• 한국수산과학회지
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• 제47권5호
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• pp.671-681
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• 2014

The distribution and abundance of coastal fish species in Asan Bay, Korea, were estimated from hydroacoustic survey and net catches. Acoustic data were collected with 38 and 200 kHz from July to October of 2012, and converted to the nautical area scattering coefficient (NASC, $m^2/mile^2$) for $0.25n{\cdot}mile$ along ten transects. Japanese anchovy Engraulis japonicus was the dominant specie in the net catches. The virtual echogram technique was used to distinguish E. japonicus from other species based on the differences in the mean volume backscattering strength (${\Delta}MVBS$) at 38 and 200 kHz. Engraulis japonicus and other fishes are mainly distributed in the center channel and outer part of Asan Bay. E. japonicus tends to move from inner to outer Asan Bay in summer and fall. From NASC data, the target strength and length-weight function of E. japonicus and other fishes were used to estimate the E. japonicus stock at 24.1-93.3 tons, and other fish at 40.6-88.4 tons from July to October 2012. The estimated anchovy biomass compared well with the cumulative catch weight from stow net catches. The hydroacoustic method offers an approach to understanding spatial/temporal structure and estimating the biomass of fish aggregations in coastal areas.

• Journal of Ecology and Environment
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• 제33권2호
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• pp.121-131
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• 2010

Betula platyphylla var. japonica and Betula ermanii segregate vertically at an elevation of approximately 1,850 m on Mt. Neko in Nagano Prefecture, Japan. B. platyphylla var. japonica and B. ermanii were the dominant species below and above this altitude, at which the mean-annual and growing-season air temperatures were $4^{\circ}C$ and $14.1^{\circ}C$, respectively. Based on a modification of Kira's warmth index which employs cumulative temperature represented as $^{\circ}C$ day, leaf unfolding in both species was observed to be initiated at $58^{\circ}C$ day and $169^{\circ}C$ day, respectively. In 1996, leaf unfolding was initiated on 18 May in B. platyphylla var. japonica (+/-6 days) and on 5 June in B. ermanii (+/-8 days), shortly after the last frost which occurred on 5 May 1995 above 1,850 m; below this elevation there was no risk of frost at the time. At elevations above 1,850 m, the unfolded leaves of B. platyphylla were damaged by late frost, while B. ermanii escaped injury because the leaves were still protected by winter buds. The optimum temperature for seed germination in both B. platyphylla and B. ermanii was $30^{\circ}C$. Temperature alternation from 10 to $30^{\circ}C$ and moist storage of seeds at $4^{\circ}C$ (stratification) prior to incubation increased germination rates in both species. The seedlings of B. ermanii had a greater survival rates than those of B. platyphylla var. japonica when planted above 1,850 m. Comparisons of the timing of leaf unfolding and the latest frost at a site appeared to be the main factors affecting the vertical distribution of these species.

• 응용통계연구
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• 제32권5호
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• pp.721-740
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• 2019

영상처리 분야에서 중요한 분야인 잡음 제거는 통계적인 접근이 필요하지만 잡음에 대한 특정한 분포를 가정하기 어려우며 지역적 특징을 반영하는 공간 필터는 소표본에 해당하므로 모수적인 방법으로 접근할 수 없다. 1차 영상 미분과 2차 영상 미분은 영상에 포함된 잡음 수준에 따라 확연한 차이를 보이며 캐니 에지 검출기를 사용하면 보다 명확히 알 수 있다. 잡음 수준을 통계적으로 확인하고자 Fligner-Killeen 검정을 진행하고 붓스트랩 방법을 사용하였으며 추정된 잡음의 수준을 베타분포의 누적분포함수를 이용하여 0과 1사이의 값을 갖도록 하였다. 본 연구에서는 영상에 포함된 잡음 수준을 고려하는 잡음 제거 알고리즘을 제시하고자 한다.

• 한국지반공학회논문집
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• 제28권1호
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• pp.17-27
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• 2012

압밀층 두께와 같은 지층 변수들은 공간적인 분포 추정 자체도 중요하지만 추정에 수반되는 불확실성을 정량적으로 평가하는 것도 중요하다. 본 연구에서는 송도신도시 압밀층 두께 추정결과의 국부적 불확실성을 지시자 방법을 이용하여 평가하였다. 지시자 방법을 이용하여 작성한 각 위치에서의 조건부 누적분포함수의 평균을 이용하여 송도신도시 압밀층 두께의 공간적 분포를 추정하였으며, 추정결과의 불확실성은 조건부 분산을 이용하여 평가할 수 있었다. 이러한 분석결과는 송도신도시 이차압축침하량의 공간적 분포추정과 추정결과의 불확실성 평가에 활용할 수 있었다.

