• 충청수학회지
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• 제24권3호
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• pp.401-407
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• 2011

The paper deals with characterization of standard Gumbel distribution and standard $Fr{\acute{e}}chet$ distribution and was motivated by [4], where the Weibull distribution is characterized. We present criterions using the independence of some suitable functions of lower records in a sequence of independent identically distributed random variables $\{X_n,\;n{\geq}1\}$.

• Journal of applied mathematics & informatics
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• 제37권3_4호
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• pp.245-249
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• 2019

In this paper we obtain characterizations of a family of continuous probability distribution by independence property of upper record values. Also, we introduce some examples of the characterizations of distributions from these general classes of continuous distributions.

• 충청수학회지
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• 제23권2호
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• pp.245-250
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• 2010

We present characterizations of the Weibull distribution by the independent property of record values that F(x) has a Weibull distribution if and only if $\frac{X_{U(m)}}{X_{U(n)}}$ and $X_{U(n)}$ or $\frac{X_{U(n)}}{X_{U(n)}{\pm}X_{U(m)}}$ and $X_{U(n)}$ are independent for $1{\leq}m.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.2215-2219
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• 2009

For model calibration in rainfall-runoff modeling, streamflow data at a specific outlet is obviously required but is not sufficient to identify parameters of a model since numerous parameter combinations can result in very similar model performance measures (i.e. objective functions) and indistinguishable simulated hydrographs. This phenomenon has been called 'equifinality' due to inherent parameter uncertainty involved in rainfall-runoff modeling. This study aims to investigate catchment responses in time and space to various uncertain parameter sets in distributed rainfall-runoff modeling. Seven plausible (or behavioral) parameter sets, which guarantee identically-good model performances, were sampled using deterministic and stochastic optimization methods entitled SCE and SCEM, respectively. Then, we applied them to a computational tracer method linked with a distributed rainfall-runoff model in order to trace and visualize potential origins of streamflow at a catchment outlet. The results showed that all hydrograph simulations based on the plausible parameter sets were performed equally well while internal catchment responses to them showed totally different aspects; different parameter values led to different distributions with respect to the streamflow origins in space and time despite identical simulated hydrographs. Additional information provided by the computational tracer method may be utilized as a complementary constraint for filtering out non-physical parameter set(s) (or reducing parameter uncertainty) in distributed rainfall-runoff modeling.

• 대한전자공학회논문지SP
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• 제48권2호
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• pp.134-140
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• 2011

압축 센싱 (Compressed Sensing) 기술을 통해 $M{\times}N$ 측정 행렬의 원소들이 특정의 독립적인 확률 분포에서 뽑혀 identically 분포의 성질을 가지고 있을 때 $M{\ll}N$의 경우에도 스파스 (sparse) 신호를 높은 확률로 정확하게 복원할 수 있다. $L_1$-최소화 알고리즘이 불완전한 측정에 대해서도 스파스 (sparse) 신호를 복원할 수 있다는 것은 잘 알려진 사실이다. 본 논문에서는 OMP를 변형시킨 support 검출과 가중치 기법을 이용한 $L_1$-최소화 방법을 통하여 스파스 (sparse) 신호의 복원 성능을 향상시키는 알고리즘을 제안하고자 한다.

• 한국전자파학회논문지
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• 제28권10호
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• pp.825-831
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• 2017

실제 레이다가 운용되는 환경에서 발생되는 클러터는 비균질성(heterogeneous)의 특성을 갖는 동시에 바이스태틱 레이다나 모노스태틱 non-sidelooking 레이다 구조인 경우는 클러터의 비정상성(nonstationary) 특성도 갖는다. 이러한 특성에 의해서 클러터 신호를 추정하는데 필요한 IID(Independent Identically Distributed) secondary 데이터 개수에 제약이 따르므로 클러터 억제 성능이 저하된다. 본 논문에서는 바이스태틱 레이다 환경에서 Cell under test에 대한 사전정보만을 이용하여 클러터 신호를 추정함으로써 secondary 데이터 없이 클러터를 억제하는 알고리즘을 제시한다. 바이스태틱 클러터의 angle-Doppler 스펙트럼 상에서 구조 분석을 통해 사전정보로 부터 클러터를 추정하는 것이 가능함을 보이고, 고유치 해석에 의해 클러터 억제 과정을 제시한다. 마지막으로 시뮬레이션을 통해 제시하는 클러터 억제 알고리즘의 성능을 보인다.

