for feature evaluation.

Time-of-Flight (ToF)-Kameras erzeugen Tiefenbilder (3D-Bilder), indem sie Infrarotlicht aussenden und die Zeit messen, bis die Reflexion des Lichtes wieder empfangen wird. Durch den Einsatz mehrerer ToF-Kameras können ihre vergleichsweise geringere Auflösungen überwunden, das Sichtfeld vergrößert und Verdeckungen reduziert werden. Der gleichzeitige Betrieb birgt jedoch die Möglichkeit von Störungen, die zu fehlerhaften Tiefenmessungen führen. Das Problem der gegenseitigen Störungen tritt nicht nur bei Mehrkamerasystemen auf, sondern auch wenn mehrere unabhängige ToF-Kameras eingesetzt werden. In dieser Arbeit wird eine neue optische Synchronisation vorgestellt, die keine zusätzliche Hardware oder Infrastruktur erfordert, um ein Zeitmultiplexverfahren (engl. Time-Division Multiple Access, TDMA) für die Anwendung mit ToF-Kameras zu nutzen, um so die Störungen zu vermeiden. Dies ermöglicht es einer Kamera, den Aufnahmeprozess anderer ToF-Kameras zu erkennen und ihre Aufnahmezeiten schnell zu synchronisieren, um störungsfrei zu arbeiten. Anstatt Kabel zur Synchronisation zu benötigen, wird nur die vorhandene Hardware genutzt, um eine optische Synchronisation zu erreichen. Dazu wird die Firmware der Kamera um das Synchronisationsverfahren erweitert. Die optische Synchronisation wurde konzipiert, implementiert und in einem Versuchsaufbau mit drei ToF-Kameras verifiziert. Die Messungen zeigen die Wirksamkeit der vorgeschlagenen optischen Synchronisation. Während der Experimente wurde die Bildrate durch das zusätzliche Synchronisationsverfahren lediglich um etwa 1 Prozent reduziert. / Time-of-Flight (ToF) cameras produce depth images (three-dimensional images) by measuring the time between the emission of infrared light and the reception of its reflection. A setup of multiple ToF cameras may be used to overcome their comparatively low resolution, increase the field of view, and reduce occlusion. However, the simultaneous operation of multiple ToF cameras introduces the possibility of interference resulting in erroneous depth measurements. The problem of interference is not only related to a collaborative multicamera setup but also to multiple ToF cameras operating independently. In this work, a new optical synchronization for ToF cameras is presented, requiring no additional hardware or infrastructure to utilize a time-division multiple access (TDMA) scheme to mitigate interference. It effectively enables a camera to sense the acquisition process of other ToF cameras and rapidly synchronizes its acquisition times to operate without interference. Instead of requiring cables to synchronize, only the existing hardware is utilized to enable an optical synchronization. To achieve this, the camera’s firmware is extended with the synchronization procedure. The optical synchronization has been conceptualized, implemented, and verified with an experimental setup deploying three ToF cameras. The measurements show the efficacy of the proposed optical synchronization. During the experiments, the frame rate was reduced by only about 1% due to the synchronization procedure.

Abstract Critical Infrastructures (CIs) are an integral part of our society and economy. Services like electricity supply or telecommunication services are expected to be available at all times and a service failure may have catastrophic consequences for society or economy. Current CI protection strategies are from a time when CIs or CI sectors could be operated more or less self-sufficient and interconnections among CIs or CI sectors, which may lead to cascading service failures to other CIs or CI sectors, where not as omnipresent as today. In this PhD thesis, a cross-sector CI model for on-line risk monitoring of CI services, called CI security model, is presented. The model allows to monitor a CI service risk and to notify services that depend on it of possible risks in order to reduce and mitigate possible cascading failures. The model estimates CI service risk by observing the CI service state as measured by base measurements (e.g. sensor or software states) within the CI service components and by observing the experienced service risk of CI services it depends on (CI service dependencies). CI service risk is estimated in a probabilistic way using a Bayesian network based approach. Furthermore, the model allows CI service risk prediction in the short-term, mid-term and long-term future, given a current CI service risk and it allows to model interdependencies (a CI service risk that loops back to the originating service via dependencies), a special case that is difficult to model using Bayesian networks. The representation of a CI as a CI security model requires analysis. In this PhD thesis, a