• 한국지반공학회논문집
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• 제28권11호
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• pp.5-16
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• 2012

일부 지점에서 수행된 시추조사결과를 이용하여 미조사구간의 지층분포를 추정하는 경우, 예측하고자 하는 변수들의 공간적인 분포 추정뿐만 아니라 추정결과에 수반되는 불확실성을 정량적으로 평가하는 것도 중요하다. 본 논문에서는 송도신도시 풍화토층 출현심도 추정결과의 국부적 불확실성을 지시자 방법을 이용하여 평가하였다. 지시자 방법을 이용하여 작성한 각 위치에서의 조건부 누적분포함수의 평균을 이용하여 송도신도시 풍화토층 출현심도의 공간적 분포를 추정하였다. 또한, 조건부 누적분포함수와 손실함수를 이용하여 송도신도시의 최적 풍화토층 출현심도를 결정하였다. 본 논문에서 이용한 손실함수를 고려할 수 있는 설계방법이 지반공학분야에도 잘 적용될 수 있음을 확인하였다.

• 한국산림과학회지
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• 제89권3호
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• pp.306-309
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• 2000

우리나라 해안지역에 널리 분포하고 있는 해송에 극심한 피해를 주고 있는 솔껍질깍지벌레는 1963년 전남 고흥에서 최초로 발생된 것으로 추정되며, 그 후 계속 확산되어 현재 서해안과 남해안 지역으로 분포가 확대되었다. 본 연구는 1983년부터 1999년까지 임업연구원에서 수행한 발생선단지 조사 자료를 토대로 확산방향을 최초 발생지로부터 서해안쪽으로의 북향, 내륙지역으로의 동북향, 남해안쪽으로의 동향 등 3방향으로 구분하여 확산 유형을 분석하였다. 내륙으로의 확산은 해송의 밀도가 높은 곳에서는 연평균 약 4.3km의 확산속도를 보였지만 1990년대 초반부터 해송의 밀도가 낮아지는 곳에서는 속도가 매우 느려졌다. 해송의 밀도가 높은 서해안쪽 북향과 남해안쪽 동향은 확산속도가 각각 연평균 5.9, 3.3km로서 북향으로의 확산속도가 가장 높았다.

• Smart Structures and Systems
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• 제17권6호
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• 한국농공학회논문집
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• 제55권4호
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• pp.95-106
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• 2013

It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties of atmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need of bias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correction methods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillful techniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regional climate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2 scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the west central Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections) were examined and the variations among the results using different methods were evaluated in various ways. The results for daily temperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correction methods, monthly maximum values showed as significant variation (~2'C) as the mean differences between the retrospective simulations and future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected results appreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to be relevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variation among the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among other processes that may be required for cliamte change impact assessment. This study underscores the need to carefully select a bias-correction method and that the approach for any given analysis should depend on the research question being asked.

• 한국산학기술학회논문지
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• 제15권5호
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• pp.2603-2609
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• 2014

기계시스템의 신뢰성 해석을 위해서는 기계시스템에 성능을 미치는 변수의 확률 분포와 파라미터를 결정하는 통계적 모델링은 반드시 필요하다. 하지만, 신뢰성 해석에서 상당수의 변수는 상관관계가 있음에도 불구하고 독립변수로 취급되거나 실험데이터 수가 부족하다는 이유로 통계 모델에 대한 잘못된 가정을 하는 경우가 많다. 본 연구에서는 베이지안 방법을 이용하여 상관관계를 갖는 데이터의 결합분포함수를 copula를 이용하여 모델링함으로써 적은 수의 데이터로부터 정확한 입력모델을 산정하는 방법을 제안하였으며, 방법의 검증을 위해 다양한 상관계수와 데이터 수에 대해 통계 시뮬레이션을 수행하였다. 그 결과 Bayesian방법은 상관계수가 낮아 후보함수가 유사하거나 샘플수가 적어 정확한 모델을 산정하기 어려운 경우에도 후보 copula 중 실제 copula와 가장 근사한 후보 copula를 선정하였다. 이러한 근사 후보 copula는 신뢰성 해석결과 역시 실제 copula 함수를 이용한 신뢰성 해석 결과와 유사한 결과를 가짐을 확인할 수 있으므로 베이지안 방법은 신뢰성 해석을 위해 정확한 통계모델링을 제공함을 알 수 있다.

• 한국태양에너지학회 논문집
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• 제38권5호
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• pp.11-25
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• 2018

The Ln-least method is commonly used to estimate the Weibull parameters from the observed wind speed data. In previous studies, the bin method has been used to calculate the cumulative frequency distribution for the Ln-least method. The purpose of this study is to obtain better performance in the Ln-least method by applying probability plotting position(PPP) instead of the bin method. Two types of the wind speed data were used for the analysis. One was the observed wind speed data taken from three sites with different topographical conditions. The other was the virtual wind speed data which were statistically generated by a random variable with known Weibull parameters. Also, ten types of PPP formulas were applied which were Hazen, California, Weibull, Blom, Gringorten, Chegodayev, Cunnane, Tukey, Beard and Median. In addition, in order to suggest the most suitable PPP formula for estimating Weibull parameters, two accuracy tests, the root mean square error(RMSE) and $R^2$ tests, were performed. As a result, all of PPPs showed better performances than the bin method and the best PPP was the Hazen formula. In the RMSE test, compared with the bin method, the Hazen formula increased estimation performance by 38.2% for the observed wind speed data and by 37.0% for the virtual wind speed data. For the $R^2$ test, the Hazen formula improved the performance by 1.2% and 2.7%, respectively. In addition, the performance of the PPP depended on the frequency of low wind speeds and wind speed variability.