Emergency rapid mapping with drones: models and solution approaches for offline and online mission planning

Die Verfügbarkeit von unbemannten Luftfahrzeugen (unmanned aerial vehicles oder UAVs) und die Fortschritte in der Entwicklung leichtgewichtiger Sensorik eröffnen neue Möglichkeiten für den Einsatz von Fernerkundungstechnologien zur Schnellerkundung in Großschadenslagen. Hier ermöglichen sie es beispielsweise nach Großbränden, Einsatzkräften in kurzer Zeit ein erstes Lagebild zur Verfügung zu stellen. Die begrenzte Flugdauer der UAVs wie auch der Bedarf der Einsatzkräfte nach einer schnellen Ersteinschätzung bedeuten jedoch, dass die betroffenen Gebiete nur stichprobenartig überprüft werden können. In Kombination mit Interpolationsverfahren ermöglichen diese Stichproben anschließend eine Abschätzung der Verteilung von Gefahrstoffen. Die vorliegende Arbeit befasst sich mit dem Problem der Planung von UAV-Missionen, die den Informationsgewinn im Notfalleinsatz maximieren. Das Problem wird dabei sowohl in der Offline-Variante, die Missionen vor Abflug bestimmt, als auch in der Online-Variante, bei der die Pläne während des Fluges der UAVs aktualisiert werden, untersucht. Das übergreifende Ziel ist die Konzeption effizienter Modelle und Verfahren, die Informationen über die räumliche Korrelation im beobachteten Gebiet nutzen, um in zeitkritischen Situationen Lösungen von hoher Vorhersagegüte zu bestimmen. In der Offline-Planung wird das generalized correlated team orienteering problem eingeführt und eine zweistufige Heuristik zur schnellen Bestimmung explorativer UAV-Missionen vorgeschlagen. In einer umfangreichen Studie wird die Leistungsfähigkeit und Konkurrenzfähigkeit der Heuristik hinsichtlich Rechenzeit und Lösungsqualität bestätigt. Anhand von in dieser Arbeit neu eingeführten Benchmarkinstanzen wird der höhere Informationsgewinn der vorgeschlagenen Modelle im Vergleich zu verwandten Konzepten aufgezeigt. Im Bereich der Online-Planung wird die Kombination von lernenden Verfahren zur Modellierung der Schadstoffe mit Planungsverfahren, die dieses Wissen nutzen, um Missionen zu verbessern, untersucht. Hierzu wird eine breite Spanne von Lösungsverfahren aus unterschiedlichen Disziplinen klassifiziert und um neue effiziente Modellierungsvarianten für die Schnellerkundung ergänzt. Die Untersuchung im Rahmen einer ereignisdiskreten Simulation zeigt, dass vergleichsweise einfache Approximationen räumlicher Zusammenhänge in sehr kurzer Zeit Lösungen hoher Qualität ermöglichen. Darüber hinaus wird die höhere Robustheit genauerer, aber aufwändigerer Modelle und Lösungskonzepte demonstriert

Data Acquisition Applications

Data acquisition systems have numerous applications. This book has a total of 13 chapters and is divided into three sections: Industrial applications, Medical applications and Scientific experiments. The chapters are written by experts from around the world, while the targeted audience for this book includes professionals who are designers or researchers in the field of data acquisition systems. Faculty members and graduate students could also benefit from the book

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization

Statistical Learning and Stochastic Process for Robust Predictive Control of Vehicle Suspension Systems

Predictive controllers play an important role in today's industry because of their capability of verifying optimum control signals for nonlinear systems in a real-time fashion. Due to their mathematical properties, such controllers are best suited for control problems with constraints. Also, these interesting controllers can be equipped with different types of optimization and learning modules. The main goal of this thesis is to explore the potential of predictive controllers for a challenging automotive problem, known as active vehicle suspension control. In this context, it is intended to explore both modeling and optimization modules using different statistical methodologies ranging from statistical learning to random process control. Among the variants of predictive controllers, learning-based model predictive controller (LBMPC) is becoming more and more interesting to the researchers of control society due to its structural flexibility and optimal performance. The current investigation will contribute to the improvement of LBMPC by adopting different statistical learning strategies and forecasting methods to improve the efficiency and robustness of learning performed in LBMPC. Also, advanced probabilistic tools such as reinforcement learning, absorbing state stochastic process, graphical modelling, and bootstrapping are used to quantify different sources of uncertainty which can affect the performance of the LBMPC when it is used for vehicle suspension control. Moreover, a comparative study is conducted using gradient-based as well as deterministic and stochastic direct search optimization algorithms for calculating the optimal control commands. By combining the well-established control and statistical theories, a novel variant of LBMPC is developed which not only affords stability and robustness, but also surpasses a wide range of conventional controllers for the vehicle suspension control problem. The findings of the current investigation can be interesting to the researchers of automotive industry (in particular those interested in automotive control), as several open issues regarding the potential of statistical tools for improving the performance of controllers for vehicle suspension problem are addressed

Uncertainty modeling : fundamental concepts and models

This book series represents a commendable effort in compiling the latest developments on three important Engineering subjects: discrete modeling, inverse methods, and uncertainty structural integrity. Although academic publications on these subjects are plenty, this book series may be the first time that these modern topics are compiled together, grouped in volumes, and made available for the community. The application of numerical or analytical techniques to model complex Engineering problems, fed by experimental data, usually translated in the form of stochastic information collected from the problem in hand, is much closer to real-world situations than the conventional solution of PDEs. Moreover, inverse problems are becoming almost as common as direct problems, given the need in the industry to maintain current processes working efficiently, as well as to create new solutions based on the immense amount of information available digitally these days. On top of all this, deterministic analysis is slowly giving space to statistically driven structural analysis, delivering upper and lower bound solutions which help immensely the analyst in the decisionmaking process. All these trends have been topics of investigation for decades, and in recent years the application of these methods in the industry proves that they have achieved the necessary maturity to be definitely incorporated into the roster of modern Engineering tools. The present book series fulfills its role by collecting and organizing these topics, found otherwise scattered in the literature and not always accessible to industry. Moreover, many of the chapters compiled in these books present ongoing research topics conducted by capable fellows from academia and research institutes. They contain novel contributions to several investigation fields and constitute therefore a useful source of bibliographical reference and results repository. The Latin American Journal of Solids and Structures (LAJSS) is honored in supporting the publication of this book series, for it contributes academically and carries technologically significant content in the field of structural mechanics

Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters