The smart grid is forecasted to be the future of the grid by integrating the traditional grid with information and communication technology. However, the use of this technology has not only brought its benefits but also the vulnerability to cyber-attacks. False data injection (FDI) attacks are a new category of attacks targeting the smart grid that manipulates the state estimation process to trigger a chain of incorrect control decisions leading to severe impacts. This research proposes the use of cognitive dynamic systems (CDS) to address the cyber-security issue and improve state estimation. CDS is a powerful research tool inspired by certain features of the brain that can be used to study complex systems. As two of its special features, Cognitive Control (CC) is concerned with control in the absence of uncertainty, Cognitive Risk Control (CRC) uses the concept of predictive adaptation to bring risk under control in the presence of unexpected uncertainty. The primary research objective of this thesis is to apply the CDS for the SG with emphasis on state estimation and cyber-security. The main objective of CC is to improve the state estimation process while CRC is concerned with mitigating cyber-attacks. Simulation results show that the proposed methods have robust performance for both state estimation and cyber-attack mitigation under various challenging scenarios. This thesis contributes to the body of knowledge by achieving the following objectives: proposes the first theoretical work that integrates the CDS with the DC model of the SG for control and cyber-attack detection; demonstrates the first experimental work that brings a new concept of CRC for cyber-attack mitigation for the DC state estimator; introduces a new CDS architecture adapted for the AC model of the SG for state estimation and cyber-attack mitigation which builds upon all the research efforts made previously. / Thesis / Doctor of Philosophy (PhD) / The smart grid is forecasted to be the future of the grid by integrating the traditional grid with information and communication technology. However, the use of this technology has not only brought its benefits but also the vulnerability to cyber-attacks. False data injection attacks is a new category of attacks targeting the smart grid that can cause serious damage by manipulating the state estimation process and starting a chain of incorrect control decisions. The cognitive dynamic system is a powerful research tool inspired by the brain that can be used to study real time cyber physical systems. The key goal of this thesis is to apply cognitive dynamic systems to the smart grid to improve the state estimation process, detect cyber-attacks and mitigate their effects. Simulation results show that the proposed methods have robust performance in both state estimation and cyber-attack mitigation under various challenging scenarios.

The professional development of a career counselling intern on a satellite campus at a distance education institution was facilitated with the use of a heutagogic learning strategy. The heutagogic learning process was recorded by reflective writing based on Kolb's experiential learning model. This research is a disquisition of the reflective dataset. The research is an autoethnographic case study in the constructionist paradigm with a creative analysis process. During the research process features about the benefits of heutagogic learning in the professional development of the career counselling intern were critically constructed. The findings of the research are captured in memescapes showing mindset changes and mental transformations on patterns which describe the theory-praxis gap, diversity, wellness, the macro-ethic. The recommendations include the hope that these findings will feed through to inform future career counselling internships in the IOP field. / Industrial & Organisational Psychology / MCom (Industrial and Organisational Psychology)

The professional development of a career counselling intern on a satellite campus at a distance education institution was facilitated with the use of a heutagogic learning strategy. The heutagogic learning process was recorded by reflective writing based on Kolb's experiential learning model. This research is a disquisition of the reflective dataset. The research is an autoethnographic case study in the constructionist paradigm with a creative analysis process. During the research process features about the benefits of heutagogic learning in the professional development of the career counselling intern were critically constructed. The findings of the research are captured in memescapes showing mindset changes and mental transformations on patterns which describe the theory-praxis gap, diversity, wellness, the macro-ethic. The recommendations include the hope that these findings will feed through to inform future career counselling internships in the IOP field. / Industrial and Organisational Psychology / M. Com. (Industrial and Organisational Psychology)

