• Communications for Statistical Applications and Methods
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• 제30권3호
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• pp.259-272
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• 2023

In this paper, we suggest a new method for the prediction of sharp changes in particulate matter (PM₁₀) using quantile mapping. To predict the current PM₁₀ density in Seoul, we consider PM₁₀ and precipitation in Baengnyeong and Ganghwa monitoring stations observed a few hours before. For the PM₁₀ distribution estimation, we use the extreme value mixture model, which is a combination of conventional probability distributions and the generalized Pareto distribution. Furthermore, we also consider a quantile generalized additive model (QGAM) for the relationship modeling between precipitation and PM₁₀. To prove the validity of our proposed model, we conducted a simulation study and showed that the proposed method gives lower mean absolute differences. Real data analysis shows that the proposed method could give a more accurate prediction when there are sharp changes in PM₁₀ in Seoul.

• 농업과학연구
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• 제3권2호
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• pp.207-213
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• 1976

최대습윤밀도(最大濕潤密度)로서 함수비시험(含水比試驗)을 하지 않고 직접최적합수비(直接最適合水比)와 최대건록밀도(最大乾綠密度)를 추정(推定)하는 관계식(關係式)을 구(求)하기 위(爲)하여 전국(全國)에서 최근(最近)에 공사(工事)를 실시(實施)하였거나 또는 공사중(工事中)에 있는 157개(個) 지구(地區)의 다짐시험(試驗) 결과(結果)를 분석(分析)하여 다음과 같은 결론(結論)을 얻었다. 최대건조밀도(最大乾燥密度)와 최적합수비(最適合水比)의 사이에는 ${\gamma}=0.9636$의 높은 상관성(相關性)을 나타냈고 ${\gamma}_{dmax.}={\frac{1}{0.4193+0.00937W_{opt.}}$의 관계식(關係式)을 얻었다. 최적합수비(最適合水比)에 대응(對應)하는 습윤밀도(濕潤密度)와 최적합수비(最適合水比)는 $W_{opt.}={\frac{1-0.4193{\gamma}_{tmax.}}{0.937_{\gamma}_{tmax.}-0.01}$의 관계식(關係式)을 얻었고 최대습윤밀도(最大濕潤密度)(${\gamma}_{tmax}$)로 추정(推定)한 최적합수비(最適合水比)는 실측치(實測値)와 큰 차이(差異)가 없었다. 최대습윤밀도(最大濕潤密度)로 추정(推定)한 함수비(含水比)에 의하여 최대건조밀도(最大乾燥密度)를 추정(推定)한 결과실측치(結果實測値)와의 오차(誤差)는 ${\pm}5%$내(內)에 속했다. 최대건조밀도(最大乾燥密度)와 공극비(空隙比)는 ${\gamma}=0.9706$의 높은 상관성(相關性)이 인정(認定)되고 ${\gamma}_{dmax.}={\frac{1}{0.3938+0.3426e}}$의 관계식(關係式)을 얻었다.

• Journal of the Korean Physical Society
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• 제73권10호
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• pp.1584-1588
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• 2018

During hyperthermia therapy, cancer cells are heated to a temperature in the range of $40{\sim}45^{\circ}C$ for a defined time period to damage these cells while keeping healthy tissues at safe temperatures. Prior to hyperthermia therapy, the amount of heat energy transferred to the cancer cells must be predicted. Among various non-invasive methods, the thermal prediction method using the specific absorption rate (SAR) is the most widely used method. The existing methods predict the thermal distribution by using a single constant for the mass density in one organ through assignment. However, because the SAR and the bio heat equation (BHE) vary with the mass density, the mass density of each organ must be accurately considered. In this study, the mass density distribution was calculated using the relationship between the Hounsfield unit and the mass density of tissues in preceding research. The SAR distribution was found using a quasi-static approximation to Maxwell's equation and was used to calculate the potential distribution and the energy distributions for capacitive RF heating. The thermal distribution during exposure to RF waves was determined by solving the BHE with consideration given to the considering contributions of heat conduction and external heating. Compared with reference data for the mass density, our results was within 1%. When the reconstructed temperature distribution was compared to the measured temperature distribution, the difference was within 3%. In this study, the density distribution and the thermal distribution were reconstructed for the agar phantom. Based on these data, we developed an algorithm that could be applied to patients.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2002년도 춘계학술대회
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• pp.23-28
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• 2002

Support vector machine(SVM) has been very successful in classification, regression, time series prediction and density estimation. In this paper, we will propose SVM for fuzzy data classification.

