Parameter design considerations in precision engineering

Parameter design is an experimental design and analysis methodology for developing robust processes and products. Robustness implies insensitivity to noise disturbances. Subtle experimental realities, such as the joint effect of process knowledge and analysis methodology, may affect the effectiveness of parameter design in precision engineering; where the objective is to detect minute variation in product and process performance. In this thesis, approaches to statistical forced-noise design and analysis methodologies were investigated with respect to detecting performance variations. Given a low degree of process knowledge, Taguchi's methodology of signal-to-noise ratio analysis was found to be more suitable in detecting minute performance variations than the classical approach based on polynomial decomposition. Comparison of inner-array noise (IAN) and outer-array noise (OAN) structuring approaches showed that OAN is a more efficient design for precision engineering. ^

Improving the predictability of time series by chaotic parameter estimation

Limited literature regarding parameter estimation of dynamic systems has been identified as the central-most reason for not having parametric bounds in chaotic time series. However, literature suggests that a chaotic system displays a sensitive dependence on initial conditions, and our study reveals that the behavior of chaotic system: is also sensitive to changes in parameter values. Therefore, parameter estimation technique could make it possible to establish parametric bounds on a nonlinear dynamic system underlying a given time series, which in turn can improve predictability. By extracting the relationship between parametric bounds and predictability, we implemented chaos-based models for improving prediction in time series. ^ This study describes work done to establish bounds on a set of unknown parameters. Our research results reveal that by establishing parametric bounds, it is possible to improve the predictability of any time series, although the dynamics or the mathematical model of that series is not known apriori. In our attempt to improve the predictability of various time series, we have established the bounds for a set of unknown parameters. These are: (i) the embedding dimension to unfold a set of observation in the phase space, (ii) the time delay to use for a series, (iii) the number of neighborhood points to use for avoiding detection of false neighborhood and, (iv) the local polynomial to build numerical interpolation functions from one region to another. Using these bounds, we are able to get better predictability in chaotic time series than previously reported. In addition, the developments of this dissertation can establish a theoretical framework to investigate predictability in time series from the system-dynamics point of view. ^ In closing, our procedure significantly reduces the computer resource usage, as the search method is refined and efficient. Finally, the uniqueness of our method lies in its ability to extract chaotic dynamics inherent in non-linear time series by observing its values. ^

Essays on the determinants of private investment: The effects of relative price uncertainty and political instability

The objective of this study was to provide empirical evidence on the effects of relative price uncertainty and political instability on private investment. My effort is expressed in a single-equation model using macroeconomic and socio-political data from eight Latin American countries for the period 1970–1996. Relative price uncertainty is measured by the implied volatility of the exchange rate and political instability is measured by using indicators of social unrest and political violence. ^ I found that, after controlling for other variables, relative price uncertainty and political instability are negatively associated with private investment. Macroeconomic and political stability are key ingredients for the achievement of a strong investment response. This highlights the need to develop the state and build a civil society in which citizens can participate in decision-making and express consent without generating social turmoil. At the same time the government needs to implement structural policies along with relative price adjustments to eliminate excess volatility in price movements in order to provide a stable environment for investment. ^

Reduced-parameter model of head-related transfer functions for synthesized spatial audio

Digital systems can generate left and right audio channels that create the effect of virtual sound source placement (spatialization) by processing an audio signal through pairs of Head-Related Transfer Functions (HRTFs) or, equivalently, Head-Related Impulse Responses (HRIRs). The spatialization effect is better when individually-measured HRTFs or HRIRs are used than when generic ones (e.g., from a mannequin) are used. However, the measurement process is not available to the majority of users. There is ongoing interest to find mechanisms to customize HRTFs or HRIRs to a specific user, in order to achieve an improved spatialization effect for that subject. Unfortunately, the current models used for HRTFs and HRIRs contain over a hundred parameters and none of those parameters can be easily related to the characteristics of the subject. This dissertation proposes an alternative model for the representation of HRTFs, which contains at most 30 parameters, all of which have a defined functional significance. It also presents methods to obtain the value of parameters in the model to make it approximately equivalent to an individually-measured HRTF. This conversion is achieved by the systematic deconstruction of HRIR sequences through an augmented version of the Hankel Total Least Squares (HTLS) decomposition approach. An average 95% match (fit) was observed between the original HRIRs and those re-constructed from the Damped and Delayed Sinusoids (DDSs) found by the decomposition process, for ipsilateral source locations. The dissertation also introduces and evaluates an HRIR customization procedure, based on a multilinear model implemented through a 3-mode tensor, for mapping of anatomical data from the subjects to the HRIR sequences at different sound source locations. This model uses the Higher-Order Singular Value Decomposition (HOSVD) method to represent the HRIRs and is capable of generating customized HRIRs from easily attainable anatomical measurements of a new intended user of the system. Listening tests were performed to compare the spatialization performance of customized, generic and individually-measured HRIRs when they are used for synthesized spatial audio. Statistical analysis of the results confirms that the type of HRIRs used for spatialization is a significant factor in the spatialization success, with the customized HRIRs yielding better results than generic HRIRs.

