• Title/Summary/Keyword: coefficient

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Development of Probabilistic Models Optimized for Korean Marine Environment Varying from Sea to Sea Based on the Three-parameter Weibull Distribution (우리나라 해역별 해양환경에 최적화된 확률모형 개발)

  • Yong Jun Cho
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.36 no.1
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    • pp.20-36
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    • 2024
  • In this study, probabilistic models for the wave- and lifting forces were derived directly from long-term in-situ wave data embedding the Korean marine environment characteristics varying from sea to sea based on the Three-Parameter Weibull distribution. Korean marine environment characteristics varying from sea to sea carved out their presence on the probability coefficients of probabilistic models for wave- and lifting forces. Energetic wave conditions along the southern coast of Korea distinguish themselves from the others with a relatively large scale coefficient, small location coefficient, and shape coefficient around 1.3. On the other hand, mild marine environment along the western coast has a small variability, leading to small scale-coefficient, large location coefficient and shape coefficient around 2.0. In the sea off Mokpo, near the boundary between the South- and West Seas, marine environment was characterized by small scale-coefficient, large location coefficient, and shape coefficient around 1.2, implying that marine environments characteristics of the South-and West Sea coexist in the sea off Mokpo.

Estimation model of coefficient of permeability of soil layer using linear regression analysis (단순회귀분석에 의한 토층지반의 투수계수 산정모델)

  • Lee, Moon-Se;Kim, Kyeong-Su
    • Proceedings of the Korean Geotechical Society Conference
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    • 2009.03a
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    • pp.1043-1052
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    • 2009
  • To derive easily the coefficient of permeability from several other soil properties, the estimation model of coefficient of permeability was proposed using linear regression analysis. The coefficient of permeability is one of the major factors to evaluate the soil characteristics. The study area is located in Kangwon-do Pyeongchang-gun Jinbu-Myeon. Soil samples of 45 spots were taken from the study area and various soil tests were carried out in laboratory. After selecting the soil factor influenced by the coefficient of permeability through the correlation analysis, the estimation model of coefficient of permeability was developed using the linear regression analysis between the selected soil factor and the coefficient of permeability from permeability test. Also, the estimation model of coefficient of permeability was compared with the results from permeability test and empirical equation, and the suitability of proposed model was proved. As the result of correlation analysis between various soil factors and the coefficient of permeability using SPSS(statistical package for the social sciences), the largest influence factor of coefficient of permeability were the effective grain size, porosity and dry unit weight. The coefficient of permeability calculated from the proposed model was similar to that resulted from permeability test. Therefore, the proposed model can be used in case of estimating the coefficient of permeability at the same soil condition like study area.

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Diffusion of Progesterone in Polyacrylamide Gel (Polyacrylamide gel에서 Progesterone의 확산 거동)

  • 김명희;김말남;민병례
    • Korean Journal of Microbiology
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    • v.28 no.3
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    • pp.278-282
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    • 1990
  • Diffusion and partition of progesterone into the polyacrylamide gel was examined. Diffusion coefficient of progesterone decreased down to an asymptotic value as the concentration of the organic solvents in the diffusing medium increased. However the partition coefficient diminished steadily. Crosslinking density in the gel didn't affected the diffusion coefficient considerably but lowered the partition coefficient due to the contraction of pore volume of the gel. Progesterone showed higher diffusion coefficient as well as partition coefficient in the polyurethane than in the polyacrylamide gel, which seems to be ascribed to the difference in hydrophobicity, pore volume and pore size of the polymer matrix.

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Flowrate characteristics and application of screw pump (스크류펌프의 유량특성과 적용)

  • Lim, Jong-Soon
    • 유체기계공업학회:학술대회논문집
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    • 2004.12a
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    • pp.614-625
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    • 2004
  • The main purpose of this study is to get flowrate coefficient 'q' and to study characteristics of flowrate of screw pump. This study is based on Muskin's formula and depends on the computer simulation. The results of study are as follows. 1. Flowrate coefficient will reduce according to increase of inclination. The larger the pitch ratio is and the smaller the diameter ratio is, the more coefficient reduces. 2. As a rule, the coefficient increases according to reduction of the diameter ratio, but the coefficient tends to reduce when the ratio is less than 0.45. So, in actual application, it is recommended that the ratio should be above 0.45. 3. If the pitch ratio increases, the coefficient increases in case of small inclination and decreases in case of large inclination. 4. The coefficient increases according to number of windings. Especially, singles winding is not used in actual application because it has too small coefficient. 5. The coefficient decreases when the influent water level falls. Flowrate is almost zero when the water level is below $40\%$ of Filling Points.

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Estimation of Roughness Coefficient Using a Representative Grain Diameter for Han Stream in Jeju Island (한천의 대표입경을 이용한 조도계수 산정)

  • Lee, Jun-Ho;Yang, Sung-Kee;Kim, Dong-Su
    • Journal of Environmental Science International
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    • v.22 no.5
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    • pp.563-570
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    • 2013
  • Roughness coefficient was computed for review of applicability based on measurement of the representative grain diameter reflecting channel characteristics of Han Stream. After field survey, collection of bed material, and grain analysis on the collected bed material, roughness coefficient was computed using representative grain and existing empirical equation for roughness coefficient. Value of roughness coefficient calculated using equation by Meyer-Peter and Muller (1948) was 0.0417 for upstream, 0.0432 for midstream, and 0.0493 for downstream. As a result of comparing the computed roughness coefficient to other empirical equations for review of applicability, the coefficient was larger in Strickler (1923) equation by 0.006. Smaller coefficient was shown by Planning Report for River Improvement Works. Equation by Garde and Raju (1978) was larger by 0.004, and equations by Lane and Carlson (1953) and by Meyer-Peter and Muller (1948) were larger by 0.001. Such precise roughness coefficient is extremely important when computing the amount of flood in rivers to prevent destruction of downstream embankments and property damages from flooding. Since roughness coefficient is a factor determined by complicated elements and differs according to time and space, continued management of roughness coefficient in rivers and streams is deemed necessary.

