abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities. / Dissertation/Thesis / Doctoral Dissertation Systems Engineering 2019

Mobile Crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of the smart devices to improve the sensing capacity and reduce the sensing costs in large regions. Due to the ubiquitous nature of MCS, smart devices require cyber protection against adversaries that are becoming smarter with the objective of clogging the resources and spreading misinformation in such a non-dedicated sensing environment. In an MCS setting, one of the various adversary types has the primary goal of keeping participant devices occupied by submitting fake/illegitimate sensing tasks so as to clog the participant resources such as the battery, sensing, storage, and computing. With this in mind, this thesis proposes a systematical study of fake task injection in MCS, including modeling, detection, and mitigation of such resource clogging attacks. We introduce modeling of fake task attacks in MCS intending to clog the server and drain battery energy from mobile devices. We creatively grant mobility to the tasks for more extensive coverage of potential participants and propose two take movement patterns, namely Zone-free Movement (ZFM) model and Zone-limited Movement (ZLM) model. Based on the attack model and task movement patterns, we design task features and create structured simulation settings that can be modified to adapt different research scenarios and research purposes. Since the development of a secure sensing campaign highly depends on the existence of a realistic adversarial model. With this in mind, we apply the self-organizing feature map (SOFM) to maximize the number of impacted participants and recruits according to the user movement pattern of these cities. Our simulation results verify the magnified effect of SOFM-based fake task injection comparing with randomly selected attack regions in terms of more affected recruits and participants, and increased energy consumption in the recruited devices due to the illegitimate task submission. For the sake of a secure MCS platform, we introduce Machine Learning (ML) methods into the MCS server to detect and eliminate the fake tasks, making sure the tasks arrived at the user side are legitimate tasks. In our work, two machine learning algorithms, Random Forest and Gradient Boosting are adopted to train the system to predict the legitimacy of a task, and Gradient Boosting is proven to be a more promising algorithm. We have validated the feasibility of ML in differentiating the legitimacy of tasks in terms of precision, recall, and F1 score. By comparing the energy-consuming, effected recruits, and impacted candidates with and without ML, we convince the efficiency of applying ML to mitigate the effect of fake task injection.

Im Bereich der medizinischen Geräte haben eingebettete Systeme verstärkt Einzug gehalten. Nicht nur in Operationssälen oder Intensivstationen, auch im Bereich der Prothesensteuerung spielt moderne Computertechnik in zunehmendem Maße eine Rolle, auch und insbesondere im Bereich der Prothesensteuerung durch elektronisch vorverarbeitete Nervensignale. Die zur Signalverarbeitung eingesetzte, vortrainierte selbst-organisierende Karte stößt jedoch auf das Problem, sich den verändernden Gegebenheiten in den Nervensignalen des Patienten nicht anpassen zu können. In dieser Arbeit wird die Möglichkeit untersucht, die Steuerung der Handprothese mit einer Nachlernfunktion auszustatten, um während des Einsatzes der Prothese auf die Veränderungen der Nervensignale des Patienten reagieren zu können. Da diese Veränderungen höchst individuell verlaufen, werden Parameter eingeführt, mit denen das Nachlernverfahren an die Gegebenheiten des Patienten angepasst werden kann. Verschiedene denkbare Lernstrategien werden untersucht und hinsichtlich ihrer Effizienz und ihrer Aktualität bewertet. Um die Verwendbarkeit der Implementierung sicherzustellen, muss darauf geachtet werden, dass der entstehende SystemC-Code keine Elemente des nicht synthetisierbaren Subsets enthält. Zusätzlich wird die Synthetisierbarkeit mit dem Agility-Compiler untersucht.