• 대한교통학회지
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• 제21권4호
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• pp.57-66
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• 2003

로짓모형은 선택대안에 대한 확률 계산이 용이하고, 설명변수의 파라메타 추정이 용이하기 때문에 교통 수단 선택모형으로 널리 쓰여지고 있다. 그러나 이러한 로짓모형은 수단선택 효용함수의 오차항 분포가 선택 대안간에 독립적이고, 그 분산이 동일하다는(IID:Independent and Identically Distributed)가정을 내포한다. 본 연구는 수단선택 효용오차의 분산이 수단간에 동일하다는 가정을 완화시키는 이분산 로짓모형 추정에 관한 연구이다. 수단선택 효용오차항의 동분산성을 극복함으로써 보다 현실적인 통행자의 수단선택행태를 반영하는 로짓모형을 추정하는데 본 연구의 목적이 있다. 이를 위해 로짓모형 오차항의 분산과 직접적인 관련이 있는 규모인자(scale factor)를 도입하였다. 이는 대중 교통과 승용차의 통행시간차이에 따른 이분산성을 고려하도록 정의되었으며, 이를 통행시간 파라메타 추정에 활용하였다. 본 연구에서 개발된 이분산 로짓모형의 추정 결과. 통행자의 통행시간이 증가하면서 대중교통수단과 승용차의 통행시간차이가 동일하더라도 통행자의 대중교통 수단선택확률이 차이를 보임으로 현실적인 통행자의 수단선택 행태를 반영하는 것으로 판명되었다.

• 한국해양공학회지
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• 제33권3호
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• pp.222-228
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• 2019

An extreme value analysis (EVA) is essential to obtain a design value for highly nonlinear variables such as long-term environmental data for wind and waves, and slamming or sloshing impact pressures. According to the extreme value theory (EVT), the extreme value distribution is derived by multiplying the initial cumulative distribution functions for independent and identically distributed (IID) random variables. However, in the position mooring of DNVGL, the sampled global maxima of the mooring line tension are assumed to be IID stochastic variables without checking their independence. The ITTC Recommended Procedures and Guidelines for Sloshing Model Tests never deal with the independence of the sampling data. Hence, a design value estimated without the IID check would be under- or over-estimated because of considering observations far away from a Weibull or generalized Pareto distribution (GPD) as outliers. In this study, the IID sampling data are first checked in an EVA. With no IID random variables, an automatic resampling scheme is recommended using the block maxima approach for a generalized extreme value (GEV) distribution and peaks-over-threshold (POT) approach for a GPD. A partial autocorrelation function (PACF) is used to check the IID variables. In this study, only one 5 h sample of sloshing test results was used for a feasibility study of the resampling IID variables approach. Based on this study, the resampling IID variables may reduce the number of outliers, and the statistically more appropriate design value could be achieved with independent samples.

• 한국시뮬레이션학회논문지
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• 제13권2호
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• pp.23-34
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• 2004

Regenerative simulation (RS) is a method of stochastic steady-state simulation in which output data are collected and analysed within regenerative cycles (RCs). Since data collected during consecutive RCs are independent and identically distributed, there is no problem with the initial transient period in simulated processes, which is a perennial issue of concern in all other types of steady-state simulation. In this paper, we address the issue of experimental analysis of the quality of sequential regenerative simulation in the sense of the coverage of the final confidence intervals of mean values. The ultimate purpose of this study is to determine the best version of RS to be implemented in Akaroa2 [1], a fully automated controller of distributed stochastic simulation in LAN environments.

• 인터넷정보학회논문지
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• 제24권6호
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• pp.1-11
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• 2023

Federated learning (FL) is a ground breaking machine learning paradigm that allow smultiple participants to collaboratively train models in a cloud environment, all while maintaining the privacy of their raw data. This approach is in valuable in applications involving sensitive or geographically distributed data. However, one of the challenges in FL is dealing with heterogeneous and non-independent and identically distributed (non-IID) data across participants, which can result in suboptimal model performance compared to traditionalmachine learning methods. To tackle this, we introduce FedGCD, a novel FL algorithm that employs Graph Neural Network (GNN)-based community detection to enhance model convergence in federated settings. In our experiments, FedGCD consistently outperformed existing FL algorithms in various scenarios: for instance, in a non-IID environment, it achieved an accuracy of 0.9113, a precision of 0.8798,and an F1-Score of 0.8972. In a semi-IID setting, it demonstrated the highest accuracy at 0.9315 and an impressive F1-Score of 0.9312. We also introduce a new metric, nonIIDness, to quantitatively measure the degree of data heterogeneity. Our results indicate that FedGCD not only addresses the challenges of data heterogeneity and non-IIDness but also sets new benchmarks for FL algorithms. The community detection approach adopted in FedGCD has broader implications, suggesting that it could be adapted for other distributed machine learning scenarios, thereby improving model performance and convergence across a range of applications.