• 한국환경과학회지
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• 제15권8호
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• pp.701-718
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• 2006

The average ratio of the daily UV-B to total solar (75) irradiance at Busan (35.23$^{\circ}$N, 129.07$^{\circ}$E) in Korea is found as 0.11%. There is also a high exponential relationship between hourly UV-B and total solar irradiance: UV-B=exp (a$\times$(75-b))(R$^2$=0.93). The daily variation of total ozone is compared with the UV-B irradiance at Pohang (36.03$^{\circ}$N, 129.40$^{\circ}$E) in Korea using the Total Ozone Mapping Spectrometer (TOMS) data during the period of May to July in 2005. The total ozone (TO) has been maintained to a decreasing trend since 1979, which leading to a negative correlation with the ground-level UV-B irradiance doting the given period of cloudless day: UV-B=239.23-0.056 TO (R$^2$=0.52). The statistical predictions of daily total ozone are analyzed by using the data of the Brewer spectrophotometer and TOMS in East Asia including the Korean peninsula. The long-term monthly averages of total ozone using the multiplicative seasonal AutoRegressive Integrated Moving Average (ARIMA) model are used to predict the hourly mean UV-B irradiance by interpolating the daily mean total ozone far the predicting period. We also can predict the next day's total ozone by using regression models based on the present day's total ozone by TOMS and the next day's predicted maximum air temperature by the Meteorological Mesoscale Model 5 (MM5). These predicted and observed total ozone amounts are used to input data of the parameterization model (PM) of hourly UV-B irradiance. The PM of UV-B irradiance is based on the main parameters such as cloudiness, solar zenith angle, total ozone, opacity of aerosols, altitude, and surface albedo. The input data for the model requires daily total ozone, hourly amount and type of cloud, visibility and air pressure. To simplify cloud effects in the model, the constant cloud transmittance are used. For example, the correlation coefficient of the PM using these cloud transmissivities is shown high in more than 0.91 for cloudy days in Busan, and the relative mean bias error (RMBE) and the relative root mean square error (RRMSE) are less than 21% and 27%, respectively. In this study, the daily variations of calculated and predicted UV-B irradiance are presented in high correlation coefficients of more than 0.86 at each monitoring site of the Korean peninsula as well as East Asia. The RMBE is within 10% of the mean measured hourly irradiance, and the RRMSE is within 15% for hourly irradiance, respectively. Although errors are present in cloud amounts and total ozone, the results are still acceptable.

• 지질공학
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• 제24권4호
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• pp.643-648
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• 2014

터널의 굴착과정에서 막장전반부에 분포하는 절리를 예측하여 그들로 인한 붕괴를 예방하기위해 보강계획을 수립하는 것은 매우 중요한 사안이다. 그러나 빠른 굴착공정에서 절리의 분포를 충분히 조사하고 예측하는 것은 쉽지 않은 일이다. 본 연구는 굴착면 선단부에 분포하는 절리를 예측할 수 있는 새로운 통계적 기법을 제시하고자 한다. 제시될 방법은 단일 절리군에 대한 절리간격의 누적분포도를 이용한 절리분포의 예측이다. 누적분포도는 수평축에 절리를 동일간격으로 순차적 배열을 한 후, 수직축에 누적간격을 기준으로 각 절리를 하나의 점으로 표기한다. 이 도표에서, 표기된 점들이 선형을 이루면 절리의 분포양상이 규칙적임을 의미하며, 직선의 기울기는 절리의 간격을 의미한다. 기울기가 낮으면 절리사이의 간격이 적은 것 이다. 점들의 분포가 군집형을 이루면 이는 절리의 분포양상이 군집형을 이룸을 의미한다. 현장에서 조사된 자료를 누적분포도에 점기하면 특정 절리군에 대한 분포양상이 분포도에 표기될 것이고, 이 분포양상을 연장하면 앞으로 굴착될 굴착면 전방의 절리분포를 예측할 수 있을 것이다. 실 터널현장에서 10 m 간격에서 측정된 특정 절리군에 대한 누적분포도 분석이 수행되었으며 이를 기반으로 3 m 전방의 미 굴착구간에 대한 절리분포가 예측되었다. 예측결과를 실제 현장자료와 비교한 결과 누적분포도는 전방절리를 적절히 예측하고 있었다. 분포도의 특성상 점기된 절리들의 선형과 등간격의 군집형태는 그 자체로 절리의 분포가 규칙적이며 누적분포도의 예측이 정확할 수 있음을 의미하는 것이다. 그러므로 본 연구는 향후 누적분포도의 분포양상에 대한 고찰과 그 결과의 넓은 공유가 있기를 바란다.

