Essays on Optimal Control of Dynamic Systems with Learning

<p>This dissertation studies the optimal control of two different dynamic systems with learning: (i) diagnostic service systems, and (ii) green incentive policy design. In both cases, analytical models have been developed to improve our understanding of the system, and managerial insights are gained on its optimal management.</p><p>We first consider a diagnostic service system in a queueing framework, where the service is in the form of sequential hypothesis testing. The agent should dynamically weigh the benefit of performing an additional test on the current task to improve the accuracy of her judgment against the incurred delay cost for the accumulated workload. We analyze the accuracy/congestion tradeoff in this setting and fully characterize the structure of the optimal policy. Further, we allow for admission control (dismissing tasks from the queue without processing) in the system, and derive its implications on the structure of the optimal policy and system's performance.</p><p>We then study Feed-in-Tariff (FIT) policies, which are incentive mechanisms by governments to promote renewable energy technologies. We focus on two key network externalities that govern the evolution of a new technology in the market over time: (i) technological learning, and (ii) social learning. By developing an intertemporal model that captures these dynamics, we investigate how lawmakers should leverage on such effects to make FIT policies more efficient. We contrast our findings against the current practice of FIT-implementing jurisdictions, and also determine how the FIT regimes should depend on specific technology and market characteristics.</p>Dissertatio

Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters

The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data

Predicting arsenic and heavy metals contamination in groundwater resources of Ghahavand plain based on an artificial neural network optimized by imperialist competitive algorithm

Background: The effects of trace elements on human health and the environment gives importance to the analysis of heavy metals contamination in environmental samples and, more particularly, human food sources. Therefore, the current study aimed to predict arsenic and heavy metals (Cu, Pb, and Zn) contamination in the groundwater resources of Ghahavand Plain based on an artificial neural network (ANN) optimized by imperialist competitive algorithm (ICA). Methods: This study presents a new method for predicting heavy metal concentrations in the groundwater resources of Ghahavand plain based on ANN and ICA. The developed approaches were trained using 75% of the data to obtain the optimum coefficients and then tested using 25% of the data. Two statistical indicators, the coefficient of determination (R2) and the root-mean-square error (RMSE), were employed to evaluate model performance. A comparison of the performances of the ICA-ANN and ANN models revealed the superiority of the new model. Results of this study demonstrate that heavy metal concentrations can be reliably predicted by applying the new approach. Results: Results from different statistical indicators during the training and validation periods indicate that the best performance can be obtained with the ANN-ICA model. Conclusion: This method can be employed effectively to predict heavy metal concentrations in the groundwater resources of Ghahavand plain

Modeling of Groundwater Resources Heavy Metals Concentration Using Soft Computing Methods: Application of Different Types of Artificial Neural Networks

Nowadays, groundwater resources play a vital role as a source of drinking water in arid and semiarid regions and forecasting of pollutants content in these resources is very important. Therefore, this study aimed to compare two soft computing methods for modeling Cd, Pb and Zn concentration in groundwater resources of Asadabad Plain, Western Iran. The relative accuracy of several soft computing models, namely multi-layer perceptron (MLP) and radial basis function (RBF) for forecasting of heavy metals concentration have been investigated. In addition, Levenberg-Marquardt, gradient descent and conjugate gradient training algorithms were utilized for the MLP models. The ANN models for this study were developed using MATLAB R 2014 Software program. The MLP performs better than the other models for heavy metals concentration estimation. The simulation results revealed that MLP model was able to model heavy metals concentration in groundwater resources favorably. It generally is effectively utilized in environmental applications and in the water quality estimations. In addition, out of three algorithms, Levenberg-Marquardt was better than the others were. This study proposed soft computing modeling techniques for the prediction and estimation of heavy metals concentration in groundwater resources of Asadabad Plain. Based on collected data from the plain, MLP and RBF models were developed for each heavy metal. MLP can be utilized effectively in applications of prediction of heavy metals concentration in groundwater resources of Asadabad Plain

Relationship Between Serum Ferritin Level and Amikacin Ototoxicity

Objectives: Aminoglycosides are highly effective against bacteria but have serious side-effects including ototoxicity and nephrotoxicity. One of the theories in aminoglycosides ototoxicity is that Iron-aminoglycoside complex causes ototoxicity by creating free radicals. Based on this theory, the relationship between serum iron level and amikacin ototoxicity was studied to determine whether more iron results in more ototoxcity.Methods: This prospective cohort study was conducted from August 2005 to October 2008. Patients with amikacin prescription and different serum-ferritin levels were examined. Burned patients with amikacin prescription were divided into Group1 (89 patients; serum-ferritin >150) and Group2 (92 patients, serum-ferritin <150). Their hearing thresholds and red-blood-cells indices were compared using t- and paired t-test.Results: In comparing the two groups, thresholds of Group1 were higher than Group2 at all frequencies, and the difference was statistically significant (p<0.001). The maximum threshold shift in Group1 was greater than 20 dB and in Group2, it was less than 10 dB, at 8000Hz. Again, this result was statistically and clinically significant (p<0.001). Finally, the mean corpuscular volume (MCV)was higher in Group1 than Group2, and (p=0.001).Conclusion: The results suggest that the level of iron is related to aminoglycoside ototoxicity. More iron can create more ototoxicity, and iron deficiency may inhibit aminoglycoside ototoxicity. An increase in MCV may be due to higher serum ferritin and an indication of more ototoxicity

Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis.

The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes

Comparison of the Frequency of Old Septal Deviation in Patients with and without Traumatic Nasal Bone Fracture

Investigating the frequency of traumatic nasal bone fracture in patients with and without old septal deviation and possible deviation. Prospective study of 105 patients with nose trauma conducted and cases were divided into two groups: a study group 35 patients with nasal fracture and a control group of 70 patients without nasal fracture. Diagnosis of septal condition was made by anterior rhinos copy and endoscopy using manipulation of septum. 31 (89%) of the patients with nasal fracture after trauma were diagnosed with old septal deviation. In comparison, only 39 (34%) of patients in the control group were diagnosed with old septal deviation. In comparison, only 39 (54%) of patients in the control group were diagnosed with old septal deviation. Existing old septal deviation significantly increases the risk of traumatic nasal bone fracture