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In this dissertation, advanced nonlinear control strategies and nonlinear minimum-variance observation are combined, in order to improve the estimation and/or tracking quality within control and fault detection tasks, for several types of systems from the fields of electromobility and conventional drivetrain technology that have some potential for sustainability or performance improvements. The application-specific innovations in terms of nonlinear Kalman filter methods are: (1) Improved state of charge estimation for Lithium-ion battery cells, powered by a novel self-adaptive EKF that uses a high-order polynomial curve fit as a decomposition of the uncertain nonlinear output equation with intentionally redundant bases, and with a reduced number of polynomial parameters that are adapted online by the EKF itself. (2) Online estimation of the time delay between two periodic signals of roughly the same shape that have pronounced uncorrelated noise, based on a fractional-order approximation of the transcendent transfer function of the time delay which is used as a model in a novel kind of EKF. (3) Using two (E)KFs (one for the linear subsystem and one for the nonlinear subsystem of a new kind of multi-stage piezo-hydraulic actuator) in a cascaded loop structure in order to reduce the computation load of the estimation, by appropriate 'interfacing' between the two observers (using one shared system model equation, among other aspects). - The innovations in terms of nonlinear control methods are powered by observation, as well: (1) Sliding mode velocity control of a DC drive that is subject to nonlinear friction and unknown load torques, enhanced by an equivalent control law, and with a new intelligent switching gain adaptation scheme (for reduced control chattering and, thus, less energy consumption and actuator wear), which is powered by Taylor-linearized model predictive control, which in turn requires observer-based disturbance compensation (by a KF with a double-integrator disturbance model) for model-matching purposes in order to function correctly. (2) Direct speed control of permanent-magnet three-phase synchronous motors that have a high power-to-volume ratio, based on sliding mode control in a rotating d,q coordinate system, with a new equivalent control method that exploits both system inputs and with a secondary sliding surface to ensure compliance with the current-trajectory of maximum efficiency for the required torque, and which works without measurement of the rotor angle (thanks to a new kind of EKF that estimates all states in the stationary α,β coordinate system, as well as the disturbance/load torque and its derivative). In all instances, improvements (compared to methods existing in the literature) in terms of control and estimation performance have been achieved and confirmed using simulation studies or real experiments.
Analysis of User Behavior
(2020)
Online behaviors analysis consists of extracting patterns from server-logs. The works presented here were carried out within the "mBook" project which aimed to develop indicators of the quantity and quality of the learning process of pupils from their usage of an eponymous electronic textbook for History. In this thesis, the research group investigates several models that adopt different points of view on the data. The studied methods are either well established in the field of pattern mining or transferred from other fields of machine learning and data mining. The authors improve the performance of archetypal analysis in large dimensions and apply it to unveil correlations between visibility time of particular objects in the e-textbook and pupils' motivation. They present next two models based on mixtures of Markov chains. The first extracts users' weekly browsing patterns. The second is designed to process essions at a fine resolution, which is sine qua non to reveal the significance of scrolling behaviors. The authors also propose a new paradigm for online behaviors analysis that interprets sessions as trajectories within the page-graph. In this respect, they establish a general framework for the study of similarity measures between spatio-temporal trajectories, for which the study of sessions is a particular case. Finally, they construct two centroid-based clustering methods using neural networks and thus lay the foundations for unsupervised behaviors analysis using neural networks.
Comparable collaborations between farmers and institutions with communal catering have been less in research focus so far. Within the region of Lüneburg, an example for such a regional-organic cooperation is not known yet. Thus,this work represents the starting point to fill the research gap within the field of sustainable food systems in urban living labs as part of the research project GLOCULL (Globally and Locally-sustainable food-water-energy innovation in Urban Living Labs). The work aims at building up such a regional-organic food cooperation between a local farmer and a kindergarten community catering servicebased on scientific insights and practical persons’ knowledge.
