• Journal of the Korean Society for Precision Engineering
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• v.7 no.2
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• pp.85-94
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• 1990

In automatic metal cuttings, the chip control is one of the serious problems. So the automatic detection of chip forms is essential to the chip control in automatic metal cuttings. Cutting experiments were carried out under the variety of cutting conditions (cutting speed, feed, depth of cut and tool geometry) and with workpiece made of steel (S45C), and cutting forces were measured in-processing by using a piezoelectric type Tool Dynamometer. In this report, the frequency analysis of dynamic components, the upper frequency distributions, the ratio of RMS values, the numbers of null point and the probability density were calculated from the dynamic componeents of cutting forces filtered through various band pass filters. Experimental results showed that computer chip form monitoring system based on the cutting forces was designed and simulated and that 6 type of chip forms could be detected while in-process machining.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.273-293
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• 2019

We consider the problem of model selection in multiple linear regression with outliers and non-normal error distributions. In this article, the robust model selection criterion is proposed based on the robust estimation method with the least absolute deviation (LAD). The proposed criterion is shown to be consistent. We suggest proposed criterion based algorithms that are suitable for a large number of predictors in the model. These algorithms select only relevant predictor variables with probability one for large sample sizes. An exhaustive simulation study shows that the criterion performs well. However, the proposed criterion is applied to a real data set to examine its applicability. The simulation results show the proficiency of algorithms in the presence of outliers, non-normal distribution, and multicollinearity.

• Structural Engineering and Mechanics
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• v.70 no.1
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• pp.81-96
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• 2019

In this paper, a hybrid uncertain model is applied to system reliability based design optimization (RBDO) of trusses. All random variables are described by random distributions but some key distribution parameters of them which lack information are defined by variation intervals. For system RBDO of trusses, the first order reliability method, as well as monotonicity analysis and the branch and bound method, are utilized to determine the system failure probability; and Improved (${\mu}+{\lambda}$) constrained differential evolution (ICDE) is employed for the optimization process. System reliability assessment of several numerical examples and system RBDO of different truss structures are proposed to verify our results. Moreover, the effect of different classes of interval distribution parameters on the optimum weight of the structure and the reliability index are also investigated. The results indicate that the weight of the structure is increased by increasing the uncertainty level. Moreover, it is shown that for a certain random variable, the optimum weight is more increased by the translation interval parameters than the rotation ones.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.52.2-52.2
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• 2018

The shape of a galaxy's spectral energy distribution ranging from ultraviolet (UV) to infrared (IR) wavelengths provides crucial information about the underlying stellar populations, metal contents, and star-formation history. Therefore, analysis of the SED is the main means through which astronomers study distant galaxies. However, interstellar dust absorbs and scatters UV and optical light, re-emitting the absorbed energy in the mid-IR and Far-IR. I present the updated 3D Monte-Carlo radaitive transfer code MoCafe to compute the radiative transfer of stellar, dust emission through a dusty medium. The code calculates the emission expected from dust not only in pure thermal equilibrium state but also in non-thermal equilibrium state. The stochastic heating of very small dust grains and/or PAHs is calculated by solving the transition probability matrix equation between different vibrational, internal energy states. The calculation of stochastic heating is computationally expensive. A pilot study of radiative transfer models of SEDs in clumpy (turbulent), galactic environments, which has been successfully used to understand the Calzetti attenuation curves in Seon & Draine (2016), is also presented.

• Journal of applied mathematics & informatics
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• v.39 no.5_6
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• pp.725-742
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• 2021

The aim of this article is to estimate the coefficient bounds of certain subclasses of analytic functions. We claim that this is a novel and unique effort in combining the coefficient functional along with the new domains and the probability distributions which have not been found or are available in the literature of coefficients bounds. Here the authors analyze these bounds in the special domains associated with exponential function and sine function. Further we obtain Fekete-Szegö inequalities for the defined subclasses of analytic functions defined through Poisson distribution series and Pascal distribution series.

• Journal of information and communication convergence engineering
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• v.18 no.4
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• pp.267-277
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• 2020

It is important to understand the exact habitat distribution of endangered species because of their decreasing numbers. In this study, we build a system with a deep learning module that collects the image data of endangered animals, processes the data, and saves the data automatically. The system provides a more efficient way than human effort for classifying images and addresses two problems faced in previous studies. First, specious answers were suggested in those studies because the probability distributions of answer candidates were calculated even if the actual answer did not exist within the group. Second, when there were more than two entities in an image, only a single entity was focused on. We applied an object detection algorithm (YOLO) to resolve these problems. Our system has an average precision of 86.79%, a mean recall rate of 93.23%, and a processing speed of 13 frames per second.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.131-134
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• 2023

The age of information (AoI) was proposed to quantify the freshness of information about the status of a remote source system, which is defined as the amount of time that has elapsed since a packet was created at its source. This paper analyzes the age of information of a discrete time Geo/D/1/1 status update system. For this purpose, the system is modeled as a discrete-time two-state Markov chain. The stationary probability distributions for peak AoI and AoI are obtained. The average peak AoI, the average AoI, and the freshness ratio of information are also derived. Some numerical results of the analysis are presented.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.1-16
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• 2023

Synthetic data generation is generally used in performance evaluation and function tests in data-intensive applications, as well as in various areas of data analytics, such as privacy-preserving data publishing (PPDP) and statistical disclosure limit/control. A significant amount of research has been conducted on tools and languages for data generation. However, existing tools and languages have been developed for specific purposes and are unsuitable for other domains. In this article, we propose a regular expression-based data generation language (DGL) for flexible big data generation. To achieve a general-purpose and powerful DGL, we enhanced the standard regular expressions to support the data domain, type/format inference, sequence and random generation, probability distributions, and resource reference. To efficiently implement the proposed language, we propose caching techniques for both the intermediate and database queries. We evaluated the proposed improvement experimentally.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.259-272
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• 2023

In this paper, we suggest a new method for the prediction of sharp changes in particulate matter (PM₁₀) using quantile mapping. To predict the current PM₁₀ density in Seoul, we consider PM₁₀ and precipitation in Baengnyeong and Ganghwa monitoring stations observed a few hours before. For the PM₁₀ distribution estimation, we use the extreme value mixture model, which is a combination of conventional probability distributions and the generalized Pareto distribution. Furthermore, we also consider a quantile generalized additive model (QGAM) for the relationship modeling between precipitation and PM₁₀. To prove the validity of our proposed model, we conducted a simulation study and showed that the proposed method gives lower mean absolute differences. Real data analysis shows that the proposed method could give a more accurate prediction when there are sharp changes in PM₁₀ in Seoul.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.53.4-53.4
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• 2019

We reconstruct the underlying dark matter (DM) density distribution of the local universe within 20Mpc/h cubic box by using the galaxy position and peculiar velocity. About 1,000 subboxes in the Illustris-TNG cosmological simulation are used to train the relation between DM density distribution and galaxy properties by using UNet-like convolutional neural network (CNN). The estimated DM density distributions have a good agreement with their truth values in terms of pixel-to-pixel correlation, the probability distribution of DM density, and matter power spectrum. We apply the trained CNN architecture to the galaxy properties from the Cosmicflows-3 catalogue to reconstruct the DM density distribution of the local universe. The reconstructed DM density distribution can be used to understand the evolution and fate of our local environment.