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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Modern day technological advancements are moving at a rapid pace. In the field of Artificial Intelligence, algorithms are becoming ever faster and process larger amounts of data. These fast algorithms call for data structures that can store this processed data compactly. This premise also holds true in the AI subfield of planning. In the common planning approach of state space search, found states are memorized as to not unnecessarily revisit them. Research has put a big focus on improving the speed of state space searches which in turn leads to a lot of states being stored. A crucial bottleneck then occurs when memory runs out due to storing these large amounts of states. This is where this project, with its exploration of compact state set representations, comes into the picture. This project's focus is on exploring memory usage for planning by state space search. More specifically, the project investigates compact state set representations for an A* state space search's closed- and open lists. It was hypothesized that the closed list would be the larger of the two which is why a focus was put on testing compact representations of that state set. Results from this project confirm this hypothesis as it is shown that the closed list is the largest and most critical of the two (although the differences between the two become increasingly small for strong heuristics).  Four different state set representations were tested for use as closed lists in an A* algorithm: Level-Ordered Edge Sequences (LOES), compressed LOES (cLOES), Binary Decision Diagram (BDD) and an explicit representation. A primary focus was put on exploring the LOES data structure because of the limited amount of research done on the data structure. Explicit representation was used as the main point of comparison with it being a very commonly used standard in state space search. The results from this project show that LOES managed to lower memory usage significantly for large tasks when compared to the explicit representation. The lower memory usage did, however, come at the cost of speed with LOES being noticeably slower. Although less drastic, the same differences could be seen when comparing LOES to its compressed version, cLOES. Out of all the tested state set representations, cLOES was shown to be the most compact but also the slowest. Moreover, the results indicated that, even if most tasks didn't benefit from the additional compression provided by cLOES in comparison to normal LOES, the tasks that did, benefited a lot. Lastly, the BDD data structure gave more inconclusive results. The poor BDD results were seemingly caused by an unfit implementation for the closed list use case. The results did, however, suggest that BDD was faster but less compact than LOES for large tasks. The different closed list state set representations were also tested with four different heuristics: blind, max, CEGAR and Merge-and-Shrink heuristic. A takeaway from these tests was that stronger heuristics resulted in fewer states being stored in the open- and closed list. Moreover, the closed states made up a smaller portion of the total amount states for the stronger heuristics. This smaller number of stored closed states made, as a consequence, the differences between the tested state set representations less pronounced. For large tasks, however, the closed list did get big enough to experience the effect of the efficient closed list implementations. Conclusively, LOES and cLOES proved strong replacements to explicit representation. Especially in use cases where compactness is more critical than speed such as in embedded systems. Additionally, even though strong heuristics lessened the effect of efficient state set representations, there are still notable advantages to be found for big tasks where the closed list grows large enough.

<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>

[pt] Nesse artigo aplicamos a técnica de estimação dos nowcasts apresentada por Giannone, Reichlin e Small (2008), para o PIB e inflação brasileiros. Extraímos informações de um elevado número de variáveis e produzimos modelos capazes de informar contemporaneamente uma medida para as variáveis em questão. Em posse dessa leitura cotidiana, produzida por esses modelos, estimamos uma regra de Taylor diária para o Banco Central do Brasil (BCB), o que permitiu melhor identificar choques monetários e alterações na função de reação do BCB ao longo do tempo. Concluímos, primeiramente, que os modelos nowcasts apresentam acurácia comparável às previsões do relatório Focus do BCB. Segundo, 2 (duas) comparações históricas realizadas mostraram indícios que nossa proxy para choques monetários diários está relacionada às decisões explícitas de política monetária. Por fim, encontramos evidências que os modelos nowcasts puderam capturar grande parte da informação relevante para a determinação da taxa de juros de curto prazo, o que deveria estimular a aplicação de tais modelos nos processos decisórios públicos e privados. / [en] In this article we apply the two-steps nowcasting method, described in Giannone, Reichlin, and Small (2008), to build nowcast models for Brazilian GDP and inflation. Throught the application of this method, we could extract information from a large data-set and build models which could be used to produce a daily measurement of GDP and inflation. Using this measurement was possible to build a daily Taylor rule for the Brazilian Central Bank (BCB). This new application of nowcast models allowed us to extract a daily measurement of monetary shocks. Our study produced three main findings. First, the nowcast model showed an accuracy close to projections presented in the Focus survey. Second, we identified by historical comparison that the monetary shocks proxy, measured by the differences between the daily Taylor rule and the movements in the short-term interest rate, are related with unanticipated monetary policies decisions. Finally, nowcasts were able to capture a great part of relevant information to determine the short-term interest rate, which should stimulate the policymakers and financial markets members to apply those models.

