• Journal of Information Processing Systems
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• v.8 no.3
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• pp.421-436
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• 2012

Different types of fingerprint detection algorithms that are based on extraction of minutiae points are prevalent in recent literature. In this paper, we propose a new algorithm to locate the virtual core point/centroid of an image. The Euclidean distance between the virtual core point and the minutiae points is taken as a random variable. The mean, variance, skewness, and kurtosis of the random variable are taken as the statistical parameters of the image to observe the similarities or dissimilarities among fingerprints from the same or different persons. Finally, we verified our observations with a moment parameter-based analysis of some previous works.

• Journal of Ship and Ocean Technology
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• v.12 no.2
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• pp.1-15
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• 2008

The main idea of this paper introduce stochastic structural parameters and random dynamic excitation directly into the dynamic functional variational formulations, and developed the nonlinear dynamic analysis of a stochastic variational principle and the corresponding stochastic finite element method via the weighted residual method and the small parameter perturbation technique. An interpolation method was adopted, which is based on representing the random field in terms of an interpolation rule involving a set of deterministic shape functions. Direct integration Wilson-${\theta}$ Method was adopted to solve finite element equations. Numerical examples are compared with Monte-Carlo simulation method to show that the approaches proposed herein are accurate and effective for the nonlinear dynamic analysis of structures with random parameters.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.91-95
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• 2004

Moments of skew t random vectors and their quadratic forms are derived. It is shown that the moments of the sample autocovariance function and of the sample variogram estimator do not depend on a skewness parameter.

• Proceedings of the Korean Reliability Society Conference
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• 2006.05a
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• pp.57-57
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• 2006

• Transactions of Materials Processing
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• v.29 no.4
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.6
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• pp.789-799
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• 2007

In this paper, we propose a stochastic finite element formulation which takes into account the randonmess in the material and geometrical parameters. The formulation is proposed for plate structures, and is based on the weighted integral approach. Contrary to the case of elastic modulus, plate thickness contributes to the stiffness as a third-order function. Furthermore, Poisson's ratio is even more complex since this parameter appears in the constitutive relations in the fraction form. Accordingly, we employ Taylor's expansion to derive decomposed stochastic field functions in ascending order. In order to verify the proposed formulation, the results obtained using the proposed scheme are compared with those in the literature and those of Monte Carlo analysis as well.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.87-96
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• 2019

Purpose: The purpose of this paper is to determine an optimal production time for economic production quantity model with preventive maintenance and random defective rate as the function of a machinery deteriorates. Methods: If a machinery shifts from "in-control" state to "out-of-control" state, a proportion of defective items being produced increases. It is assumed that time to state shift is a random variable and follows an arbitrary distribution. The elapsed time until process shift decreases stochastically as a production cycle repeats and quasi-renewal process is used to implement for production facilities to deteriorate. Results: When the exponential parameter for exponential distribution increases, the optimal production time increases. When Weibull distribution is considered, the optimal production time is closely affected by the shape parameter of Weibull distribution. Conclusion: A mathematical model is suggested to find optimal production time and optimal number of production cycles and numerical examples are implemented to validate the patterns for changes of optimal times under different parameters assumptions. The real application is implemented using the proposed approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.185-201
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• 2023

Grant-free random access (GFRA) can reduce the access delay and signaling cost, and satisfy the short transmission packet and strict delay constraints requirement in internet of things (IoT). IoT is a major trend in the future, which is characterized by the variety of applications and devices. However, most existing studies on GFRA only consider a single type of device and omit the effect of access delay. In this paper, we study GFRA in multicell massive multipleinput multiple-output (MIMO) systems where different types of devices with various configurations and requirements co-exist. By introducing the backoff mechanism, each device is randomly activated according to the backoff parameter, and active devices randomly select an orthogonal pilot sequence from a predefined pilot pool. An analytical approximation of the average spectral efficiency for each type of device is derived. Based on it, we obtain the optimal backoff parameter for each type of devices under their delay constraints. It is found that the optimal backoff parameters are closely related to the device number and delay constraint. In general, devices that have larger quantity should have more backoff time before they are allowed to access. However, as the delay constraint become stricter, the required backoff time reduces gradually, and the device with larger quantity may have less backoff time than that with smaller quantity when its delay constraint is extremely strict. When the pilot length is short, the effect of delay constraints mentioned above works more obviously.

• Korean Journal of Cognitive Science
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• v.31 no.1
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• pp.1-23
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• 2020

This paper introduces SNUDM, an analysis program for Ratcliff's diffusion model, which has been one of the most important models in cognitive psychology over the past 35 years and which has come to occupy an important place in cognitive neuroscience in recent years. The analysis tool is designed with the basic principles of easy comprehension and simplicity in use. A diffusion process was programmed as the limit of a simple random walk in a manner resembling Ratcliff & Tuerlinckx(2002). The response time distribution of the model was constructed by simulating the time taken by a random walk until it reaches a threshold with small steps. The optimal parameter values in the model are found to be the smallest value of the chi-square values obtained by comparing the resulting distribution and the experimental data using Simplex method. For simplicity and ease of use, the input file used here is created as a file containing the quantile of the reaction time, the trials and other information. The number of participants and the number of conditions required for such work programs are given in a way that answers the question. Using this analysis tool, the experimental data of Ratcliff, Gomez, & McKoon(2004) were analyzed. We found the very similar pattern of parameter values to Ratcliff et al.(2004) found. When comparing DMAT, fast-dm and SNUDM with the generated data, we found that when the number of trials is small, SNUDM estimates the boundary parameter to a value similar to fast-dm and less than the DMAT. In addition, when the number of trials was large, it was confirmed that all three tools estimate parameters similarly.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.899-914
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• 2003

It is generally accepted that self-similar (or fractal) processes may provide better models for teletraffic in modern telecommunication networks than Poisson Processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. Three generators of pseudo-random self-similar sequences, based on the FFT〔20〕, RMD〔12〕 and SRA methods〔5, 10〕, are compared and analysed in this paper. Properties of these generators were experimentally studied in the sense of their statistical accuracy and times required to produce sequences of a given (long) length. While all three generators show similar levels of accuracy of the output data (in the sense of relative accuracy of the Horst parameter), the RMD- and SRA-based generators appear to be much faster than the generator based on FFT. Our results also show that a robust method for comparative studies of self-similarity in pseudo-random sequences is needed.