• Structural Engineering and Mechanics
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• v.57 no.1
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• pp.21-43
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• 2016

A conditional probability based approach known as Particle Filter Method (PFM) is a powerful tool for system parameter identification. In this paper, PFM has been applied to identify the vehicle parameters based on response statistics of the bridge. The flexibility of vehicle model has been considered in the formulation of bridge-vehicle interaction dynamics. The random unevenness of bridge has been idealized as non homogeneous random process in space. The simulated response has been contaminated with artificial noise to reflect the field condition. The performance of the identification system has been examined for various measurement location, vehicle velocity, bridge surface roughness factor, noise level and assumption of prior probability density. Identified vehicle parameters are found reasonably accurate and reconstructed interactive force time history with identified parameters closely matches with the simulated results. The study also reveals that crude assumption of prior probability density function does not end up with an incorrect estimate of parameters except requiring longer time for the iterative process to converge.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.115-120
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• 2003

Inference for probability P(Y

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.1
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• pp.159-166
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• 1987

A new family of sinusoidal orthogoal trnasform is introduced. For a derivation, a parametric sinusoidal matrix whose transform might be implemented by a suitable FFT algorithm is modeled basically on the analogy of well-known sinusoidal transform such as DCT,SCT, etc., and its orthogonality condition is calculated. The parameters satisfying orthogonality condition are determined, in a sense, by particular solution after trial and error. However more than then transform matrices not yet known are obtained. It is also shown that these transforms can be computed by a DFT. of an image.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.881-889
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• 2009

Support vector machines(SVMs) are known as flexible and efficient classifier of multivariate observations, producing a hyperplane or hyperdimensional curved surface in multidimensional feature space that best separates training samples by known groups. As various methodological extensions are made for SVM classifiers in recent years, it becomes more difficult to understand the constructed model intuitively. The aim of this paper is to visualize various SVM classifications tuned by several parameters in reduced dimensions, so that data analysts secure the tangible image of the products that the machine made.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.453-458
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• 2018

Geomechanical parameters are important factors for engineering projects during design, construction and support stages of tunnel and dam projects. Geostatistical estimation methods are known as one of the most significant approach at estimation of Geomechanical parameters. In this study, Azad dam headrace tunnel is chosen to estimate Geomechanical parameters such as Rock Quality Designation (RQD) and uniaxial compressive strength (UCS) by ordinary kriging as a geostatistical method. Also Rock Mass Rating (RMR) distribution is presented along the tunnel. Main aim in employment of geostatistical methods is estimation of points that unsampled by sampled points.To estimation of parameters, initially data are transformed to Gaussian distribution, next structural data analysis is completed, and then ordinary kriging is applied. At end, specified distribution maps for each parameter are presented. Results from the geostatistical estimation method and actual data have been compared. Results show that, the estimated parameters with this method are very close to the actual parameters. Regarding to the reduction of costs and time consuming, this method can use to geomechanical estimation.

• Journal of Gastric Cancer
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• v.19 no.3
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• pp.254-277
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• 2019

Inflammation can be a causative factor for carcinogenesis or can result from a consequence of cancer progression. Moreover, cancer therapeutic interventions can also induce an inflammatory response. Various inflammatory parameters are used to assess the inflammatory status during cancer treatment. It is important to select the most optimal biomarker among these parameters. Additionally, suitable biomarkers must be examined if there are no known parameters. We briefly reviewed the published literature for the use of inflammatory parameters in the treatment of patients with cancer. Most studies on inflammation evaluated the correlation between host characteristics, effect of interventions, and clinical outcomes. Additionally, the levels of C-reactive protein, albumin, lymphocytes, and platelets were the most commonly used laboratory parameters, either independently or in combination with other laboratory parameters and clinical characteristics. Furthermore, the immune parameters are classically examined using flow cytometry, immunohistochemical staining, and enzyme-linked immunosorbent assay techniques. However, gene expression profiling can aid in assessing the overall peri-interventional immune status. The checklists of guidelines, such as STAndards for Reporting of Diagnostic accuracy and REporting recommendations for tumor MARKer prognostic studies should be considered when designing studies to investigate the inflammatory parameters. Finally, the data should be interpreted after adjusting for clinically important variables, such as age and cancer stage.

• The Journal of the Acoustical Society of Korea
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• v.17 no.3E
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• pp.53-57
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• 1998

Spectral information at a speech coder should be quantized with sufficient accuracy to keep perceptually transparent output speech. Spectral information at a low bit rate speech coder is usually transformed into corresponding line spectrum pair parameters and is often quantized with a vector quantization algorithm. As the vector quantization algorithm generally has high complexity in the optimal code vector searching routine, the complexity reduction in that routine is investigated using the ordering property of the line spectrum pair. When the proposed complexity reduction algorithm is applied to the well-known split vector quantization algorithm, the 46% complexity reduction is achieved in the distortion measure compu-tation.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.329-344
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• 2007

In this paper, we shall propose the AMLEs of the scale parameter and the location parameter in the two-parameter Rayleigh distribution based on progressive Type-II censored samples when one parameter is known. We also propose the AMLEs of the two parameters in the Rayleigh distribution based on progressive Type-II censored samples when two parameters are unknown. We simulate the mean squared errors of the proposed estimators through Monte Carlo simulation for various censoring schemes.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.359-365
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• 1998

A global optimization method known as the Shuffled Complex Evolution method from the University of Arizona(SCE-UA) was used for calibrating a Tank model. The model was calibrated with error-free synthetic data, and the SCE-UA method was found to effectively search optimal parameters. Historical data from an agricultural watershed was used to calibrate and validate the model parameters. The simulated results were in good agreement with the observed.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.543-548
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• 2007

We present a dynamic Bayesian network (DBN) model of a generalized class of nonstationary birth-death processes. The model includes birth and death rate parameters that are randomly selected from a known discrete set of values. We present an on-line algorithm to obtain optimal estimates of the parameters. We provide a simulation of real-time characterization of load traffic estimation using our DBN approach.