• Journal of the Korea Society of Computer and Information
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• v.21 no.8
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• pp.77-84
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• 2016

In general statistical analysis, we need to make a normal assumption. If this assumption is not satisfied, we cannot expect a good result of statistical data analysis. Most of statistical methods processing the outlier and noise also need to the assumption. But the assumption is not satisfied in big data because of its large volume and heterogeneity. So we propose a methodology based on box-plot and data smoothing for controling outlier and noise in big data analysis. The proposed methodology is not dependent upon the normal assumption. In addition, we select patent documents as target domain of big data because patent big data analysis is a important issue in management of technology. We analyze patent documents using big data learning methods for technology analysis. The collected patent data from patent databases on the world are preprocessed and analyzed by text mining and statistics. But the most researches about patent big data analysis did not consider the outlier and noise problem. This problem decreases the accuracy of prediction and increases the variance of parameter estimation. In this paper, we check the existence of the outlier and noise in patent big data. To know whether the outlier is or not in the patent big data, we use box-plot and smoothing visualization. We use the patent documents related to three dimensional printing technology to illustrate how the proposed methodology can be used for finding the existence of noise in the searched patent big data.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.453-456
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• 2005

It is known that the classical Fatou's lemma and Lebesgue convergence theorem do not require the assumption that J1. is finite. In this note, we show that the assumption $\mu$(X) < $\infty$ cannot be replaced with a weaker assumption to prove Fatou's lemma and Lebesgue convergence theorem for a sequence of set-valued measurable function suggested by Zhang and Wang (Fuzzy Sets and Systems 56(1993) 237-241).

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.81-83
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• 1998

ATMS(Assumption-based Truth Maintenance System)는 추론기관의 추론 과정을 기억하고 각 추론 상태의 진위를 관리해주는 기능을 수행한다. ATMS는 JTMS나 LTMS와는 다르게 각 노드의 레이블과 Nogood들을 관리함으로써, 추론기관의 추론에 모순(Contradiction)이 발생하였을 때 이를 효과적으로 처리해준다. 기존의 ATMS는 모순에 영향을 주는 가정(Assumption)을 제거(Retract)함으로써 모순에 영향을 주는 원인을 제거하는 방식을 취하고 있다. 그러나, 본 논문에서는 이와 같은 방식으로 문제가 해결되지 못하는 새로운 종류의 모순을 설명하고 이를 처리하기 위해서는 ATMS가 추론기관과 연동하여 모순을 처리하는 방식에 대해서 서술하고자한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.169-172
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• 2006

Most studies using state-space models have been conducted under the assumption of independently distributed noises in measurement and state equation without adequate verification of the assumption. To avoid the improper use of state-space model, testing the assumption prior to the parameter estimation of state-space model is very important. The purpose of this paper is to investigate the general relationship between parameters of state-space models and those of ARIMA processes. Under the assumption, we derive restricted parameter spaces of ARIMA(p,0,p-1) models with mutually different AR roots where $p\;{\le}\;5$. In addition, the results of ARIMA(p,0,p-1) case can be expanded to more general ARIMA models, such as ARIMA(p-1,0,p-1), ARIMA(p-1,1,p-1), ARIMA(p,0,p-2) and ARIMA(p-1,1,p-2).

• Journal of applied mathematics & informatics
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• v.24 no.1_2
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• pp.461-470
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• 2007

In CRYPTO 1995, Bellare, $Gu\'{e}rin$, and Rogaway proposed a very efficient message authentication scheme. This scheme is secure against adaptive chosen message attacks, under the assumption that its underlying primitive is a pseudorandom function. This article studies how to weaken that assumption. For an adaptive chosen message attack, we take into account two scenarios. On the one hand, the adversary intercepts the authenticated messages corresponding to messages chosen adaptively by herself, so the verifier does not receive them. On the other hand, the adversary can only eavesdrop the authenticated messages corresponding to messages chosen adaptively by herself, so the verifier receives them. We modify the original scheme. In the first scenario, our scheme is secure if the underlying primitive is a pseudorandom function. In the second scenario, our scheme is still secure under a weaker assumption that the underlying primitive is an indistinguishable-uniform function.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.441-447
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• 2014