The wide accessibility of the Internet and web-based programs enable an increased volume of online interventions for mental health treatment. In contrast to traditional face-to-face therapy, online treatment has the potential to overcome some of the barriers such as improved geographical accessibility, individual time planning, and reduced costs. The availability of clients' treatment data fuels research to analyze the collected data to obtain a better understanding of the relationship among symptoms in mental disorders and derive outcome and symptom predictions. This research leads to predictive models that can be integrated into the online treatment process to assist clinicians and clients. This dissertation discusses different aspects of the development of predictive modeling in online treatment: Categorization of predictive models, data analyses for predictive purposes, and model evaluation. Specifically, the categorization of predictive models and barriers against the uptake of mental health treatment are discussed in the first part of this dissertation. Data analysis and predictive modeling are emphasized in the second part by presenting methods for inference and prediction of mood as well as the prediction of treatment outcome and costs. Prediction of future and current mood can be beneficial in many aspects. Inference of users' mood levels based on unobtrusive measures or diary data can provide crucial information for intervention scheduling. Prediction of future mood can be used to assess clients' response to the treatment and expected treatment outcome. Prediction of the expected treatment costs and outcomes for different treatment types allows simultaneous optimization of these objectives and to increase the cost-effectiveness of the treatment. In the third part, a systematic predictive model evaluation incorporating simulation analyses is demonstrated and a method for model parameter estimation for computationally limited devices is presented. This dissertation aims to overcome the current challenges of predictive model development and its use in online treatment. The development of predictive models for varies data collected in online treatment is demonstrated and how these models can be applied in practice. The derived results contribute to computer science and mental health research with client individual data analysis, the development ofpredictive models, and their statistical evaluation.
The aim of this paper is to determine how a carbon footprint label for grocery products can be designed to facilitate a sustainable consumption behaviour. Therefore, a mixed-method approach was used consisting out of a review of relevant literature and an explorative quantitative survey with n=158 participants. It was found that consumers generally have a positive attitude towards carbon labelling, but they lack understanding of the term, its underlying concepts and the emissions caused by grocery products. In regard to the design criteria of a carbon label, labels with a coloured scale are preferred most by consumers. Also, the mechanisms of consumer behaviour imply that not all parts of the behaviour are visible and controllable for individuals themselves. The concluding concept proposal summarises important criteria of a carbon labelling system that has the goal to educate consumers and facilitate a lower carbon consumption behaviour, such as a simple visual design, the use of a colour scale, a design enabling a comparison, the provision of a link to further information, the public enforcement of the system and overall uniformity.
The dissertation contains four journal articles which are embedded within a framework manuscript that interconnects the individual articles and provides relevant background information. The dissertation's overall objective is to provide a multilayered and critical in-depth engagement with the timely phenomenon of integrated reporting (IR), a new reporting concept that is envisaged to revolutionize firms' present reporting infrastructure. While extant corporate reports (e.g., annual financial- and CSR report) often are criticized for being disconnected and to suffer from a lack of coherence, IR intends to provide all information that is material to a firm's short-, medium- und long-term value creation within one single, succinct document. To contribute to a set of previously defined relevant research gaps in literature, the dissertation makes use of a combined empirical-quantitative and explorative-qualitative research design. The first article entitled investigates a set of different IR-, corporate governance and financial accounting-specific factors that are expected to determine European and South African firms' materiality disclosure quality. To this purpose, an original, hand-collected materiality disclosure score was developed. The second article explores IR perceptions of SME managers that have not embarked on IR, but are potential candidates to do so in future. Based on a review of extant literature, the article develops a theoretical framework to subsequently discuss motives for and barriers to IR adoption. The critical discussion contributes to the academic debate on incentives for and barriers to voluntary IR adoption. The third article investigates whether voluntary IR adoption among European firms is associated with lower cost of public debt. While earlier studies suggest that IR leads to lower information asymmetries, increases analyst forecasts, and decreases cost of equity, corresponding evidence for the debt market is largely missing. Subsequent analyses test as to whether such an association is even more pronounced by a firm's environmental, social and governance (ESG) performance or its belonging to an environmentally sensitive industry. The fourth article uses an experimental design to investigate nonprofessional investors' reactions to an IR assurance. To this purpose, two separate experiments with two different groups of nonprofessional investors were carried out: one with Masters students and one with managers of large corporations. Results help to answer the question as to whether an IR assurance as well as its determinants, namely the assurance provider and the assurance level, affect nonprofessional investors' financial decision-making. In the second step, subsequent in-depth interviews reveal an IR assurance-critical attitude among managers, who draw upon their practical experience with assurance engagements.
