Orientador: Mauro Hugo Mathias / Banca: José Elias Tomazini / Banca: Francisco Carlos Parquet Bizarria / Resumo: Na indústria moderna o monitoramento da condição de operação de máquinas rotativas é essencial para se determinar o surgimento de falhas em mancais de rolamentos. Este trabalho apresenta uma técnica de análise adotada para a identificação de falhas em mancais de rolamento em seus estágios iniciais, utilizando procedimentos de análise de sinais no domínio do tempo e da freqüência, com especial atenção para a técnica do HFRT (High Frequency Resonance Technique), também conhecida como Técnica do Envelope. Este método de análise de sinais foi escolhido em razão de ser uma ferramenta apropriada para identificar falhas em mancais de rolamentos na sua fase inicial. A teoria das técnicas foi discutida e os passos para a implementação computacional foram apresentados. As rotinas foram implementadas através da linguagem de programação MATLAB e um sinal simulado representativo de um sinal coletado de um mancal de rolamento com defeito pontual na pista externa foi desenvolvido para verificar a eficácia dos métodos implementados. Os experimentos foram desenvolvidos utilizando-se uma bancada de testes aplicada para testar mancais de rolamento com defeitos pontuais produzidos em laboratório. A aquisição dos dados foi desenvolvida com instrumentação comercial. Os resultados obtidos mostraram ser efetivos para identificar falhas em rolamentos para os dados simulados e dados experimentais. / Abstract: In the modern industries, the condition monitoring of the rotational machinery operation is important to evidence the beginning of the fails in bearings. This work presents a technique of analysis applied to identify fails in bearing during the initial phases, using techniques of signal analysis in time and frequency domain with special attention for the High Frequency Resonance Technique, also called envelope technique. This method for signal analysis was chosen because is an appropriated tool to identify fails in bearings during initial phases. The theory for the techniques was discussed and the steps for the computational implementation were showed. The routines were implemented through MATLAB programming language and it was prepared a representative signal of a bearing with a single point defect in the outer race in order to verify the capability of the method implemented in the routine. The experiments were performed using a experimental test rig applied to test bearings with single point defects performed in laboratory. The data acquisition were performed with commercial instrumentation. The results obtained shown to be effective to identify fails in bearings for both numerically simulated data and experimental data. / Mestre

How does the living body impact, and perhaps even help constitute, the thinking, reasoning, feeling agent? This is the guiding question that the following work seeks to answer. The subtitle of this project is emotion, prediction and embodied cognition for good reason: these are the three closely related themes that tie together the various chapters of the following thesis. The central claim is that a better understanding of the nature of emotion offers valuable insight for understanding the nature of the so called 'predictive mind', including a powerful new way to think about the mind as embodied Recently a new perspective has arguably taken the pole position in both philosophy of mind and the cognitive sciences when it comes to discussing the nature of mind. This framework takes the brain to be a probabilistic prediction engine. Such engines, so the framework proposes, are dedicated to the task of minimizing the disparity between how they expect the world to be and how the world actually is. Part of the power of the framework is the elegant suggestion that much of what we take to be central to human intelligence - perception, action, emotion, learning and language - can be understood within the framework of prediction and error reduction. In what follows I will refer to this general approach to understanding the mind and brain as 'predictive processing'. While the predictive processing framework is in many ways revolutionary, there is a tendency for researchers interested in this topic to assume a very traditional 'neurocentric' stance concerning the mind. I argue that this neurocentric stance is completely optional, and that a focus on emotional processing provides good reasons to think that the predictive mind is also a deeply embodied mind. The result is a way of understanding the predictive brain that allows the body and the surrounding environment to make a robust constitutive contribution to the predictive process. While it's true that predictive models can get us a long way in making sense of what drives the neural-economy, I will argue that a complete picture of human intelligence requires us to also explore the many ways that a predictive brain is embodied in a living body and embedded in the social-cultural world in which it was born and lives.

