Control algorithms for large scale adaptive optics

In this dissertation, the problem of creating effective large scale Adaptive Optics (AO) systems control algorithms for the new generation of giant optical telescopes is addressed. The effectiveness of AO control algorithms is evaluated in several respects, such as computational complexity, compensation error rejection and robustness, i.e. reasonable insensitivity to the system imperfections. The results of this research are summarized as follows: 1. Robustness study of Sparse Minimum Variance Pseudo Open Loop Controller (POLC) for multi-conjugate adaptive optics (MCAO). The AO system model that accounts for various system errors has been developed and applied to check the stability and performance of the POLC algorithm, which is one of the most promising approaches for the future AO systems control. It has been shown through numerous simulations that, despite the initial assumption that the exact system knowledge is necessary for the POLC algorithm to work, it is highly robust against various system errors. 2. Predictive Kalman Filter (KF) and Minimum Variance (MV) control algorithms for MCAO. The limiting performance of the non-dynamic Minimum Variance and dynamic KF-based phase estimation algorithms for MCAO has been evaluated by doing Monte-Carlo simulations. The validity of simple near-Markov autoregressive phase dynamics model has been tested and its adequate ability to predict the turbulence phase has been demonstrated both for single- and multiconjugate AO. It has also been shown that there is no performance improvement gained from the use of the more complicated KF approach in comparison to the much simpler MV algorithm in the case of MCAO. 3. Sparse predictive Minimum Variance control algorithm for MCAO. The temporal prediction stage has been added to the non-dynamic MV control algorithm in such a way that no additional computational burden is introduced. It has been confirmed through simulations that the use of phase prediction makes it possible to significantly reduce the system sampling rate and thus overall computational complexity while both maintaining the system stable and effectively compensating for the measurement and control latencies.

Predictive control of hybrid vehicle powertrain for intelligent energy management

This thesis studies the minimization of the fuel consumption for a Hybrid Electric Vehicle (HEV) using Model Predictive Control (MPC). The presented MPC – based controller calculates an optimal sequence of control inputs to a hybrid vehicle using the measured plant outputs, the current dynamic states, a system model, system constraints, and an optimization cost function. The MPC controller is developed using Matlab MPC control toolbox. To evaluate the performance of the presented controller, a power-split hybrid vehicle, 2004 Toyota Prius, is selected. The vehicle uses a planetary gear set to combine three power components, an engine, a motor, and a generator, and transfer energy from these components to the vehicle wheels. The planetary gear model is developed based on the Willis’s formula. The dynamic models of the engine, the motor, and the generator, are derived based on their dynamics at the planetary gear. The MPC controller for HEV energy management is validated in the MATLAB/Simulink environment. Both the step response performance (a 0 – 60 mph step input) and the driving cycle tracking performance are evaluated. Two standard driving cycles, Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET), are used in the evaluation tests. For the UDDS and HWFET driving cycles, the simulation results, the fuel consumption and the battery state of charge, using the MPC controller are compared with the simulation results using the original vehicle model in Autonomie. The MPC approach shows the feasibility to improve vehicle performance and minimize fuel consumption.

DEVELOPMENT OF A PREDICTIVE COMBUSTION MODEL OF A SPARK IGNITED ENGINE WITH GASOLINE DIRECT INJECTION, VARIABLE VALVE TIMING, DURATION AND LIFT TECHNOLOGIES

There is a need by engine manufactures for computationally efficient and accurate predictive combustion modeling tools for integration in engine simulation software for the assessment of combustion system hardware designs and early development of engine calibrations. This thesis discusses the process for the development and validation of a combustion modeling tool for Gasoline Direct Injected Spark Ignited Engine with variable valve timing, lift and duration valvetrain hardware from experimental data. Data was correlated and regressed from accepted methods for calculating the turbulent flow and flame propagation characteristics for an internal combustion engine. A non-linear regression modeling method was utilized to develop a combustion model to determine the fuel mass burn rate at multiple points during the combustion process. The computational fluid dynamic software Converge ©, was used to simulate and correlate the 3-D combustion system, port and piston geometry to the turbulent flow development within the cylinder to properly predict the experimental data turbulent flow parameters through the intake, compression and expansion processes. The engine simulation software GT-Power © is then used to determine the 1-D flow characteristics of the engine hardware being tested to correlate the regressed combustion modeling tool to experimental data to determine accuracy. The results of the combustion modeling tool show accurate trends capturing the combustion sensitivities to turbulent flow, thermodynamic and internal residual effects with changes in intake and exhaust valve timing, lift and duration.