Parameter optimization for noise and vibration reduction associated with position and speed sensorless control of permanent magnet machines at low speed

With the advantages and popularity of Permanent Magnet (PM) motors due to their high power density, there is an increasing incentive to use them in variety of applications including electric actuation. These applications have strict noise emission standards. The generation of audible noise and associated vibration modes are characteristics of all electric motors, it is especially problematic in low speed sensorless control rotary actuation applications using high frequency voltage injection technique. This dissertation is aimed at solving the problem of optimizing the sensorless control algorithm for low noise and vibration while achieving at least 12 bit absolute accuracy for speed and position control. The low speed sensorless algorithm is simulated using an improved Phase Variable Model, developed and implemented in a hardware-in-the-loop prototyping environment. Two experimental testbeds were developed and built to test and verify the algorithm in real time.^ A neural network based modeling approach was used to predict the audible noise due to the high frequency injected carrier signal. This model was created based on noise measurements in an especially built chamber. The developed noise model is then integrated into the high frequency based sensorless control scheme so that appropriate tradeoffs and mitigation techniques can be devised. This will improve the position estimation and control performance while keeping the noise below a certain level. Genetic algorithms were used for including the noise optimization parameters into the developed control algorithm.^ A novel wavelet based filtering approach was proposed in this dissertation for the sensorless control algorithm at low speed. This novel filter was capable of extracting the position information at low values of injection voltage where conventional filters fail. This filtering approach can be used in practice to reduce the injected voltage in sensorless control algorithm resulting in significant reduction of noise and vibration.^ Online optimization of sensorless position estimation algorithm was performed to reduce vibration and to improve the position estimation performance. The results obtained are important and represent original contributions that can be helpful in choosing optimal parameters for sensorless control algorithm in many practical applications.^

Instability of CTG Repeats is Governed by the Position of a DNA Base Lesion through Base Excision Repair

Trinucleotide repeat (TNR) expansions and deletions are associated with human neurodegeneration and cancer. However, their underlying mechanisms remain to be elucidated. Recent studies have demonstrated that CAG repeat expansions can be initiated by oxidative DNA base damage and fulfilled by base excision repair (BER), suggesting active roles for oxidative DNA damage and BER in TNR instability. Here, we provide the first evidence that oxidative DNA damage can induce CTG repeat deletions along with limited expansions in human cells. Biochemical characterization of BER in the context of (CTG)20 repeats further revealed that repeat instability correlated with the position of a base lesion in the repeat tract. A lesion located at the 59-end of CTG repeats resulted in expansion, whereas a lesion located either in the middle or the 39-end of the repeats led to deletions only. The positioning effects appeared to be determined by the formation of hairpins at various locations on the template and the damaged strands that were bypassed by DNA polymerase b and processed by flap endonuclease 1 with different efficiency. Our study indicates that the position of a DNA base lesion governs whether TNR is expanded or deleted through BER.

Interval and Point Estimators for the Location Parameter of the Three-Parameter Lognormal Distribution

The three-parameter lognormal distribution is the extension of the two-parameter lognormal distribution to meet the need of the biological, sociological, and other fields. Numerous research papers have been published for the parameter estimation problems for the lognormal distributions. The inclusion of the location parameter brings in some technical difficulties for the parameter estimation problems, especially for the interval estimation. This paper proposes a method for constructing exact confidence intervals and exact upper confidence limits for the location parameter of the three-parameter lognormal distribution. The point estimation problem is discussed as well. The performance of the point estimator is compared with the maximum likelihood estimator, which is widely used in practice. Simulation result shows that the proposed method is less biased in estimating the location parameter. The large sample size case is discussed in the paper.

Oxidative DNA Damage Modulates Trinucleotide Repeat Instability Via DNA Base Excision Repair

Trinucleotide repeat (TNR) expansion is the cause of more than 40 types of human neurodegenerative diseases such as Huntington’s disease. Recent studies have linked TNR expansion with oxidative DNA damage and base excision repair (BER). In this research, we provided the first evidence that oxidative DNA damage can induce CAG repeat deletion/contraction via BER. We found that BER of an oxidized DNA base lesion, 8-oxoguanine in a CAG repeat tract, resulted in the formation of a CTG hairpin at the template strand. DNA polymerase β (pol b) then skipped over the hairpin creating a 5’-flap that was cleaved by flap endonuclease 1 (FEN1) leading to CAG repeat deletion. To further investigate whether BER may help to shorten an expanded TNR tract, we examined BER in a CAG repeat hairpin loop. We found that 8-oxoguanine DNA glycosylase removed the oxidized base located in the loop region of the hairpin leaving an abasic site. Apurinic/apyrimidinic (AP) endonuclease 1 then incised the 5’-end of the abasic site leaving a nick in the loop. This further converted the hairpin into an intermediate with a 3’-flap and a 5’-flap. As a 5’-3’ endonuclease, FEN1 cleaved the 5’-flap, whereas a 3’-5’ endonuclease, Mus81/Eme1, removed the 3’-flap. The coordination between FEN1 and Mus81/Eme1 ultimately resulted in removal of a CAG repeat hairpin attenuating or preventing TNR expansion. To further explore if pol β bypass of an oxidized base lesion, 5’,8-cyclodeoxyadenosine, may affect TNR instability, we examined pol β DNA synthesis in bypassing this base lesion and found that the lesion preferentially induced TNR deletion during BER and Okazaki fragment maturation. The repeat deletion was mediated by the formation of a loop in the template strand induced specifically by the damage. Pol β then skipped over the loop structure creating a 5’-flap that was efficiently removed by FEN1 leading to repeat deletion. Our study demonstrates that pol β-mediated BER plays an important role in mediating TNR deletion and removing a TNR hairpin to prevent TNR expansion. Our research provides a molecular basis for further developing BER as a target for prevention and treatment of neurodegenerative diseases caused by TNR expansion.