The meridional temperature gradient between middle and high latitudes is decreasing due to Arctic amplification, which enhances the warming in the Arctic region. This change in temperature is also influencing the circulation and the horizontal energy fluxes between the mid latitudes and the Arctic, which itself might influence the Arctic additionally. The horizontal energy flux, to our best knowledge, has never been analyzed using the up-to-date method called self-organizing map (SOM). The SOM is a simple unsupervised neural network that is used to extract patterns of high-dimensional data and presents the patterns in a two dimensional lattice, where similar (more different) patterns are closer together (farther apart) within the lattice. An advantage of using the SOM is that there are no underlying linear assumptions like in other methods that characterize the circulation, such as the Arctic Oscillation or the North Atlantic Oscillation index. The SOM has been used in this work to extract and analyze horizontal heat flux patterns from reanalysis data and climate model data. Using the SOM method, it was possible to find distinct horizontal heat flux patterns into the Arctic, that have been combined into heat flux pathways. The SOM made it possible to characterize the pathways' change in occurrence frequency throughout the last thirty years and the change between present-day climate model simulations and climate projections with increased greenhouse gas concentrations. Using reanalysis data, three distinct patterns have been extracted, which all show different features. They are named according to the main pathway the horizontal heat flux takes to reach the Arctic: the Atlantic pathway, the Pacific pathway, and the continental pathway. For the reanalysis data, it is shown that the Atlantic pathway, which is connected with positive temperature anomalies in the central Arctic, has become more frequent during the last three decades, while the Pacific pathway, that is connected to negative temperature anomalies around Svalbard, has become less frequent. This suggests that the circulation, which is connected to the temperature in the Arctic, is changing. The trends for the occurrence frequencies of the SOM horizontal heat flux pathways have, to our best knowledge, never been analyzed prior to this work. With respect to climate model results, the three distinct patterns were also identified in climate simulations of the second half of the twentieth century and climate projections of the second half of the twenty-first century from eight models. This demonstrates that these three pathways are an inherent part of the atmosphere. In comparison with the reanalysis data, the climate models show much stronger occurrence frequencies for the continental pathway. The reanalysis data of the continental pathway does not show such high occurrence frequencies. However, the multi model mean shows a clear decrease in these occurrence frequencies of the continental pathway between the present-day climate simulation and the climate projection with increased greenhouse gas concentrations. The continental pathway is mostly connected to strong zonal fluxes while there are only small meridional transports over Siberia or North America. This suggests that the fluxes become more meridional with an enhanced warming and thus increase the heat flux into the Arctic, which might influence the surface air temperature.:Bibliographische Beschreibung Bibliographic Description Acronyms 1. Introduction: Arctic Amplification, Circulation and Transport 1.1. Arctic Amplification 1.2. The (AC)3 project 1.3. Overview of General Circulation in Mid and High Latitudes 1.3.1. Drivers of the general circulation 1.3.2. Circulation impacts on high and mid latitudes 1.3.3. Atmospheric energy transport into the Arctic 1.4. Overview of the Thesis 2. The Self-organizing Map 2.1. Mathematical Description 2.2. SOM Parameters and their Effect on Clustering Meteorological Data 2.2.1. Map size 2.2.2. Neighborhood function 2.2.3. Iterations 2.2.4. Learning rate 2.2.5. Summary of the effect of learning parameters 2.3. Limits of SOM 2.4. Application of SOM in Atmospheric Sciences 2.5. Comparison with the K-Means Clustering Algorithm 2.6. A Practical Guide to SOM 3. Clustering of Atmospheric Energy Transport within ERA-Interim 3.1. Data and Method 3.1.1. ERA-Interim data 3.1.2. Analysis method 3.2. Results 3.2.1. Heat transport SOM 3.2.2. Temperature anomaly composites related to transport pathways 3.2.3. Mean meridional heat transport 3.2.4. Trend of transport pathways 3.2.5. Two-meter temperature trends 3.3. Discussion 3.4. Summary of ERA-Interim Analysis 4. Comparison of Flux Pathways in CMIP5 Model Analysis 4.1. Methods and Data 4.1.1. CMIP5 model data 4.1.2. Analysis using the SOM method 4.2. Results 4.2.1. Historical patterns 4.2.2. RCP8.5 patterns 4.2.3. Mean pathway occurrence frequencies 4.2.4. Pathway occurrence frequency trends during the historical and future time intervals 4.3. Discussion of CMIP5 Analysis 5. Summary and Conclusion of the Horizontal Energy Flux SOM Analysis References A. Appendix: ERA-Interim Self-Organizing Map Analysis B. Appendix: CMIP5 Self-Organizing Map Results Acknowledgments Curriculum Vitae Affirmation / Der meridionale Temperaturgradient zwischen mittleren und hohen Breiten nimmt aufgrund der arktischen Verstärkung