Pan, Yunpeng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 98-107). / Abstract also in Chinese. / Abstract --- p.i / p.iii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Model Predictive Control --- p.2 / Chapter 1.2 --- Neural Networks --- p.3 / Chapter 1.3 --- Existing studies --- p.6 / Chapter 1.4 --- Thesis structure --- p.7 / Chapter 2 --- Two Recurrent Neural Networks Approaches to Linear Model Predictive Control --- p.9 / Chapter 2.1 --- Problem Formulation --- p.9 / Chapter 2.1.1 --- Quadratic Programming Formulation --- p.10 / Chapter 2.1.2 --- Linear Programming Formulation --- p.13 / Chapter 2.2 --- Neural Network Approaches --- p.15 / Chapter 2.2.1 --- Neural Network Model 1 --- p.15 / Chapter 2.2.2 --- Neural Network Model 2 --- p.16 / Chapter 2.2.3 --- Control Scheme --- p.17 / Chapter 2.3 --- Simulation Results --- p.18 / Chapter 3 --- Model Predictive Control for Nonlinear Affine Systems Based on the Simplified Dual Neural Network --- p.22 / Chapter 3.1 --- Problem Formulation --- p.22 / Chapter 3.2 --- A Neural Network Approach --- p.25 / Chapter 3.2.1 --- The Simplified Dual Network --- p.26 / Chapter 3.2.2 --- RNN-based MPC Scheme --- p.28 / Chapter 3.3 --- Simulation Results --- p.28 / Chapter 3.3.1 --- Example 1 --- p.28 / Chapter 3.3.2 --- Example 2 --- p.29 / Chapter 3.3.3 --- Example 3 --- p.33 / Chapter 4 --- Nonlinear Model Predictive Control Using a Recurrent Neural Network --- p.36 / Chapter 4.1 --- Problem Formulation --- p.36 / Chapter 4.2 --- A Recurrent Neural Network Approach --- p.40 / Chapter 4.2.1 --- Neural Network Model --- p.40 / Chapter 4.2.2 --- Learning Algorithm --- p.41 / Chapter 4.2.3 --- Control Scheme --- p.41 / Chapter 4.3 --- Application to Mobile Robot Tracking --- p.42 / Chapter 4.3.1 --- Example 1 --- p.44 / Chapter 4.3/2 --- Example 2 --- p.44 / Chapter 4.3.3 --- Example 3 --- p.46 / Chapter 4.3.4 --- Example 4 --- p.48 / Chapter 5 --- Model Predictive Control of Unknown Nonlinear Dynamic Sys- tems Based on Recurrent Neural Networks --- p.50 / Chapter 5.1 --- MPC System Description --- p.51 / Chapter 5.1.1 --- Model Predictive Control --- p.51 / Chapter 5.1.2 --- Dynamical System Identification --- p.52 / Chapter 5.2 --- Problem Formulation --- p.54 / Chapter 5.3 --- Dynamic Optimization --- p.58 / Chapter 5.3.1 --- The Simplified Dual Neural Network --- p.59 / Chapter 5.3.2 --- A Recursive Learning Algorithm --- p.60 / Chapter 5.3.3 --- Convergence Analysis --- p.61 / Chapter 5.4 --- RNN-based MPC Scheme --- p.65 / Chapter 5.5 --- Simulation Results --- p.67 / Chapter 5.5.1 --- Example 1 --- p.67 / Chapter 5.5.2 --- Example 2 --- p.68 / Chapter 5.5.3 --- Example 3 --- p.76 / Chapter 6 --- Model Predictive Control for Systems With Bounded Uncertainties Using a Discrete-Time Recurrent Neural Network --- p.81 / Chapter 6.1 --- Problem Formulation --- p.82 / Chapter 6.1.1 --- Process Model --- p.82 / Chapter 6.1.2 --- Robust. MPC Design --- p.82 / Chapter 6.2 --- Recurrent Neural Network Approach --- p.86 / Chapter 6.2.1 --- Neural Network Model --- p.86 / Chapter 6.2.2 --- Convergence Analysis --- p.88 / Chapter 6.2.3 --- Control Scheme --- p.90 / Chapter 6.3 --- Simulation Results --- p.91 / Chapter 7 --- Summary and future works --- p.95 / Chapter 7.1 --- Summary --- p.95 / Chapter 7.2 --- Future works --- p.96 / Bibliography --- p.97

Predicting developmental growth stages in soft red winter wheat (Triticum aestivum L.) (SRWW) could improve agronomic management in Kentucky. However, predicting SRWW development is complex due to vernalization requirement and photoperiod sensitivity differences of cultivars. The objectives of this study are to (1) determine ability of Kompetitive Allele Specific PCR (KASP) genotyping to predict phenotype; (2) determine the relative vernalization requirement (RVR) of 50 SRWW cultivars in a greenhouse (GH) assay; and (3) measure growing degree-days (GDD) required by cultivars to reach eight growth stages in a field assay. Fifty SRWW cultivars were characterized with 14 KASP markers for Vrn and Ppd loci. Additionally, cultivars were grown in a GH, vernalized outdoors for three, six, or nine weeks, and moved back into the GH where days to full flower were measured. Cultivars were also seeded into hill plots monthly from October to March at Princeton (2016; 2017) and Lexington, KY (2017) in three field trials. Cumulative GDD to emergence, green-up, pseudo-stem erection, jointing, flag leaf, beginning flower, full flower, and harvest maturity were measured. Field trials and supporting historical wheat development data suggest that prediction of SRWW growth and development is possible using a cumulative GDD scale in Kentucky.