• 한국군사과학기술학회지
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• 제6권3호
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• pp.86-102
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• 2003

Polynitrogen molecules are of great interest as potential high energy-density materials, and hence such structures of various isomers of nitrogen clusters have been calculated using molecular modeling techniques by the researchers from various sectors of scientific institutions. In this article, the predicted meta-stable structures of these hypothetical molecules have been thoroughly reviewed.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 추계학술대회 논문집
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• pp.625-626
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• 2010

• Computers and Concrete
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• 제19권5호
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• pp.555-566
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• 2017

Recently some efforts have been performed to combine the advantages of light-weight and self-compacting concrete in one package called Light-Weight Self-Compacting Concrete (LWSCC). Accurate prediction of hardened properties from fresh state characteristics is vital in design of concrete structures. Considering the lack of references in mixture design of LWSCC, investigating the proper mixture components and their effects on mechanical properties of LWSCC can lead to a reliable basis for its application in construction industry. This study utilizes wide range of existing data of LWSCC mixtures to study the individual and combined effects of the components on the compressive strength. From sensitivity of compressive strength to the proportions and interaction of the components, two equations are proposed to estimate the LWSCC compressive strength. Predicted values of the equations are in good agreement with the experimental data. Application of lightweight aggregate to reduce the density of LWSCC may bring some mixing problems like segregation. Reaching a higher strength by lowered density is a challenging problem that is investigated as well. The results show that, the compressive strength can be improved by increasing the of mixture density of LWSCC, especially in the range of density under $2000Kg/m^3$.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.188-195
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• 2000

This study was conducted to measure the moisture content of powdered food using RF impedance spectroscopic method. In frequency range of 1.0 to 30㎒, the impedance such as reactance and resistance of parallel plate type sample holder filled with wheat flour and red-pepper powder of which moisture content range were 5.93∼-17.07%w.b. and 10.87 ∼ 27.36%w.b., respectively, was characterized using by Q-meter (HP4342). The reactance was a better parameter than the resistance in estimating the moisture density defined as product of moisture content and bulk density which was used to eliminate the effect of bulk density on RF spectral data in this study. Multivariate data analyses such as principal component regression, partial least square regression and multiple linear regression were performed to develop one calibration model having moisture density and reactance spectral data as parameters for determination of moisture content of both wheat flour and red-pepper powder. The best regression model was one by the multiple linear regression model. Its performance for unknown data of powdered food was showed that the bias, standard error of prediction and determination coefficient are 0.179% moisture content, 1.679% moisture content and 0.8849, respectively.

• 한국안전학회지
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• 제29권1호
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• pp.21-24
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• 2014

In this study, we have investigated the maximum smoke density and the initial stage smoke density in order to see the characteristics of smoke release of the architectural surface materials and the architectural adhesives, using smoke density chamber. As a result of the study, polyurethane foam showed the highest smoke density index, 206.55 within 10 min. In the case of the other samples, reinforced styrofoam was followed as 39.90, general styrofoam 33.73, and glass fiber 5.40, respectively. In the intial stage of a fire, it is forecasted actually to give hardship at the clear visibility. In the case of architectural adhesives, the highest ranking was those for windows and doors 509.64, stone 275.63, wood 232.25, tile 18.65, and styrofoam 6.44 were followed, respectively. This result is an early research to show characteristics of smoke release through experiment. However, it is meaningful that this study can be used as a basic for further study on architectural fire hazard prediction.

• 한국분말재료학회지
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• 제26권5호
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• pp.369-374
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• 2019

The properties of powder metallurgy products are related to their densities. In the present work, we demonstrate a method to apply artificial neural networks (ANNs) trained on experimental data to predict the bulk density of barium titanates. The density is modeled as a function of pressure, press rate, heating rate, sintering temperature, and soaking time using the ANN method. The model predictions with the training and testing data result in a high coefficient of correlation (R2 = 0.95 and Pearson's r = 0.97) and low average error. Moreover, a graphical user interface for the model is developed on the basis of the transformed weights of the optimally trained model. It facilitates the prediction of an infinite combination of process parameters with reasonable accuracy. Sensitivity analysis performed on the ANN model aids the identification of the impact of process parameters on the density of barium titanates.