Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Semiempirical Study of the Bergman Reaction: Towards a Computationally Efficient and Accurate Method for Modeling Enediyne Anticancer Antibiotics

Over the past 7 years, the enediyne anticancer antibiotics have been widely studied due to their DNA cleaving ability. The focus of these antibiotics, represented by kedarcidin chromophore, neocarzinostatin chromophore, calicheamicin, esperamicin A, and dynemicin A, is on the enediyne moiety contained within each of these antibiotics. In its inactive form, the moiety is benign to its environment. Upon suitable activation, the system undergoes a Bergman cycloaromatization proceeding through a 1,4-dehydrobenzene diradical intermediate. It is this diradical intermediate that is thought to cleave double-stranded dna through hydrogen atom abstraction. Semiempirical, semiempiricalci, Hartree–Fock ab initio, and mp2 electron correlation methods have been used to investigate the inactive hex-3-ene-1,5-diyne reactant, the 1,4-dehydrobenzene diradical, and a transition state structure of the Bergman reaction. Geometries calculated with different basis sets and by semiempirical methods have been used for single-point calculations using electron correlation methods. These results are compared with the best experimental and theoretical results reported in the literature. Implications of these results for computational studies of the enediyne anticancer antibiotics are discussed.

A Computationally Efficient Procedure for Modeling the First Step in the Alkaline Hydrolysis of Esters

A computationally efficient procedure for modeling the alkaline hydrolysis of esters is proposed based on calculations performed on methyl acetate and methyl benzoate systems. Extensive geometry and energy comparisons were performed on the simple ester methyl acetate. The effectiveness of performing high level single point ab initio energy calculations on the geometries obtained from semiempirical and ab initio methods was determined. The AM1 and PM3 semiempirical methods are evaluated for their ability to model the transition states and intermediates for ester hydrolysis. The Cramer/Truhlar SM3 solvation method was used to determine activation energies. The most computationally efficient way to model the transition states of large esters is to use the PM3 method. The PM3 transition structure can then be used as a template for the design of haptens capable of inducing catalytic antibodies.

Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao's method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims.

Geochemical Modeling and Analysis of the Frac Water Used in the Hydraulic Fracturing of the Marcellus Formation, Pennsylvania

The hydraulic fracturing of the Marcellus Formation creates a byproduct known as frac water. Five frac water samples were collected in Bradford County, PA. Inorganic chemical analysis, field parameters analysis, alkalinity titrations, total dissolved solids(TDS), total suspended solids (TSS), biological oxygen demand (BOD), and chemical oxygen demand (COD) were conducted on each sample to characterize frac water. A database of frac water chemistry results from across the state of Pennsylvania from multiple sources was compiled in order to provide the public and research communitywith an accurate characterization of frac water. Four geochemical models were created to model the reactions between frac water and the Marcellus Formation, Purcell Limestone, and the oil field brines presumed present in the formations. The average concentrations of chloride and TDS in the five frac water samples were 1.1 �± 0.5 x 105 mg/L (5.5X average seawater) and 140,000 mg/L (4X average seawater). BOD values for frac water immediately upon flow back were over 10X greater than the BOD of typical wastewater, but decreased into the range of typical wastewater after a short period of time. The COD of frac water decreases dramatically with an increase in elapsed time from flow back, but remain considerably higher than typicalwastewater. Different alkalinity calculation methods produced a range of alkalinity values for frac water: this result is most likely due to high concentrations of aliphatic acid anions present in the samples. Laboratory analyses indicate that the frac watercomposition is quite variable depending on the companies from which the water was collected, the geology of the local area, and number of fracturing jobs in which the frac water was used, but will require more treatment than typical wastewater regardless of theprecise composition of each sample. The geochemical models created suggest that the presence of organic complexes in an oil field brine and Marcellus Formation aid in the dissolution of ions such as bariumand strontium into the solution. Although equilibration reactions between the Marcellus Formation and the slickwater account for some of the final frac water composition, the predominant control of frac water composition appears to be the ratio of the mixture between the oil field brine and slickwater. The high concentration of barium in the frac water is likely due to the abundance of barite nodules in the Purcell Limestone, and the lack of sulfate in the frac water samples is due to the reducing, anoxic conditions in the earth's subsurface that allow for the degassing of H2S(g).