Deux thématiques importantes des technologies de la santé: la pratique médicale fondée sur des preuves probantes et l’évaluation des interventions en médecine sont fondées sur une approche positiviste et une conception mécaniste des organisations en santé. Dans ce mémoire, nous soulevons l’hypothèse selon laquelle les théories de la complexité et la systémique permettent une conceptualisation différente de ces deux aspects de la gouvernance clinique d’une unité de Soins Intensifs Chirurgicaux (SIC), qui est considérée comme un système adaptatif dynamique non linéaire qui nécessite une approche systémique de la cognition. L’étude de cas d’une unité de SIC, permet de démontrer par de nombreux exemples et des analyses de micro-situations, toutes les caractéristiques de la complexité des patients critiques et instables et de la structure organisationnelle des SIC. Après une critique épistémologique de l’Evidence-Based Medicine nous proposons une pratique fondée sur des raisonnements cliniques alliant l’abduction, l’herméneutique et la systémique aux SIC. En nous inspirant des travaux de Karl Weick, nous suggérons aussi de repenser l’évaluation des modes d’interventions cliniques en s’inspirant de la notion d’organisation de haute fiabilité pour mettre en place les conditions nécessaires à l’amélioration des pratiques aux SIC. / In Health Technology Assessment and Management, Evidence-Based Medicine and many tools available for clinical assessment reflect a positivistic and mechanistic approach to Health Care Organizations and scientific knowledge. We argue that the Complexity Theories and the Systemic decision-making process give a different insight on those two aspects of Clinical Governance in a Surgical Intensive Care Unit (SICU). In a case-study, we describe the nature of critically ill and unstable patients and the organizational structure of a SICU in a university based hospital. We demonstrate all the characteristics of complexity in that setting, through the use of many examples and micro-situational analysis. After an epistemological critical appraisal of EBM, we suggest that if a SICU is conceptualized as a dynamic non-linear adaptative system, then clinical knowledge and scientific thought processes must include hermeneutical, systemic and abductive types of reasoning. Finally, we draw upon Karl Weick’s work and suggest that a SICU must be considered as a High Reliability Organization in order to aim for improving patient care and create better conditions for quality and performance in this complex environment.

Master's thesis is dealing with desgin of a measuring unit incorporating inertial sensors, used for analysis in canoe sprint. Data from a three-axis accelerometer and a three-axis gyroscope were combined using an extended Kalman filter, yielding speed, roll, pitch and yaw of the boat and stroke rate. Calculated values were verified by a GPS. Furthermore, parameters describing dynamic behaviour of the system were identified, allowing an inclusion of dynamic quantities like force and power into the analysis.

<p>The Ensemble Kalman filter (EnKF) has achieved great successes in data assimilation in atmospheric and oceanic sciences, but its failure in convergence to the right filtering distribution precludes its use for uncertainty quantification. Other existing methods, such as particle filter or sequential importance sampler, do not scale well to the dimension of the system and the sample size of the datasets. In this dissertation, we address these difficulties in a coherent way.</p><p><br></p><p> </p><p>In the first part of the dissertation, we reformulate the EnKF under the framework of Langevin dynamics, which leads to a new particle filtering algorithm, the so-called Langevinized EnKF (LEnKF). The LEnKF algorithm inherits the forecast-analysis procedure from the EnKF and the use of mini-batch data from the stochastic gradient Langevin-type algorithms, which make it scalable with respect to both the dimension and sample size. We prove that the LEnKF converges to the right filtering distribution in Wasserstein distance under the big data scenario that the dynamic system consists of a large number of stages and has a large number of samples observed at each stage, and thus it can be used for uncertainty quantification. We reformulate the Bayesian inverse problem as a dynamic state estimation problem based on the techniques of subsampling and Langevin diffusion process. We illustrate the performance of the LEnKF using a variety of examples, including the Lorenz-96 model, high-dimensional variable selection, Bayesian deep learning, and Long Short-Term Memory (LSTM) network learning with dynamic data.</p><p><br></p><p> </p><p>In the second part of the dissertation, we focus on two extensions of the LEnKF algorithm. Like the EnKF, the LEnKF algorithm was developed for Gaussian dynamic systems containing no unknown parameters. We propose the so-called stochastic approximation- LEnKF (SA-LEnKF) for simultaneously estimating the states and parameters of dynamic systems, where the parameters are estimated on the fly based on the state variables simulated by the LEnKF under the framework of stochastic approximation. Under mild conditions, we prove the consistency of resulting parameter estimator and the ergodicity of the SA-LEnKF. For non-Gaussian dynamic systems, we extend the LEnKF algorithm (Extended LEnKF) by introducing a latent Gaussian measurement variable to dynamic systems. Those two extensions inherit the scalability of the LEnKF algorithm with respect to the dimension and sample size. The numerical results indicate that they outperform other existing methods in both states/parameters estimation and uncertainty quantification.</p>

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