The main goal of this Diploma thesis is to describe an approach for modeling run-off triangles of nonlife insurance (calculation of IBNR reserve) based on state space models and apply the method to the selected run-off triangles. In difference from (Atherino a kol., 2010) the KFAS package in R software is used for modeling purposes in the numerical study at the end of the thesis. One provides a preview of various possibilities of data and model adjustment applied to the same run-off triangles in order to asses added value of these steps (logartihmic transformation of input data, interventions for outliers etc.). A special attention is devoted to lognormal modification of the basic state space model. An integral part of the numerical study in the thesis is a residual diagnostic of models and simulation approach to IBNR reserves. 1

The Ph.D. thesis is focused on using the fuzzy logic for control of a parallel manipulator based on a Stewart platform. The proposed mechanism makes possible to simulate the physiological movements of the human body and observe degradation processes of the cord implants. Parallel manipulators such as a Stewart platform represent a completely parallel kinematic mechanism that has major differences from typical serial link robots. However, they have some drawbacks of relatively small workspace and difficult forward kinematic problems. Generally, forward kinematic of a parallel manipulators is very complicated and difficult to solve. This thesis presents a simple and efficient approach to design simulation model of forward kinematic based on Takagi-Sugeno type fuzzy inference system. The control system of the parallel manipulator id based on state-space and fuzzy logic controllers. The proposed fuzzy controller uses a Sugeno type fuzzy inference system (FIS) which is derived from discrete position state-space controller with an input integrator. The controller design method is based on anfis (adaptive neuro-fuzzy inference system) training routine. It utilizes a combination of the least-squares method and the backpropagation gradient descent method for training FIS membership function parameters to emulate a given training data set. The proposed fuzzy logic controllers are used for the control of a linear actuator. The capabilities of the designed control system are shown on verification experiment.

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In this Thesis a Unified Discrete State-Space Model for power converters in CCM is presented. Two main approaches to arriving at the discrete model are used. The first approach involves an impulse function approximation of the duty cycle modulations of the converter switches , and this approach results in a small signal discrete model. The Second approach is direct and does not involve any approximation of the modulations , this approach yields both a large signal nonlinear discrete model and a linear small signal model. Harmonic analysis of the converter states at steady-state is done for steady-state waveform acquisition , which increases the accuracy of the model especially for finding the control to inductor current frequency response. Finally the Discrete model is verified for the Half-Bridge DC/DC topology for its three main control schemes (Asymmetric , Symmetric , DCS). A GUI platform in MATLAB is presented as a wrapper that utilizes the models and analysis presented in this thesis.

The response of beams resting on viscoelastically damped foundation under moving SDoF oscillators is scrutinized through a novel state-space formulation, in which a number of internal variables is introduced with the aim of representing the frequency-dependent behaviour of the viscoelastic foundation. A suitable single-step scheme is provided for the numerical integration of the equations of motion, and the Dimensional Analysis is applied in order to define the dimensionless combinations of the design parameters that rule the responses of beam and moving oscillator. The effects of boundary conditions, span length and number of modes of the beam, along with those of the mechanical properties of oscillator and foundation, are investigated in a new dimensionless form, and some interesting trends are highlighted. The inaccuracy associated with the use of effective values of stiffness and damping for the viscoelastic foundation, as usual in the present state-of-practice, is also quantified.

There is a pressing need for creative new data analysis methods whichcan sift through scientific simulation data and produce meaningfulresults. The types of analyses and the amount of data handled by currentmethods are still quite restricted, and new methods could providescientists with a large productivity boost. New methods could be simpleto develop in big data processing systems such as Apache Spark, which isdesigned to process many input files in parallel while treating themlogically as one large dataset. This distributed model, combined withthe large number of analysis libraries created for the platform, makesSpark ideal for processing simulation output.Unfortunately, the filesystem becomes a major bottleneck in any workflowthat uses Spark in such a fashion. Faster transports are notintrinsically supported by Spark, and its interface almost denies thepossibility of maintainable third-party extensions. By leveraging thesemantics of Scala and Spark's recent scheduler upgrades, we forceco-location of Spark executors with simulation processes and enable fastlocal inter-process communication through shared memory. This provides apath for bulk data transfer into the Java Virtual Machine, removing thecurrent Spark ingestion bottleneck.Besides showing that our system makes this transfer feasible, we alsodemonstrate a proof-of-concept system integrating traditional HPC codeswith bleeding-edge analytics libraries. This provides scientists withguidance on how to apply our libraries to gain a new and powerful toolfor developing new analysis techniques in large scientific simulationpipelines.