Background: Multi-state models are appropriate for cancer studies such as gastrectomy which have high mortality statistics. These models can be used to better describe the natural disease process. But reaching that goal requires making assumptions like Markov and homogeneity with time. The present study aims to investigate these hypotheses. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. To assess Markov assumption and time homogeneity in modeling transition rates among states of multi-state model, Cox-Snell residuals, Akaikie information criteria and Schoenfeld residuals were used, respectively. Results: The assessment of Markov assumption based on Cox-Snell residuals and Akaikie information criterion showed that Markov assumption was not held just for transition rate of relapse (state 1 ${\rightarrow}$ state 2) and for other transition rates - death hazard without relapse (state 1 ${\rightarrow}$ state 3) and death hazard with relapse (state 2 ${\rightarrow}$ state 3) - this assumption could also be made. Moreover, the assessment of time homogeneity assumption based on Schoenfeld residuals revealed that this assumption - regarding the general test and each of the variables in the model- was held just for relapse (state 1 ${\rightarrow}$ state 2) and death hazard with a relapse (state 2 ${\rightarrow}$ state 3). Conclusions: Most researchers take account of assumptions such as Markov and time homogeneity in modeling transition rates. These assumptions can make the multi-state model simpler but if these assumptions are not made, they will lead to incorrect inferences and improper fitting.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1045-1063
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• 2008

This paper derives conversion formulas from yearly-based absolute rates of decrements to monthly-based rates of decrement due to cause j under FI (fractional age independence) assumption that is a generalization of UDD assumption. Next, it suggests conversion formulas from monthly-based absoluterates of decrements to monthly-based rates of decrement due to cause j under FI assumption. In addition, it calculates conversion formulas from yearly-based rates of decrement due to cause j to the corresponding monthly-based absolute rates of decrements under FI assumption. Some numerical examples are discussed.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.173-179
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• 2018

Estimating reliability of a non-repairable system using the degradation data, variance assumption such as homogeneity (constant) or heteroscedasticity (time-variant) could affect accuracy of reliability estimation. This paper showed reliability estimation and comparison results under normal conditions using accelerated degradation data obtained from destructive measurements, according to variance assumption of the data at each measurement time. Degradation data from three accelerated conditions with stress factors of temperature and humidity were used to estimate reliability. The $B_{10}$ lifetime was estimated as 1243.8 years by constant variance assumption, and 18.9 years by time-variant variance. And variance assumption provided different analysis results of important stresses to reliability. Thus, accurate assumption of variance at each measurement time is required when estimating reliability using degradation data of a non-repairable system.

• Journal of Broadcast Engineering
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• v.18 no.1
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• pp.21-30
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• 2013

It is a challenging problem to most of the image processing when the light source is unknown. The color of the light source must be estimated in order to compensate color changes. To estimate the color of the light source, additional assumption is need, so that we assumed color distribution according to the light source. If the pixels, which do not satisfy the assumption, are used, the estimation fails to provide an accurate result. The most popular color distribution assumption is Grey-World Assumption (GWA); it is the assumption that the color in each scene, the surface reflectance averages to gray or achromatic color over the entire images. In this paper, we analyze the characteristics of the camera response function, and the effect of the Grey-World Assumption on the pixel value and chromaticity, based on the inherent characteristics of the light source. Besides, we propose a novel method that detects important pixels for the color estimation of the light source. In our method, we firstly proposed a method that gives weights to pixels satisfying the assumption. Then, we proposed a pixel detection method, which we modified max-RGB method, to apply on the weighted pixels. Maximum weighted pixels in the column direction and row direction in one channel are detected. The performance of our method is verified through demonstrations in several real scenes. Proposed method better accurately estimate the color of the light than previous methods.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.5 no.1
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• pp.11-24
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• 2001

We introduce an assumption about smoothing operator for mixed formulations and show that convergence of Multigrid method for the mixed finite element formulation for the Linear Elasticity. And we show that Richardson and Kaczmarz smoothing satisfy this assumption.