• Korean Journal of Remote Sensing
• /
• v.25 no.6
• /
• pp.547-557
• /
• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Computers and Concrete
• /
• v.5 no.4
• /
• pp.401-416
• /
• 2008

The paper presents a methodology to model three-dimensional reinforced concrete members by means of embedded discontinuity elements based on the Continuum Strong Discontinuous Approach (CSDA). Mixture theory concepts are used to model reinforced concrete as a 3D composite material constituted of concrete with long fibers (rebars) bundles oriented in different directions embedded in it. The effects of the rebars are modeled by phenomenological constitutive models devised to reproduce the axial non-linear behavior, as well as the bond-slip and dowel action. The paper presents the constitutive models assumed for the components and the compatibility conditions chosen to constitute the composite. Numerical analyses of existing experimental reinforced concrete members are presented, illustrating the applicability of the proposed methodology.

• Bulletin of the Korean Chemical Society
• /
• v.29 no.5
• /
• pp.979-984
• /
• 2008

In this study, 8 solutes (aniline, caffeine, p-cresol, ethyl benzene, methylparaben, phenol, pyridine, and toluene) have been tested in terms of linear solvation energy relationships (LSER). Several micellar liquid chromatography (MLC) systems using cationic surfactant cetyltrimethylammonium bromide (CTAB) and a mixture of water with (methanol, n-propanol, and n-butanol) modifiers were characterized using the LSER solvation parameter model. The effects of the surfactant and modifier concentration on the retention in MLC were discussed. LSER model had demonstrated high potential to predict retention factors with high squared correlation coefficients ($r^2$ > 0.99). A comparison of predicted and experimental retention factors suggests that LSER formalism is able to reproduce adequately the experimental retention factors of the solutes studied in the different experimental conditions investigated. This model is a helpful tool to understand the solute-surfactant interactions and evaluate the retention characteristic of micellar liquid chromatography.

• Advances in environmental research
• /
• v.3 no.3
• /
• pp.231-251
• /
• 2014

The present study examines the phosphate adsorption potential and behavior of mixture of Ground Burnt Patties (GBP), a solid waste generated from cooking fuel used in earthen stoves and Red Soil (RS), a natural substance in fixed bed column mode operation. The characterization of adsorbent was done by Proton Induced X-ray Emission (PIXE), and Proton Induced ${\gamma}$-ray Emission (PIGE) methods. The FTIR spectroscopy of spent adsorbent reveals the presence of absorbance peak at $1127cm^{-1}$ which appears due to P = O stretching, thus confirming phosphate adsorption. The effects of bed height (10, 15 and 20 cm), flow rate (2.5, 5 and 7.5 mL/min) and initial phosphate concentration (5 and 15 mg/L) on breakthrough curves were explored. Both the breakthrough and exhaustion time increased with increase in bed depth, decrease in flow rate and influent concentration. Thomas model, Yoon-Nelson model and Modified Dose Response model were used to fit the column adsorption data using nonlinear regression analysis while Bed Depth Service Time model followed linear regression analysis under different experimental condition to evaluate model parameters that are useful in scale up of the process. The values of correlation coefficient ($R^2$) and the Sum of Square Error (SSE) revealed the Modified Dose Response model as the best fitted model to the experimental data. The adsorbent mixture responded effectively to the desorption and reusability experiment. The results of this finding advocated that mixture of GBP and RS can be used as a low cost, highly efficient adsorbent for phosphate removal from aqueous solution.

• The Journal of Korea Robotics Society
• /
• v.5 no.1
• /
• pp.70-76
• /
• 2010

This paper addresses a design issue of "model complexity and performance trade-off" in the application of bandwidth extension (BWE) methods to the intra-frame predictivevector quantization problem of wideband speech. It discusses model-based linear and non-linear prediction methods and presents a comparative study of them in terms of prediction gain. Through experimentation, the general trend of saturation in performance (with the increase in model complexity) is observed. However, specifically, it is also observed that there is no significant difference between HMM and GMM-based BWE functions.

• Structural Engineering and Mechanics
• /
• v.63 no.6
• /
• pp.809-824
• /
• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.

• Bulletin of the Korean Chemical Society
• /
• v.26 no.3
• /
• pp.384-392
• /
• 2005

Solvent effects on the kinetics of bromophenol blue fading have been investigated within a temperature range in binary mixtures of methanol, ethanol, 1-propanol, ethylene glycol and 1,2-propanediol with water of varying solvent compositions up to 40% by weight of organic solvent component. Correlation of logk with reciprocal of the dielectric constant was linear. Finally a mechanism was proposed for the bromophenol blue fading upon SESMORTAC (study of effect of solvent mixture on the one-step reaction rates using the transition state theory and cage effect) model, by means of this model, the fundamental rate constants of the fading reaction in these solvent systems were calculated.

• MALSORI
• /
• no.66
• /
• pp.61-72
• /
• 2008

This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.

• Communications for Statistical Applications and Methods
• /
• v.24 no.5
• /
• pp.457-480
• /
• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• Speech Sciences
• /
• v.10 no.3
• /
• pp.263-277
• /
• 2003

One of major problems in speech recognition is performance degradation due to the mismatch between the training and test environments. Recently, Stereo-based Piecewise LInear Compensation for Environments (SPLICE), which is frame-based bias removal algorithm for cepstral enhancement using stereo training data and noisy speech model as a mixture of Gaussians, was proposed and showed good performance in noisy environments. In this paper, we propose several methods to improve the conventional SPLICE. First we apply Cepstral Mean Subtraction (CMS) as a preprocessor to SPLICE, instead of applying it as a postprocessor. Secondly, to compensate residual distortion after SPLICE processing, two-stage SPLICE is proposed. Thirdly we employ phonetic information for training SPLICE model. According to experiments on the Aurora 2 database, proposed method outperformed the conventional SPLICE and we achieved a 50% decrease in word error rate over the Aurora baseline system.