• Journal of Mechanical Science and Technology
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• 제17권6호
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• pp.825-835
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• 2003

Previous studies have demonstrated the importance of joint angle errors mainly due to skin artifact and measurement errors during gait analysis. Joint angle errors lead to unreliable kinematics and kinetic analyses in the investigation of human motion. The purpose of this paper is to present the Joint Averaging Coordinate System (JACS) method for human gait analysis. The JACS method is based on the concept of statistical data reduction of anatomically referenced marker data. Since markers are not attached to rigid bodies, different marker combinations lead to slightly different predictions of joint angles. These different combinations can be averaged in order to provide a "best" estimate of joint angle. Results of a gait analysis are presented using clinically meaningful terminology to provide better communication with clinical personal. In order to verify the developed JACS method, a simple three-dimensional knee joint contact model was developed, employing an absolute coordinate system without using any kinematics constraint in which thigh and shank segments can be derived independently. In the experimental data recovery, the separation and penetration distance of the knee joint is supposed to be zero during one gait cycle if there are no errors in the experimental data. Using the JACS method, the separation and penetration error was reduced compared to well-developed existing methods such as ACRS and Spoor & Veldpaus method. The separation and penetration distance ranged up to 15 mm and 12 mm using the Spoor & Veldpaus and ACRS method, respectively, compared to 9 mm using JACS method. Statistical methods like the JACS can be applied in conjunction with existing techniques that reduce systematic errors in marker location, leading to an improved assessment of human gait.

• 응용통계연구
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• 제19권3호
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• pp.505-519
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• 2006

셀 수 있는 이산 자료(discrete count data)에 대한 분석은 여러 분야에서 활용되고 있지만 영(zero)을 과도하게 포함하고 있는 영과잉 자료는 자료의 성격상 포아송 분포를 따르지 못할 때가 있어 분석에 어려움이 따른다. Zero-Inflated Poisson(ZIP)모형은 이런 어려움을 극복하기 위하여 영에 대한 점확률을 가지는 분포와 포아송 분포를 합성하여 과도한 영과 영이 아닌 자료를 설명하는 모형이다. 설명 변수가 존재할 때는 포아송 분포 부분에서 반응변수의 평균과 공변량사이에 로그선형 연결함수를 사용한 Zero-Inflated Poisson Regression(ZIPR)모형이 사용될 수 있다. 본 논문에서는 Markov Chain Monte Carlo 기법을 이용한 ZIPR모형의 베이지안 추론방법을 제안하고, 이를 실제 구강위생 자료에 적용하며 다른 모형들과 비교한다. 그 결과 베이지안 추론 방법을 적용한 영과잉 모형의 추정오차가 다른 모형들의 추정오차보다 작았고, 예측치가 더 정확했다는 점에서 우수함을 알 수 있었다.