ab. Diese Temperaturänderung beeinflusst auch die Zirkulation und die horizontalen Energieflüsse zwischen den mittleren Breiten und der Arktis, was die Arktis selbst zusätzlich beeinflussen könnte. Der horizontale Energietransport wurde, unserem bestem Wissen nach, nie mit der aktuellen Methode namens Self-Organizing Map (SOM) analysiert. Die SOM ist ein einfaches unüberwachtes neuronales Netzwerk, das zum Extrahieren von Mustern hoch dimensionaler Daten verwendet wird und die Muster in einem zweidimensionalen Gitter darstellt, in dem ähnliche (unterschiedliche) Muster innerhalb des Gitters näher beieinander (weiter voneinander entfernt) liegen. Ein Vorteil der SOM besteht darin, dass keine linearen Annahmen wie bei anderen Methoden vorliegen, die die Zirkulation charakterisieren, wie z. B. die Arktische Oszillation oder der Nordatlantische Oszillationsindex. Die SOM wurde im Rahmen dieser Arbeit verwendet, um horizontale Wärmetransportmuster aus Reanalysedaten und Klimamodelldaten zu extrahieren und zu analysieren. Mit der SOM-Methode konnten unterschiedliche horizontale Muster des Wärmetransports in die Arktis identifiziert werden, welche wiederum zu Pfaden zusammengefasst wurden. Die SOM ermöglichte es, die Veränderung der Auftrittshäufigkeit der Pfade in den letzten dreißig Jahren und die Veränderung der Muster zwischen einer Simulation des heutigen Zustandes und einer Klimaprojektion mit erhöhten Treibhausgaskonzentrationen zu charakterisieren. Unter Verwendung von Reanalysedaten konnten drei unterschiedliche Pfade extrahiert werden, die alle unterschiedliche Merkmale aufweisen. Sie wurden nach dem jeweiligen Hauptpfad benannt, den der horizontale Wärmetransport vollzieht, um in die Arktis zu gelangen: der Atlantikpfad, der Pazifikpfad und der Kontinentalpfad. Für die Reanalysedaten konnte gezeigt werden, dass die Auftretenshäufigkeit des Atlantikpfads, der mit positiven Temperaturanomalien in der Zentralarktis verbunden ist, in den letzten drei Jahrzehnten gestiegen ist. Demgegenüber ist die Auftretenshäufigkeit des pazifischen Pfads, der mit negativen Temperaturanomalien um Spitzbergen verbunden ist, in den letzten drei Jahrzehnten gesunken. Dies deutet darauf hin, dass sich die Zirkulation, die mit der Temperatur in der Arktis verbunden ist, ändert. Die Trends für die Auftrittshäufigkeiten der horizontalen SOM-Wärmetransportpfade wurden, nach bestem Wissen, vor dieser Arbeit noch nie analysiert. Auswertungen basierend auf acht Klimamodellen haben die drei unterschiedlichen Muster sowohl in Klimasimulationen für die zweite Hälfte des zwanzigsten Jahrhunderts, als auch in Klimaprojektionen der zweiten Hälfte des einundzwanzigsten Jahrhunderts gefunden. Dies zeigt, dass diese drei Pfade der Atmosphäre inhärent sind. Im Vergleich zu den Reanalysedaten zeigen die Klimamodelle viel stärkere Auftrittshäufigkeiten für den Kontinentalpfad. Die Reanalysedaten des Kontinentalpfads weisen keine hohen Auftrittshäufigkeiten auf. Der Multi-Modell-Mittelwert zeigt jedoch eine deutliche Abnahme dieser Auftrittshäufigkeiten des Kontinentalpfads zwischen der Simulation des heutigen Zustands und der Projektion mit erhöhten Treibhausgaskonzentrationen. Der Kontinentalpfad ist meist mit starken zonalen Transporten verbunden, während nur kleine meridionale Transporte über Sibirien oder Nordamerika erfolgen. Dies deutet darauf hin, dass mit zunehmender Erwärmung die Flüsse meridionaler werden sowie den Wärmetransport in die Arktis erhöhen und somit die Lufttemperatur in Bodennähe beeinflussen können.:Bibliographische Beschreibung Bibliographic Description Acronyms 1. Introduction: Arctic Amplification, Circulation and Transport 1.1. Arctic Amplification 1.2. The (AC)3 project 1.3. Overview of General Circulation in Mid and High Latitudes 1.3.1. Drivers of the general circulation 1.3.2. Circulation impacts on high and mid latitudes 1.3.3. Atmospheric energy transport into the Arctic 1.4. Overview of the Thesis 2. The Self-organizing Map 2.1. Mathematical Description 2.2. SOM Parameters and their Effect on Clustering Meteorological Data 2.2.1. Map size 2.2.2. Neighborhood function 2.2.3. Iterations 2.2.4. Learning rate 2.2.5. Summary of the effect of learning parameters 2.3. Limits of SOM 2.4. Application of SOM in Atmospheric Sciences 2.5. Comparison with the K-Means Clustering Algorithm 2.6. A Practical Guide to SOM 3. Clustering of Atmospheric Energy Transport within ERA-Interim 3.1. Data and Method 3.1.1. ERA-Interim data 3.1.2. Analysis method 3.2. Results 3.2.1. Heat transport SOM 3.2.2. Temperature anomaly composites related to transport pathways 3.2.3. Mean meridional heat transport 3.2.4. Trend of transport pathways 3.2.5. Two-meter temperature trends 3.3. Discussion 3.4. Summary of ERA-Interim Analysis 4. Comparison of Flux Pathways in CMIP5 Model Analysis 4.1. Methods and Data 4.1.1. CMIP5 model data 4.1.2. Analysis using the SOM method 4.2. Results 4.2.1. Historical patterns 4.2.2. RCP8.5 patterns 4.2.3. Mean pathway occurrence frequencies 4.2.4. Pathway occurrence frequency trends during the historical and future time intervals 4.3. Discussion of CMIP5 Analysis 5. Summary and Conclusion of the Horizontal Energy Flux SOM Analysis References A. Appendix: ERA-Interim Self-Organizing Map Analysis B. Appendix: CMIP5 Self-Organizing Map Results Acknowledgments Curriculum Vitae Affirmation