Mathematical modeling of human running is a challenging problem from analytical and computational points of view. Purpose of the present research is to develop and study formulations and computational procedures for simulation of natural human running. The human skeletal structure is modeled as a mechanical system that includes link lengths, mass moments of inertia, joint torques, and external forces. The model has 55 degrees of freedom, 49 for revolute joints and 6 for global translation and rotation. Denavit-Hartenberg method is used for kinematics analysis and recursive Lagrangian formulation is used for the equations of motion. The dynamic stability is achieved by satisfying the zero moment point (ZMP) condition during the ground contact phase. B-spline interpolation is used for discretization of the joint angle profiles. The joint torque square, impulse at the foot strike, and yawing moment are included in the performance measure. A minimal set of constraints is imposed in the formulation of the problem to simulate natural running motion. Normal running with arm fixed, slow jog along curves, and running with upper body motion are formulated. Simulation results are obtained for various cases and discussed. The cases are running with different foot locations, running with backpack, and running with different running speeds. Also, extreme cases are performed. Each case gives reasonable cause and effect results. Furthermore, sparsity of the formulation is studied. The results obtained with the formulation are validated with the experimental data. The proposed formulation is robust and can predict natural motion of human running.

acase@tulane.edu / 1 / Kathryn M. Jones

Process control has been playing an increasingly important role in many industrial applications as an effective way to improve product quality, process costeffectiveness and safety. Simple linear dynamic models are used extensively in process control practice, but they are limited to the type of process behavior they can approximate. It is well-documented that simple nonlinear models can often provide much better approximations to process dynamics than linear models. It is evident that there is a potential of significant improvement of control quality through the implementation of the model-based control procedures. However, such control applications are still not widely implemented because mathematical process models in model-based control could be very difficult and expensive to obtain due to the complexity of those systems and poor understanding of the underlying physics. The main objective of this thesis is to develop new approaches to modeling and control of nonlinear processes. In this thesis, the multivariable nonlinear processes are approximated using a model with a static nonlinearity and a linear dynamics. In particular, the Hammerstein model structure, where the nonlinearity is on the input, is used. Cardinal spline functions are used to identify the multivariable input nonlinearity. Highlycoupled nonlinearity can also be identified due to flexibility and versatility of cardinal spline functions. An approach that can be used to identify both the nonlinearity and linear dynamics in a single step has been developed. The condition of persistent excitation has also been derived. Nonlinear control design approaches for the above models are then developed in this thesis based on: (1) a nonlinear compensator; (2) the extended internal model control (IMC); and (3) the model predictive control (MPC) framework. The concept of passivity is used to guarantee the stability of the closed-loop system of each of the approaches. In the nonlinear compensator approach, the passivity of the process is recovered using an appropriate static nonlinearity. The non-passive linear system is passified using a feedforward system, so that the passified overall system can be stabilized by a passive linear controller with the nonlinear compensator. In the extended IMC approach, dynamic inverses are used for both the input nonlinearity and linear dynamics. The concept of passive systems and the passivity-based stability conditions are used to obtain the invertible approximations of the subsystems and guarantee the stability of the nonlinear closed-loop system. In the MPC approach, a numerical inverse is implemented. The condition for which the numerical inversion is guaranteed to converge is derived. Based on these conditions, the input space in which the numerical inverse can be obtained is identified. This constitutes new constraints on the input space, in addition to the physical input constraints. The total input constraints are transformed into linear input constraints using polytopic descriptions and incorporated in the MPC design.

<p>The power to mass ratio of a heavy truck causes even moderate slopes to have a significant influence on the motion. The velocity will inevitable vary within an interval that is primarily determined by the ratio and the road topography. If further variations are actuated by a controller, there is a potential to lower the fuel consumption by taking the upcoming topography into account. This possibility is explored through theoretical and simulation studies as well as experiments in this work.</p><p>Look-ahead control is a predictive strategy that repeatedly solves an optimization problem online by means of a tailored dynamic programming algorithm. The scenario in this work is a drive mission for a heavy diesel truck where the route is known. It is assumed that there is road data on-board and that the current heading is known. A look-ahead controller is then developed to minimize fuel consumption and trip time.</p><p>The look-ahead control is realized and evaluated in a demonstrator vehicle and further studied in simulations. In the prototype demonstration, information about the road slope ahead is extracted from an on-board database in combination with a GPS unit. The algorithm calculates the optimal velocity trajectory online and feeds the conventional cruise controller with new set points. The results from the experiments and simulations confirm that look-ahead control reduces the fuel consumption without increasing the travel time. Also, the number of gear shifts is reduced. Drivers and passengers that have participated in tests and demonstrations have perceived the vehicle behavior as comfortable and natural.</p> / Report code: LIU-TEK-LIC-2007:28.