CI analysis method based on the PROTOS-MATINE dependency analysis methodology is presented in order to analyse CIs and represent them as CI services, CI service dependencies and base measurements. Additional research presented in this PhD thesis is related to a study of assurance indicators able to perform an on-line evaluation of the correctness of risk estimates within a CI service, as well as for risk estimates received from dependencies. A tool that supports all steps of establishing a CI security model was implemented during this PhD research. The research on the CI security model and the assurance indicators was validated based on a case study and the initial results suggest its applicability to CI environments. / Tiivistelmä Tässä väitöskirjassa esitellään läpileikkausmalli kriittisten infrastruktuurien jatkuvaan käytön riskimallinnukseen. Tämän mallin avulla voidaan tiedottaa toisistaan riippuvaisia palveluita mahdollisista vaaroista, ja siten pysäyttää tai hidastaa toisiinsa vaikuttavat ja kumuloituvat vikaantumiset. Malli analysoi kriittisen infrastruktuurin palveluriskiä tutkimalla kriittisen infrastruktuuripalvelun tilan, joka on mitattu perusmittauksella (esimerkiksi anturi- tai ohjelmistotiloina) kriittisen infrastruktuurin palvelukomponenttien välillä ja tarkkailemalla koetun kriittisen infrastruktuurin palveluriskiä, joista palvelut riippuvat (kriittisen infrastruktuurin palveluriippuvuudet). Kriittisen infrastruktuurin palveluriski arvioidaan todennäköisyyden avulla käyttämällä Bayes-verkkoja. Lisäksi malli mahdollistaa tulevien riskien ennustamisen lyhyellä, keskipitkällä ja pitkällä aikavälillä, ja mahdollistaa niiden keskinäisten riippuvuuksien mallintamisen, joka on yleensä vaikea esittää Bayes-verkoissa. Kriittisen infrastruktuurin esittäminen kriittisen infrastruktuurin tietoturvamallina edellyttää analyysiä. Tässä väitöskirjassa esitellään kriittisen infrastruktuurin analyysimenetelmä, joka perustuu PROTOS-MATINE -riippuvuusanalyysimetodologiaan. Kriittiset infrastruktuurit esitetään kriittisen infrastruktuurin palveluina, palvelujen keskinäisinä riippuvuuksina ja perusmittauksina. Lisäksi tutkitaan varmuusindikaattoreita, joilla voidaan tutkia suoraan toiminnassa olevan kriittisen infrastruktuuripalvelun riskianalyysin oikeellisuutta, kuin myös riskiarvioita riippuvuuksista. Tutkimuksessa laadittiin työkalu, joka tukee kriittisen infrastruktuurin tietoturvamallin toteuttamisen kaikkia vaiheita. Kriittisen infrastruktuurin tietoturvamalli ja varmuusindikaattorien oikeellisuus vahvistettiin konseptitutkimuksella, ja alustavat tulokset osoittavat menetelmän toimivuuden. / Kurzfassung In dieser Doktorarbeit wird ein Sektorübergreifendes Modell für die kontinuierliche Risikoabschätzung von kritische Infrastrukturen im laufenden Betrieb vorgestellt. Das Modell erlaubt es, Dienstleistungen, die in Abhängigkeit einer anderen Dienstleistung stehen, über mögliche Gefahren zu informieren und damit die Gefahr des Übergriffs von Risiken in andere Teile zu stoppen oder zu minimieren. Mit dem Modell können Gefahren in einer Dienstleistung anhand der Überwachung von kontinuierlichen Messungen (zum Beispiel Sensoren oder Softwarestatus) sowie der Überwachung von Gefahren in Dienstleistungen, die eine Abhängigkeit darstellen, analysiert werden. Die Abschätzung von Gefahren erfolgt probabilistisch mittels eines Bayessches Netzwerks. Zusätzlich erlaubt dieses Modell die Voraussage von zukünftigen Risiken in der kurzfristigen, mittelfristigen und langfristigen Zukunft und es erlaubt die Modellierung von gegenseitigen Abhängigkeiten, die im Allgemeinen schwer mit Bayesschen Netzwerken darzustellen sind. Um eine kritische Infrastruktur als ein solches Modell darzustellen, muss eine Analyse der kritischen Infrastruktur durchgeführt werden. In dieser Doktorarbeit wird diese Analyse durch die PROTOS-MATINE Methode zur Analyse von Abhängigkeiten unterstützt. Zusätzlich zu dem vorgestellten Modell wird in dieser Doktorarbeit eine Studie über Indikatoren, die das Vertrauen in die Genauigkeit einer Risikoabschätzung evaluieren können, vorgestellt. Die Studie beschäftigt sich sowohl mit der Evaluierung von Risikoabschätzungen innerhalb von Dienstleistungen als auch mit der Evaluierung von Risikoabschätzungen, die von Dienstleistungen erhalten wurden, die eine Abhängigkeiten darstellen. Eine Software, die alle Aspekte der Erstellung des vorgestellten Modells unterstützt, wurde entwickelt. Sowohl das präsentierte Modell zur Abschätzung von Risiken in kritischen Infrastrukturen als auch die Indikatoren zur Uberprüfung der Risikoabschätzungen wurden anhand einer Machbarkeitsstudie validiert. Erste Ergebnisse suggerieren die Anwendbarkeit dieser Konzepte auf kritische Infrastrukturen.