Is a change in patient-reported dysphagia after induction chemotherapy in locally advanced esophageal cancer a predictive factor for pathological response to neoadjuvant chemoradiation?

GOALS OF WORK: In patients with locally advanced esophageal cancer, only those responding to the treatment ultimately benefit from preoperative chemoradiation. We investigated whether changes in subjective dysphagia or eating restrictions after two cycles of induction chemotherapy can predict histopathological tumor response observed after chemoradiation. In addition, we examined general long-term quality of life (QoL) and, in particular, eating restrictions after esophagectomy. MATERIALS AND METHODS: Patients with resectable, locally advanced squamous cell- or adenocarcinoma of the esophagus were treated with two cycles of chemotherapy followed by chemoradiation and surgery. They were asked to complete the EORTC oesophageal-specific QoL module (EORTC QLQ-OES24), and linear analogue self-assessment QoL indicators, before and during neoadjuvant therapy and quarterly until 1 year postoperatively. A median change of at least eight points was considered as clinically meaningful. MAIN RESULTS: Clinically meaningful improvements in the median scores for dysphagia and eating restrictions were found during induction chemotherapy. These improvements were not associated with a histopathological response observed after chemoradiation, but enhanced treatment compliance. Postoperatively, dysphagia scores remained low at 1 year, while eating restrictions persisted more frequently in patients with extended transthoracic resection compared to those with limited transhiatal resection. CONCLUSIONS: The improvement of dysphagia and eating restrictions after induction chemotherapy did not predict tumor response observed after chemoradiation. One year after esophagectomy, dysphagia was a minor problem, and global QoL was rather good. Eating restrictions persisted depending on the surgical technique used.

Admission blood glucose is an independent predictive factor for hospital mortality in polytraumatised patients

PURPOSE: The goal of this study was to analyse a possible association of admission blood glucose with hospital mortality of polytraumatised patients and to develop an outcome prediction model for this patient group. METHODS: The outcome of adult polytraumatised patients admitted to the University Hospital of Berne, Switzerland, between 2002 and 2004 with an ISS > or = 17, and more than one severely injured organ system was retrospectively analysed. RESULTS: The inclusion criteria were met by 555 patients, of which 108 (19.5%) died. Hyperglycaemia proved to be an independent predictor for hospital mortality (P < 0.0001), following multiple regression analysis. After inclusion of admission blood glucose, the calculated mortality prediction model performed better than currently described models (P < 0.0001, AUC 0.924). CONCLUSION: In this retrospective, single-centre study in polytraumatised patients, admission blood glucose proved to be an independent predictor of hospital mortality following regression analysis controlling for age, gender, injury severity and other laboratory parameters. A reliable admission blood glucose-based mortality prediction model for polytraumatised patients could be established. This observation may be helpful in improving the precision of future outcome prediction models for polytraumatised patients. These observations warrant further prospective evaluation.

Limited predictive value of FDG-PET for response assessment in the preoperative treatment of esophageal cancer: results of a prospective multi-center trial (SAKK 75/02)