Modern high-throughput technologies such as microarrays, next generation sequencing and mass spectrometry provide huge amounts of data per measurement and challenge traditional analyses. New strategies of data processing, visualization and functional analysis are inevitable. This thesis presents an approach which applies a machine learning technique known as self organizing maps (SOMs). SOMs enable the parallel sample- and feature-centered view of molecular phenotypes combined with strong visualization and second-level analysis capabilities. We developed a comprehensive analysis and visualization pipeline based on SOMs. The unsupervised SOM mapping projects the initially high number of features, such as gene expression profiles, to meta-feature clusters of similar and hence potentially co-regulated single features. This reduction of dimension is attained by the re-weighting of primary information and does not entail a loss of primary information in contrast to simple filtering approaches. The meta-data provided by the SOM algorithm is visualized in terms of intuitive mosaic portraits. Sample-specific and common properties shared between samples emerge as a handful of localized spots in the portraits collecting groups of co-regulated and co-expressed meta-features. This characteristic color patterns reflect the data landscape of each sample and promote immediate identification of (meta-)features of interest. It will be demonstrated that SOM portraits transform large and heterogeneous sets of molecular biological data into an atlas of sample-specific texture maps which can be directly compared in terms of similarities and dissimilarities. Spot-clusters of correlated meta-features can be extracted from the SOM portraits in a subsequent step of aggregation. This spot-clustering effectively enables reduction of the dimensionality of the data in two subsequent steps towards a handful of signature modules in an unsupervised fashion. Furthermore we demonstrate that analysis techniques provide enhanced resolution if applied to the meta-features. The improved discrimination power of meta-features in downstream analyses such as hierarchical clustering, independent component analysis or pairwise correlation analysis is ascribed to essentially two facts: Firstly, the set of meta-features better represents the diversity of patterns and modes inherent in the data and secondly, it also possesses the better signal-to-noise characteristics as a comparable collection of single features. Additionally to the pattern-driven feature selection in the SOM portraits, we apply statistical measures to detect significantly differential features between sample classes. Implementation of scoring measurements supplements the basal SOM algorithm. Further, two variants of functional enrichment analyses are introduced which link sample specific patterns of the meta-feature landscape with biological knowledge and support functional interpretation of the data based on the ‘guilt by association’ principle. Finally, case studies selected from different ‘OMIC’ realms are presented in this thesis. In particular, molecular phenotype data derived from expression microarrays (mRNA, miRNA), sequencing (DNA methylation, histone modification patterns) or mass spectrometry (proteome), and also genotype data (SNP-microarrays) is analyzed. It is shown that the SOM analysis pipeline implies strong application capabilities and covers a broad range of potential purposes ranging from time series and treatment-vs.-control experiments to discrimination of samples according to genotypic, phenotypic or taxonomic classifications.