On the Effect of Significance of Correlation Coefficient for Recommender System

  • Lee, Hee-Choon
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1129-1139
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    • 2006
  • Pearson's correlation coefficient and vector similarity are generally applied to The users' similarity weight of user based recommender system. This study is needed to find that the correlation coefficient of similarity weight is effected by the number of pair response and significance probability. From the classified correlation coefficient by the significance probability test on the correlation coefficient and pair of response, the change of MAE is studied by comparing the predicted precision of the two. The results are experimentally related with the change of MAE from the significant correlation coefficient and the number of pair response.

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Enhanced vertical diffusion coefficient at upper layer of suspended sediment concentration profile

  • Kim, Hyoseob;Jang, Changhwan;Lhm, Namjae
    • Ocean Systems Engineering
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    • v.2 no.4
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    • pp.289-295
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    • 2012
  • Assume fluid eddy viscosity in the vertical direction is parabolic. Sediment particles diffuse with the given fluid eddy viscosity. However, when the vertical diffusion coefficient profile is computed from the suspended sediment concentration profile, the coefficient shows lager values than the fluid mixing coefficient values. This trend was explained by using two sizes of sediment particles. When fine sediment particles like wash load are added in water column the sediment mixing coefficient looks much larger than the fluid mixing coefficient.

Exploratory data analysis for Chatterjee's ξ coefficient (Chatterjee의 ξ 계수에 대한 탐색적자료분석)

  • Jang, Dae-Heung
    • The Korean Journal of Applied Statistics
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    • v.35 no.3
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    • pp.421-434
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    • 2022
  • Chatterjee (2021) proposed a new correlation coefficient ξ. Focusing on two questions (1. Is ξ coefficient distinguishable for Anscombe's quartet data set?, 2. How does the ξ coefficient value change according to the number of data for various kinds of scatterplots?), an exploratory data analysis is attempted for ξ coefficient. We can compare three measures (ξ coefficient, Pearson's correlation coefficient and mutual information).

Research on heat transfer coefficient of supercritical water based on factorial and correspondence analysis

  • Xiang, Feng;Tao, Zhou;Jialei, Zhang;Boya, Zhang;Dongliang, Ma
    • Nuclear Engineering and Technology
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    • v.52 no.7
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    • pp.1409-1416
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    • 2020
  • The study of heat transfer coefficient of supercritical water plays an important role in improving the heat transfer efficiency of the reactor. Taking the supercritical natural circulation experimental bench as the research object, the effects of power, flow, pipe diameter and mainstream temperature on the heat transfer coefficient of supercritical water were studied. At the same time, the experimental data of Chen Yuzhou's supercritical water heat transfer coefficient was collected. Through the factorial design method, the influence of different factors and their interactions on the heat transfer coefficient of supercritical water is analyzed. Through the corresponding analysis method, the influencing factors of different levels of heat transfer coefficient are analyzed. It can be found: Except for the effects of flow rate, power, power-temperature and temperature, the influence of other factors on the natural circulation heat transfer coefficient of supercritical water is negligible. When the heat transfer coefficient is low, it is mainly affected by the pipe diameter. As the heat transfer coefficient is further increased, it is mainly affected by temperature and power. When the heat transfer coefficient is at a large level, the influence of the flow rate is the largest at this time.

Estimation of the Maximum Friction Coefficient of the Rough Terrain to Control the Mobile Robots (주행로봇 제어를 위한 험지의 최대마찰계수 추정)

  • Kang, Hyun-Suk;Kwak, Yoon-Keun;Choi, Hyun-Do;Jeong, Hae-Kwan;Kim, Soo-Hyun
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.10
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    • pp.1062-1072
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    • 2008
  • When mobile robots perform the mission in the rough terrain, the traversability depended on the terrain characteristic is useful information. In the traversabilities, wheel-terrain maximum friction coefficient can indicate the index to control wheel-terrain traction force or whether mobile robots to go or not. This paper proposes estimating wheel-terrain maximum friction coefficient. The existing method to estimate the maximum friction coefficient is limited in flat terrain or relatively easy driving knowing wheel absolute velocity. But this algorithm is applicable in rough terrain where a lot of slip occurred not knowing wheel absolute velocity. This algorithm applies the tire-friction model to each wheel to express the behavior of wheel friction and classifies slip-friction characteristic into 3 major cases. In each case, the specific algorithm to estimate the maximum friction coefficient is applied. To test the proposed algorithm's feasibility, test bed(ROBHAZ-6WHEEL) simulations are performed. And then the experiment to estimate the maximum friction coefficient of the test bed is performed. To compare the estimated value with the real, we measure the real maximum friction coefficient. As a result of the experiment, the proposed algorithm has high accuracy in estimating the maximum friction coefficient.