BACKGROUND: Only responding patients benefit from preoperative therapy for locally advanced esophageal carcinoma. Early detection of non-responders may avoid futile treatment and delayed surgery. PATIENTS AND METHODS: In a multi-center phase ll trial, patients with resectable, locally advanced esophageal carcinoma were treated with 2 cycles of induction chemotherapy followed by chemoradiotherapy (CRT) and surgery. Positron emission tomography with 2[fluorine-18]fluoro-2-deoxy-d-glucose (FDG-PET) was performed at baseline and after induction chemotherapy. The metabolic response was correlated with tumor regression grade (TRG). A decrease in FDG tumor uptake of less than 40% was prospectively hypothesized as a predictor for histopathological non-response (TRG > 2) after CRT. RESULTS: 45 patients were included. The median decrease in FDG tumor uptake after chemotherapy correlated well with TRG after completion of CRT (p = 0.021). For an individual patient, less than 40% decrease in FDG tumor uptake after induction chemotherapy predicted histopathological non-response after completion of CRT, with a sensitivity of 68% and a specificity of 52% (positive predictive value 58%, negative predictive value 63%). CONCLUSIONS: Metabolic response correlated with histopathology after preoperative therapy. However, FDG-PET did not predict non-response after induction chemotherapy with sufficient clinical accuracy to justify withdrawal of subsequent CRT and selection of patients to proceed directly to surgery.

An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

Predictive-DCT Coding for 3D Mesh Sequences Compression

This paper proposes a new compression algorithm for dynamic 3d meshes. In such a sequence of meshes, neighboring vertices have a strong tendency to behave similarly and the degree of dependencies between their locations in two successive frames is very large which can be efficiently exploited using a combination of Predictive and DCT coders (PDCT). Our strategy gathers mesh vertices of similar motions into clusters, establish a local coordinate frame (LCF) for each cluster and encodes frame by frame and each cluster separately. The vertices of each cluster have small variation over a time relative to the LCF. Therefore, the location of each new vertex is well predicted from its location in the previous frame relative to the LCF of its cluster. The difference between the original and the predicted local coordinates are then transformed into frequency domain using DCT. The resulting DCT coefficients are quantized and compressed with entropy coding. The original sequence of meshes can be reconstructed from only a few non-zero DCT coefficients without significant loss in visual quality. Experimental results show that our strategy outperforms or comes close to other coders.

Executive compensation and the performance of the firm

A sample of large industrial corporations is examined to determine whether there is a relationship between the levels of compensation received by the senior executives of those firms and the firms' economic performances. We find consistent evidence of such a relationship, with differences across firms in the total compensation of their three highest-paid officers being positively related to differences in both the common stock returns and operating profitability of the firms. The implication is that compensation packages are designed to reduce agency costs.

Executive compensation and executive incentive problems: an empirical analysis

The question of whether the design of the corporate executive pay package reflects an attempt to reduce agency costs between shareholders an managers is adressed. The components of senior executive pay are found to vary systematically across firms in a manner that cannot easily be explained by tax effects, and which would indicate that individual elements of pay are aimed at controlling for limited horizon and risk exposure problems. Managerial decisions and the structure of managerial pay therefore appear to be interrelated.

The Costs and Predictive Factors of Bovine Respiratory Disease in Standardized Steer Tests

A retrospective study of 2,146 feedlot cattle in 17 feedlot tests from 1988 to 1997 was conducted to determine the impact of bovine respiratory disease (BRD) on veterinary treatment costs, average daily gain, carcass traits, mortality, and net profit. Morbidity caused by BRD was 20.6%. The average cost to treat each case of BRD was $12.39. Mortality rate of calves diagnosed and treated for BRD was 5.9% vs. .35% for those not diagnosed with BRD. Average daily gain differed between treated and non-treated steers during the first 28 days on feed but did not differ from 28 days to harvest. Net profit was $57.48 lower for treated steers. Eighty-two percent of this difference was due to a combination of mortality and treatment costs. Eighteen percent of the net profit difference was due to improved performance and carcass value of the non-treated steers. Data from 496 steers and heifers in nine feedlot tests were used to determine the effects of age, weaning, and use of modified live virus or killed vaccines prior to the test to predict BRD. Younger calves, non-weaned calves, and calves vaccinated with killed vaccines prior to the test had higher BRD morbidity than those that were older, weaned, or vaccinated with modified live virus vaccines, respectively. Treatment regimes that precluded relapse resulting in re-treatment prevented reduced performance and loss of carcass value. Using modified live virus vaccines and weaning calves 30 days prior to shipment reduced the incidence of BRD.