the majority of patients with early breast cancer receive some form of systemic adjuvant therapy (chemo-, endocrine, and/or targeted therapy). Despite the increase in adjuvant therapy prescription, little progress has been made with respect to assisting oncologists to determine which breast cancer patients, particularly those deemed at “lower risk” of relapse, require chemotherapy or other systemic therapy and which women can safely be treated with loco-regional treatment alone. For these reasons, the identification of prognostic and predictive markers that will assist the clinician in selecting the most suitable form of medical therapy has become very high priority as well as a real challenge in translational research. Unfortunately, several problems have hampered the identification and/or clinical usefulness of prognostic and predictive markers. In Chapter 1, we sought to address some of the specific questions regarding prognosis: - Are gene expression signatures robust and reproducible? - Do the different gene signatures have similar prognostic performance? Are they concordant in their prediction for the individual patient? - What is the role of individual genes in a signature and what is their biological interpretation? - What is the relationship between the molecular classification defined by cluster analysis and the different prognostic signatures? Through the following specific aims: 1. Independent validation study of a prognostic gene signature derived from microarray technology, to demonstrate its reproducibility, robustness and clinical utility compared with classical breast cancer prognostic factors in an appropriate validation cohort (Chapter 1A); 2. Independent comparison of three prognostic gene signatures (Chapter 1B); 3. Characterization of the biological foundation of the different prognostic signatures and refinement of our knowledge regarding breast cancer prognosis according to the molecular subgroups defined by ER and HER2 through a meta-analysis of publicly available gene expression data (Chapter 1C). In Chapter 2, we sought to address some specific questions regarding the prediction of response for the most commonly given breast cancer treatments: - What is the importance of proliferation genes in predicting clinical outcome in patients treated with endocrine therapy? - What is the value of TOP2A in predicting the efficacy of anthracycline therapy? - Can we identify a list of genes associated with response to anthracyline therapy? - What is the best method and cutoff to determine HER2-positive patients eligible for trastuzumab therapy? Would an alternative quantitative method for HER2 expression and homodimerization discriminate patients with significantly different probabilities of clinical outcome following treatment with trastuzumab? Through the following specific aims: 1. Investigation of molecular markers of response to endocrine therapy in hormono-sensitive patients (Chapter 2A); 2. Prospective evaluation of the predictive value of TOP2A and identification of genes associated with response in a cohort of patients treated with anthracyclines (Chapter 2B); 3. Investigation of the best method to select patients who should be treated by trastuzumab-based therapy and evaluation of a new technique to quantitatively assess HER2 expression (Chapter 2C).

Colorectal cancer (CRC) is one of the most common causes of death from malignant disease. Nevertheless, no ideal screening method exists and there is a lack of prognostic and predictive factors to support clinical decisions and to aid the development of a more individualized treatment for patients with CRC. The aim of this thesis was to investigate early detection, prognostic and predictive factors of CRC. In the first paper, a novel method to collect cells for DNA quantification from the rectal mucosa was investigated. The sensitivity and specificity of this test to detect CRC or any pathology in colon and rectum were ultimately too low to be acceptable. In the second paper, the prognostic value of growth differentiation factor 15 (GDF 15) was evaluated in patients curatively operated for colorectal cancer. GDF 15 expression was demonstrated to be associated with a negative prognosis in patients with stages I-III and III disease. In the third paper, the prognostic value of BRAF, PIK3CA KRAS and MSI was evaluated in a cohort of patients with CRC stratified by disease and recurrence. The results indicated that patients with CRC stage III without recurrence have a higher frequency of BRAF mutation compared to stage III patients with recurrence. In the fourth paper, histopathological predictors of pathologic complete response (pCR) as well as the association between pre-treatment carcinoembryonic antigen (CEA) levels and pCR in non-smoking and smoking patients receiving preoperative chemo-radiotherapy for rectal cancer were evaluated. Only in non-smokers was a low CEA level significantly associated with pCR, suggesting that the predictive value of CEA for pCR in rectal cancer in smokers can be limited. In sum, this research has investigated a new method for CRC detection and further evaluated the clinical use of prognostic and predictive markers in CRC.