Agrégation de l'enseignement supérieur, Orientation sciences / Thèse d'agrégation / info:eu-repo/semantics/nonPublished

Purpose – This thesis is to identifying key success factors for autonomous agile teams at the small-scale. Furthermore the purpose leads to an improvement of their working process. Method – This study is based on a comparative case study of a company with offices located in Jönköping and Ängelholm. The data is gathered by semi-structured qualitative interviews and by a survey with qualitative and quantitative answers. Findings – The results from RQ1 shows that there exists a difference between established and non-established autonomous agile teams in order to achieve success for projects. ​The findings from RQ2 present six themes that each represent key success factors in an autonomous agile team-based IT project at the small scale. The themes are ​Customer Oriented, Architecture, Individual Development, Team Setup, Entirety and Privilege. The findings from RQ3 resulted in six different elements (​Freedom and Flexibility, Trust and Responsibility, Clear directions, Environment, The work gives value ​ and ​Shared knowledge) ​ that make an individual team member satisfied in an autonomous agile team. Limitations – A fair limitation of this study is too few people to answered the survey. More respondents would have increased the trustworthiness of the results. Keywords – Autonomy, Self-organizing, Success factor, Agile development, Small-scale, Job satisfaction

No description available.

No description available.

Classification tasks are an integral part of science, industry, medicine, and business; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this dissertation, motivated by learning styles in human brains, ANN’s shortcomings are assuaged and its learning power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. These benefits are in two directions: enhancing ANN’s learning power, and improving decision-making. First, the proposed method, named Self-Organizing Error-Driven (SOED) Artificial Neural Network (ANN), shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five famous benchmark datasets. Second, the hybridization creates space for inclusion of decision-making goals at the level of ANN’s learning. This gives the classifier the opportunity to handle the inconclusiveness of the data smarter and in the direction of decision-making goals. Through three case studies, naming 1) churn decision analytics, 2) breast cancer diagnosis, and 3) quality control decision making through thermal monitoring of additive manufacturing processes, this novel and cost-sensitive aspect of SOED has been explored and lead to much quantified improvement in decision-making.