The idea of performing power systems dynamic analysis in the power-angle domain has been hinted at by previous researchers, but this may be the first published document to develop detailed techniques by which entire power systems can be represented and solved in the power-angle domain. With the widespread deployment of phasor measurement units and frequency data recorders the industry is looking for more real-time analytical tools to turn real-time wide-area measurements into useful information. Applications based on power-angle domain analysis are simple enough that they may be used online. Power-angle domain analysis is similar to DC load-flow techniques in that a flat voltage profile is used and it is assumed that real power and voltage angle are completely decoupled from reactive power and voltage magnitude. The linearized equations for the dynamics of generators and loads are included in the model, which allows the electromechanical response to be solved using conventional circuit analysis techniques. The effect of generation trips, load switching, and line switching can be quickly approximated with nodal analysis or mesh analysis in the power-angle domain. The analysis techniques developed here are not intended to be as accurate as time-domain simulation, but they are simpler and fast enough to be put online, and they also provide a better analytical insight into the system. Power-angle domain analysis enables applications that are not readily available with conventional techniques, such as the estimation of electromechanical propagation delays based on system parameters, the formulation of electromechanical equivalents, modal analysis, stability analysis, and event location and identification based on a small number of angle or frequency measurements. Fault studies and contingency analysis are typically performed with detailed time-domain simulations, where the electromechanical response of the system is a function of every machine in the interconnection and the lines connecting them. All of this information is rarely known for the entire system for each operating condition; as a result, for many applications it may be more suitable to compute an approximation of the system response based on the current operating state of only the major lines and generators. Power-angle domain analysis is adept at performing such approximations. / Ph. D.

Abstract Undoubtedly, liquidity is one of the most popular topics of research among the academia for decades. However intuitively-clear it is, scholars and experts have always found it not only hard but vague to define and measure. Moreover, researches or methods concerning commonality in liquidity are proposed one after another. Most of these works attempt to document what lies beneath the commonality by offering industry-wide or market-wide explanations. Nevertheless, this paper adopts an exact multivariate model-based structural decomposition methodology developed by Casals, Jerez and Sotoca (2002) to analyze the co-movement in market liquidity measures in a totally different manner. Except for decomposing three well-known market liquidity measures, share volume, dollar volume and turnover rate, of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) into trend, cycle, seasonal and irregular components, we conduct advanced bivariate analysis to extract common components, visualize them, and make a comparison among them at last. Evidence suggests that not only do these three liquidity proxies highly co-move with one another, but dollar volume seems to co-move slightly closer with share volume than with turnover rate. In the end, where this phenomenon, co-movement in market liquidity measures, accrues from is another long story and needs some further work not covered in this study.

The performance modelling of large-scale systems using discrete-state approaches is fundamentally hampered by the well-known problem of state-space explosion, which causes exponential growth of the reachable state space as a function of the number of the components which constitute the model. Because they are mapped onto continuous-time Markov chains (CTMCs), models described in the stochastic process algebra PEPA are no exception. This thesis presents a deterministic continuous-state semantics of PEPA which employs ordinary differential equations (ODEs) as the underlying mathematics for the performance evaluation. This is suitable for models consisting of large numbers of replicated components, as the ODE problem size is insensitive to the actual population levels of the system under study. Furthermore, the ODE is given an interpretation as the fluid limit of a properly defined CTMC model when the initial population levels go to infinity. This framework allows the use of existing results which give error bounds to assess the quality of the differential approximation. The computation of performance indices such as throughput, utilisation, and average response time are interpreted deterministically as functions of the ODE solution and are related to corresponding reward structures in the Markovian setting. The differential interpretation of PEPA provides a framework that is conceptually analogous to established approximation methods in queueing networks based on meanvalue analysis, as both approaches aim at reducing the computational cost of the analysis by providing estimates for the expected values of the performance metrics of interest. The relationship between these two techniques is examined in more detail in a comparison between PEPA and the Layered Queueing Network (LQN) model. General patterns of translation of LQN elements into corresponding PEPA components are applied to a substantial case study of a distributed computer system. This model is analysed using stochastic simulation to gauge the soundness of the translation. Furthermore, it is subjected to a series of numerical tests to compare execution runtimes and accuracy of the PEPA differential analysis against the LQN mean-value approximation method. Finally, this thesis discusses the major elements concerning the development of a software toolkit, the PEPA Eclipse Plug-in, which offers a comprehensive modelling environment for PEPA, including modules for static analysis, explicit state-space exploration, numerical solution of the steady-state equilibrium of the Markov chain, stochastic simulation, the differential analysis approach herein presented, and a graphical framework for model editing and visualisation of performance evaluation results.

This thesis presents the design and implementation of control strategies for building energy management systems (BEMS). The controllers considered include the multi PI-loop controllers, state-space designs, constrained input and output MIMO adaptive controllers, fuzzy logic solutions and genetic algorithm techniques. The control performances of the designs developed using the various methods based on aspects such as regulation errors squared, energy consumptions and the settling periods are investigated for different designs. The aim of the control strategy is to regulate the room temperature and the humidity to required comfort levels. In this study the building system under study is a 3 input/ 2 output system subject to external disturbances/effects. The three inputs are heating, cooling and humidification, and the 2 outputs are room air temperature and relative humidity. The external disturbances consist of climatic effects and other stochastic influences. The study is carried out within a simulation environment using the mathematical model of the test room at Loughborough University and the designed control solutions are verified through experimental trials using the full-scale BMS facility at the University of Bradford.

Efficient algorithms exist for finding optimal strategies in extensive-form games. However human scale problems, such as poker, are typically so large that computation of these strategies remain infeasible with current technology. State space abstraction techniques allow us to derive a smaller abstract game, in which an optimal strategy can be computed and then used in the real game. This thesis introduces state of the art abstraction techniques. Most of these techniques do not deal with public information. We present a new automatic public state space abstraction technique. We examine the quality of this technique in the domain of poker. Our experimental results show that the new technique brings significant performance improvement. Powered by TCPDF (www.tcpdf.org)