Wastewater treatment plants (WWTPs) face increasingly stringent effluent quality constraints as a result of rising environmental concerns. Efficient operation of the secondary clarification process is essential to be able to meet these strict regulations. Treatment plants can benefit greatly from making better use of available resources through improved automation and implementing more process systems engineering techniques to enhance plant performance. As such, the primary objective of this research is to utilize data-driven modeling techniques to obtain a representative model of a simplified secondary clarification unit in a WWTP. First, a deterministic subspace-based identification approach is used to estimate a linear state-space model of the secondary clarification process that can accurately predict process dynamics, with the ultimate objective of motivating the use of the subspace model in a model predictive control (MPC) framework for closed-loop control of the clarification process. To this end, a low-order subspace model which relates a set of typical measured outputs from a secondary clarifier to a set of typical inputs is identified and subsequently validated on simulated data obtained via Hydromantis's WWTP simulation software, GPS-X. Results illustrate that the subspace model is able to approximate the nonlinear process behaviour well and can effectively predict the dynamic output trajectory for various candidate input profiles, thus establishing its candidacy for use in MPC. Subsequently, a framework for forecasting the occurrence of sludge bulking--and consequently clarification failure--based on an engineered interaction variable that aims to capture the relationship between key input variables is proposed. Partial least squares discriminant analysis (PLS-DA) is used to discriminate between process conditions associated with clarification failure versus effective clarification. Preliminary results show that PLS-DA models augmented with the interaction variable demonstrate improved predictions and higher classification accuracy. / Thesis / Master of Applied Science (MASc)

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<p>  </p> <p>Dyadic play interactions are a cornerstone of early development and difficulty engaging in sustained synchronous interactions are linked to later difficulties with language and joint attention. For children at elevated risk for autism spectrum disorder (ASD), it is unclear if early difficulties in synchronous exchanges could inform later diagnoses. As part of a prospective monitoring study, infant siblings of children with ASD (high-risk group) or typical development (low-risk group), and their mothers completed a standardized play task. Play interactions for infants were evaluated to: (1) assess if early difficulties with social responsiveness or synchrony proceed ASD diagnoses within the first year; (2) explore whether repertoires of observed synchronous behaviors distinguish ASD-risk; and (3) examine whether the unfolding rates of synchrony and responsiveness over continuous time highlight ASD-risk differences. </p> <p><br></p> <p>By 12 months, distinct mean-level differences in synchrony and responsiveness by risk status were observed. Higher synchrony and responsiveness totals were also positively associated with infants later language and cognitive scores and negatively associated with ASD symptom severity (Chapter 2). Although, dyads utilized mostly comparable repertoires of observed synchronous and responsive behaviors, regardless of group membership (Chapter 3). And lastly, the overall rates of unfolding synchrony and responsiveness were fairly stable throughout the interaction. However, distinct patterns by ASD-risk and developmental outcomes were evident (Chapter 4). Ultimately, the encompassed studies did not consistently find robust ASD-specific differences. However, these studies did demonstrate the applicability of advanced methodologies to provide relevant contextual/dyadic elements (beyond the field’s norm of mean-level totals), particularly for infants with non-autism developmental concerns. Future research should build upon these studies to assess synchrony and responsiveness growth curves that extend beyond 12 months of age, as well as utilize behavioral coding approaches that systematically capture both synchronous and asynchronous exchanges.</p>