It is understood among research and policy makers that addressing unsustainable individual consumption patterns is key for the vision of sustainable development. Education for Sustainable Consumption (ESC) is attributed a pivotal role for this purpose, aiming to improve the capacity of individuals to connect to and act upon knowledge, values and skills in order to respond successfully and purposefully to the demands of sustainable consumption. Yet despite political, scientific, and educational efforts and increasing awareness in the general population, little has been achieved to substantially change behavioral patterns so far. As part of the explanation for this shortcoming, it has been argued that current ESC practices have neglected the personal dimension of sustainable consumption, especially the affective-motivational processes underlying unsustainable consumption patterns. Against this background, this cumulative thesis is guided by the question how personal competencies for sustainable consumption can be defined, observed, and developed within educational settings. Special attention is given to mindfulness practices, describing the practice of cultivating a deliberate, unbiased and openhearted awareness of perceptible experience in the present moment. Drawing upon an explorative, qualitative research methodology, the thesis looks at three different mindfulness-based interventions aiming to stimulate competencies for sustainable consumption, reaching out to a total number of 321 participants (employees and university students). In this thesis, the author suggests to define personal competencies for sustainable consumption as abilities, proficiencies, or skills related to inner states and processes that can be considered necessary or sufficient to engage with sustainable consumption (SC). These include ethics, self-awareness, emotional resilience, selfcare, access to and cultivation of personal resources, access to and cultivation of ethical qualities, and mindsets for sustainability. The thesis holds that the observation of personal competencies benefits from a combination of different methodological and methodical angles. When working with self-reports as empirical data, a pluralistic qualitative methods approach can help overcoming shortcomings that are specifically related to individual methods while increasing the self-reflexivity of the research. Moreover, it is suggested to let learners analyze their own personal statements in groups, applying scientific methods. The products of the group analyses represent data based on an inter-subjectively shared perspective of learners that goes beyond self-estimation of personal competencies. In terms of developing personal competencies for SC, it can be concluded that mindfulness practice alone is not sufficient to build personal competencies for SC. While it can stimulate generic personal competencies, individuals do not necessarily apply these competencies within the domain of their consumption. Nevertheless, mindfulness practice can play an important role in ESC, insofar as it lays the inner foundation to engage with sustainability-related issues. More precisely, it allows learners to experience the relevance of their inner states and processes and the influence they have on actual behaviors, leading to a level of selfawareness that would not be accessible solely through discursive-intellectual means. Furthermore, participants experience mindfulness practice as a way to develop ethical qualities and access psychological resources, entailing stronger emotional resilience and improved well-being. In order to unleash its full potential for stimulating personal competencies for SC, however, the findings of the thesis suggest that mindfulness practice should be (a) complemented with methodically controlled self-inquiry and (b) related to a specific behavioral change. In this vein, self-inquiry-based and self-experience-based learning – two pedagogical approaches developed during the period of research for this thesis – turned out to be promising pedagogies for educational settings striving to stimulate the development of personal competencies for SC.
Thermal energy storage systems have a high potential for a sustainable energy management. Low temperature thermochemical energy stores based on gas-solid reactions represent appealing alternative options to sensible and latent storage technologies, in particular for heating and cooling purposes. They convert heat energy provided from renewable energy and waste heat sources into chemical energy and can effectively contribute to load balancing and CO2 mitigation. At present, several obstacles are associated with the implementation in full-scale reactors. Notably, the mass and heat transfer must be optimized. Limitations in the heat transport and diffusions resistances are mainly related to physical stability issues, adsorption/desorption hysteresis and volume expansion and can impact the reversibility of gas-solid reactions. The aim of this thesis was to examine the energy storage and cooling efficiency of CaCl2, MgCl2, and their physical salt mixtures as adsorbents paired with water, ethanol and methanol as adsorbates for utilization in a closed, low level energy store. Two-component composite adsorbents were engineered using a representative set of different host matrices (activated carbon, binderless zeolite NaX, expanded natural graphite, expanded vermiculite, natural clinoptiolite, and silica gel). The energetic characteristics and sorption behavior of the parent salts and modified thermochemical materials were analyzed employing TGA/DSC, TG-MS, Raman spectroscopy, and XRD. Successive discharging/charging cycles were conducted to determine the cycle stability of the storage materials. The overall performance was strongly dependent on the material combination. Increase in the partial pressure of the adsorbate accelerated the overall adsorbate uptake. From energetic perspectives the CaCl2-H2O system exhibited higher energy storage densities than the CaCl2 and MgCl2 alcoholates studied. The latter were prone to irreversible decomposition. Ethyl chloride formation was observed for MgCl2 at room and elevated temperatures. TG-MS measurements confirmed the evolution of alkyl chloride from MgCl2 ethanolates and methanolates upon heating. However, CaCl2 and its ethanolates and methanolates proved reversible and cyclable in the temperature range between 25°C and 500°C. All composite adsorbents achieved intermediate energy storage densities between the salt and the matrix. The use of carbonaceous matrices had a heat and mass transfer promoting effect on the reaction system CaCl2-H2O. Expanded graphite affected only moderately the adsorption/desorption of methanol onto CaCl2. CaCl2 dispersed inside zeolite 13X showed excellent adsorption kinetics towards ethanol. However, main drawback of the molecular sieve used as supporting structure was the apparent high charging temperature. Despite variations in the reactivity over thermal cycling caused by structural deterioration, composite adsorbents based on CaCl2 have a good potential as thermochemical energy storage materials for heating and cooling applications. Further research is required so that the storage media tested can meet all necessary technical requirements.