• 한국강구조학회 논문집
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• 제18권4호
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• pp.481-490
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• 2006

지진의 피해를 입은 후 건물의 실제 성능은 많은 요인에 영향을 받는다. 신축 구조물이나 기존 구조물의 지진 성능 예측은 복잡하다. 그 이유는 고려되어져야 하는 많은 요소와 지진 반응의 복잡성뿐만 아니라 이러한 예측과 관련된 타고난 불확실성 과 가변성 때문이다. 본 연구의 목적은 구조물의 능력 평가와 반응 요구에서의 불확실성과 가변성의 적절한 취급과 결합이다. 일관된 방법으로 demand와 capacity에서의 불확실성과 가변성을 설명하기 위하여 신뢰성 이론에 기초한 성능평가의 접근 방법이 초고층 철골 건축물의 내진성능평가 법으로 채택되어져 오고 있다. 신뢰성 이론에 근거한 내진성능평가에 대한 기본 체계와 통계적 연구에 대한 핵심 요소를 요약하였다. dema nd 요소와 capacity 요소의 통계적인 분석을 위하여 국내 기준에 맞는 전형적인 초고층 철골 건축물을 36개 설계하였다. global drift capacity 산정을 위해 철골 모멘트 골조 건물을 증분동해석 하였다.

• Computers and Concrete
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• 제20권3호
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• pp.275-282
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• 2017

Grouting is an operation often carried out to consolidate and seal the rock mass in dam sites and tunnels. One of the important parameters in this operation is grouting pressure. In this paper, analytical models used to estimate pressure are investigated. To validate these models, grouting data obtained from Seymareh and Aghbolagh dams were used. Calculations showed that P-3 model from Groundy and P-25 model obtained from the results of grouting in Iran yield the most accurate predictions of the pressure and measurement errors compared to the real values in P-25 model in this dams are 12 and 14.33 Percent and in p-3 model are 12.25 and 16.66 respectively. Also, SPSS software was applied to define the optimum relation for pressure estimation. The results showed a high correlation between the pressure with the depth of the section, the amount of water take, rock quality degree and grout volume, so that the square of the multiple correlation coefficient among the parameters in this dams were 0.932 and 0.864, respectively. This indicates that regression results can be used to predict the amount of pressure. Eventually, the relationship between the parameters was obtained with the correlation coefficient equal to 0.916 based on the data from both dams generally and shows that there is a desirable correlation between the parameters. The outputs of the program led to the multiple linear regression equation of P=0.403 Depth+0.013 RQD+0.011 LU-0.109 V+0.31 that can be used in estimating the pressure.

• 한국도로학회논문집
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• 제14권6호
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• pp.121-129
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• 2012

PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

• 응용통계연구
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• 제20권1호
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• pp.91-102
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• 2007

본 논문은 공간시계열 자기회귀 이동평균(STARMA) 모형과 공간 시계열 중선형(STBL) 모형에 대해 식별, 추정, 예측 등의 통계적 절차와 특징들을 논하고, 두 모형을 비교하는데 목적이 있다. 사례 연구를 위 해 2001년부터 2006년까지 8개 지역으로부터 보고된 월별 Mumps 자료를 사용했고, 예측오차제곱합(SSF)을 활용하여 두 모형의 적합도를 비교하였다.

• 한국수자원학회논문집
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• 제37권3호
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• pp.185-193
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• 2004

본 연구는 기후변화가 유역의 유출량과 수자원에 미치는 영향을 조사하고 평가하는데 목적이 있다. 이를 위하여 먼저, YONU GCM의 제한실험과 점증실험을 실시하여 전구적 규모의 기후변화 시나리오를 작성하였으며, 통계학적 축소기법과 추계학적 일기발생기법을 이용하여 대상지점의 일 수문기상 시계열을 모의하였다. 이렇게 얻은 시계열자료를 2CO2 상황에서의 유출량자료로 변환하기 위해 준 분포형 강우-유출 모형인 SLURP 모형에 입력하였다. 본 연구에서는 이 방법을 용담댐 유역에 적용하였으며, 그 결과, 기후변화시 연 평균 유출량의 경우 현재상황에 비해 약7.6％ 감소하는 것으로 모의되었으며, 계절적으로 볼 때 겨울철과 가을철에는 유출량이 증가하였으나 여름철에는 감소하였다. 그러나, 유출량의 계절적 패턴은 변화가 없는 것으로 모의되었다.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.