• Korean Journal of Mathematics
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• v.27 no.1
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• pp.1-8
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• 2019

We will deal with an n locus model in which mutation and gene conversion are taken into consideration. Also random partitions of the number n determined by chromosomes with n loci should be investigated. The diffusion process describes the time evolution of distributions of the random partitions. In this paper, we find the probability of distribution of the diffusion process with special diffusion operator $L_1$ and we show that the average probability of genes at different loci on one chromosome can be described by the rate of gene frequency of mutation and gene conversion.

• Journal of Ocean Engineering and Technology
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• v.7 no.2
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• pp.114-120
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• 1993

Responses of marine vehicles and ocean structures in a seaway can be predicted by applying the probabilistic approach. When we consider a linear system, the responses in a random seaway can be evaluated through spectral analysis in the frequency domain. But when we treat nonlinear system in irregular waves, it is necessary to get time history of waves. In the previous study we introduced one-directional waves (long crested waves)as wave environment and carried out calculations and experiments in the waves. But the real sea in which marine vehicles and structures are operated has multi-directional waves (short crested waves). It is important to get a simulated random sea and analyse dynamic problems in the sea. We need entire sample function or probabillty density function to infer statistical value of random process. However if the process are ergodic process, we can get statistical values by analysis of one sample function. In this paper, we developed the simulation technique of multi-directional waves and proved that the time history given by this method keep ergodic characteristics by the statistical analysis.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.4-12
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• 1996

In this study, we examine the applicability of an artificial neural network(ANN) for filtering underwater random noise and for identifying underlying signals taken from noisy environment. The approach is to find a way of compressing the input data and then decompressing it using an ANN as in image compressing process. It is well known that random signal is hard to compress while ordered information is not. The use of a limited number of processing elements(PEs) in the hidden layer of an Ann ensures that some of the noise would be removed in the reconstruction process. Two types of the signals, synthesized and measured, are used to examine the effectiveness of the ANN-based filter. After training process is completed, the ANN successfully extracts the underlying signals form the synthesized or measured noisy signals. In particular, compared with the results form without filtering or moving averaged, the ANN-based filter gives much better spectrograms to identify underlying signals from the measured noisy data. This filtering process is achieved without using and kind of highly accurate signal processing technique. More experimentation needs to be followed to develop the ANN-based filtering technique to the level of complete understanding.

• Communications of the Korean Mathematical Society
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• v.12 no.3
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• pp.725-741
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• 1997

This paper considers the problem of deriving exponential probability inequalities for product symmetric Poisson processes. As an application they are used to show the existence of regular version of some product process derived from L$\acute{e}$vy process.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.963-972
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• 2007

When a control chart signals that a special cause is present, process engineers must initiate a search for and an identification of the special cause. Knowing the time of the process change could lead to identify the special cause more quickly, and to take the appropriate actions immediately to improve quality. In this paper, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart is used in monitoring the parameters of a process in which the observations can be modeled as a first-order autoregressive(AR(1)) process plus an additional random error.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.147-156
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• 2022

Industrial control systems that manage and maintain various industries were mainly operated in closed environment without external connection, but with the recent development of the Internet and the introduction of ICT technology, the access to the industrial control system of external or attackers has become easier. Such incorrect approaches or attacks can undermine the availability, a major attribute of the industrial control system, and violation of availability can cause great damage. In this paper, when issuing commands in an industrial control system, a verification group is formed using a random list to verify and execute commands, and a trust score technique is introduced that applies feedback to the verification group that conducted verification using the command execution result. This technique can reduce overhead generated by random generation in the process of requesting command verification, give flexibility to the verification process, and ensure system availability. For the performance analysis of the system, we measured the time and gas usage when deploying a smart contract, gas usage when verifying a command. As a result, we confirmed that although the proposed system generates a random list compared to the legacy system, there was little difference in the time when it took to deploy smart contract and that the gas used to deploy smart contract increased by about 1.4 times in the process of generating a random list. However, the proposed system does not perform random operations even though the operation of command verification and confidence score technique is performed together during the command verification process, thus it uses about 9% less gas per verification, which ensures availability in the verification process.

• Bulletin of the Korean Mathematical Society
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• v.35 no.3
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• pp.485-490
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• 1998

We consider a class of discrete parameter processes on a locally compact Polish space $S$ arising from successive compositions of strictly stationary Markov random maps on $S$ into itself. Sufficient conditions for the existence of the stationary solution and the weak convergence of the distributions of $\{\Gamma_n \Gamma_{n-1} \cdots \Gamma_0x \}$ are given.

• Journal of Korea Multimedia Society
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• v.20 no.10
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• pp.1671-1677
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• 2017

In this paper, we propose a machine learning method for random noise removal in image deblurring. The proposed method uses a logistic regression to select reliable data to use them, and, at the same time, to exclude data, which seem to be corrupted by random noise, in the deblurring process. The proposed method uses commonly available images as training data. Simulation results show an improved performance of the proposed method, as compared with the median filtering based reliable data selection method.

• Journal of applied mathematics & informatics
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• v.12 no.1_2
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• pp.299-305
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• 2003

The central limit theorems are obtained for stationary linear processes of the form Xt = (equation omitted), where {$\varepsilon$t} is a strictly stationary sequence of random variables which are either linearly positive quad-rant dependent or associated and {aj} is a sequence of .eat numbers with (equation omitted).

• Journal of the Korean Ceramic Society
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• v.56 no.6
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• pp.596-600
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• 2019

Artificial intelligence with a sufficient amount of realistic big data in certain applications has been demonstrated to play an important role in designing new materials or in manufacturing high-quality products. To reduce cracks in ceramic products using machine learning, it is desirable to utilize big data in recently developed data-driven optimization schemes. However, there is insufficient big data for ceramic processes. Therefore, we developed a numerical algorithm to make "virtual" manufacturing data sets using indirect methods such as computer simulations and image processing. In this study, a numerical algorithm based on the random walk was demonstrated to generate images of cracks by adjusting the conditions of the random walk process such as the number of steps, changes in direction, and the number of cracks.