Panic disorder is a common anxiety disorder, which is associated with high subjective burden as well as a high cost for the health economy. According to the National Treatment Guideline S3, cognitive behavior therapy is recommended as the most effective psychological treatment. However, many people in need do not have access to cognitive behavior therapy. Internet-based interventions have proven to be an effective way to provide access to evidence-based treatment to those affected. For anxiety disorders, such as panic disorder and agoraphobia, a good effectiveness of internet-based interventions has been proven in numerous international studies. However, the internet has changed over the last few years: mobile technologies have considerable potential to further improve the adherence and effectiveness of internet-based interventions. Against this background, the authors developed the hybrid online training "GET.ON Panic". In this training, an app has been integrated into a browser-based online training. The app consists of a mobile diary for self-monitoring as well as a mobile exposure-guide that supports participants in self-exposure exercises in their everyday lives.In an initial exploratory feasibility study, qualitative interview data and quantitative measurements were collected in a pre-post design of 10 participants. Usage, user friendliness, user satisfaction and acceptance of the app were generally considered high. The use of interoceptive exposure exercises and daily summaries of anxiety and mood were the most widely performed and rated the best, while in vivo exposure exercises and the monitoring of acute panic symptoms were found to be difficult.In the efficacy study, 92 participants with mild to moderate panic symptoms were randomized into two parallel groups. After eight weeks, the intervention group showed a significant improvement in the severity of panic symptoms compared to the waiting control group. Using the intention-to-treat approach, a covariance analysis with baseline values as a covariate yielded a mean effect of Cohen's d=0.66 in reducing the panic symptoms in favor of the intervention group. This effect increased to d=0.89 after three months and stayed at d=0.81 at the 6-month measurement point. Response and remission rates were also significantly higher in the intervention group. This positive effect was also shown for secondary outcomes such as depressive symptoms and quality of life. A correlation between app usage and clinical outcomes could not be found. This work was the first to demonstrate that a hybrid online training based on cognitive behavior therapy is effective in reducing panic symptoms as well as panic disorder. In addition, this work contributes to a deeper understanding of the potential of mobile technologies in the field of e-mental health.
Establishing the identity of asylum seekers in the absence of credible documents represents a significant challenge for governments. To support decision-making processes in identity determination and verification procedures, Germany’s Federal Office for Migration and Refugees introduced three digital identification systems under the “Integrated Identity Management - plausibility, data quality and security aspects (IDMS)” programme. Because these algorithmic systems are deployed in highly political settings affecting vulnerable populations on the move, this research investigates how the Federal Office legitimises the policy and use of IDM-S that indicate a new direction of governance driven by so-called “innovative technologies”. In this context, legitimacy - considered a core virtue of just, democratic institutions - is understood as a justificatory concept seen in conjunction with (good) governance and the right to privacy as guaranteed under Article 17 of the International Covenant on Civil and Political Rights. The data justice framework is applied to structure the evaluation of state practices. In addition, the qualitative content analysis is used to find patterns in publicly available documents. Expert interviews were carried out to include experiences of affected individuals and to verify identified information provided by the government. The analysis revealed that efforts to legitimise IDM-S included four patterns: referring to the rule of law and national security concerns, non-disclosing delegitimising information and limiting accountability, emphasising performance efficiency and the systems’ high level of innovation, implying objective operations by means of a mathematical-technical approach. The results underscore profound discrepancies between justifications and state practices, outlining severe privacy violations as well as the lack of compliance to qualitative values in governance that pertain to participation, transparency, accountability, impartiality and scientific soundness of state operations.