• The Journal of Engineering Geology
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• v.29 no.1
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• pp.51-68
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• 2019

This study performs to evaluate adaptability of seashore filtering type seawater-intake which adapts dua1 filter well alternative for direct seawater-intake. This study varies filter condition of seashore free surface aquifer which is composed of sand layer then installs real size dual filter well and single filter well to evaluate water permeability and proper pumping amount according to filter condition. According to result of step aquifer test, it is analysed that 110.3% synergy effect of water permeability coefficient is happened compare to single filter since dual filter well has better improvement. dual filter has higher water permeability coefficient compare to same pumping amount, this means dual filter has more improved water permeability than single filter. According to analysis result of continuous aquifer test, it is evaluated that dual filter well (SD1200) has higher water permeability than single filter well (SS800) by analysis of water permeability coefficient using monitoring well and gauging well, it is also analysed dual filter has 110.7% synergy effect of water permeability coefficient. As a evaluation result of pumping amount according to analysis of water level dropping rate, it is analysed that dual filter well increased 122.8% pumping amount compare to single filter well when water level dropping is 2.0 m. As a result of calculating proper pumping amount using water level dropping rate, it is analysed that dual filter well shows 136.0% higher pumping amount compare to single filter well. It is evaluated that proper pumping amount has 122.8~160% improvement compare to single filter, pumping amount improvement rate is 139.6% compare to averaged single filter. In other words, about 40% water intake efficiency can be improved by just installation of dual filter compare to normal well. Proper pumping amount of dual filter well using inflection point is 2843.3 L/min and it is evaluated that daily seawater intake amount is about $4,100m^3/day$ (${\fallingdotseq}4094.3m^3/day$) in one hole of dual filter well. Since it is possible to intake plenty of water in one hole, higher adaptability is anticipated. In case of intaking seawater using dual filter well, no worries regarding damages on facilities caused by natural disaster such as severe weather or typhoon, improvement of pollution is anticipated due to seashore sand layer acts like filter. Therefore, It can be alternative of environmental issue for existing seawater intake technique, can save maintenance expenses related to installation fee or damages and has excellent adaptability in economic aspect. The result of this study will be utilized as a basic data of site demonstration test for adaptation of riverside filtered water of upcoming dual filter well and this study is also anticipated to present standard of well design and construction related to riverside filter and seashore filter technique.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Korean Journal of Poultry Science
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• v.16 no.2
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• pp.61-71
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• 1989

The purposes of this study were to investigate the genetic variations by backcrossing in commercial chickens. Backcrossing was carried out successively back to parent stock (P.S). Heritabilities and genetic correlation coefficients were estimated to verify the genetic variations. The data obtained from a breeding programme with commercial chickens (I strain) were collected from 1955 to 1987 at Poultry Breeding Farm, Seoul National University. Data came from a total of 1230 female offspring. The results obtained are summarized as follows： 1. The general performance ($Mean\pmStandard deviation$) of each trait was $663.94\pm87.11$g for 8 weeks body weight, $1579.1\pm155.43$g for 20 weeks body weight, $2124.1\pm215.3$g for 40 weeks body weight, $2269.1\pm242.94$g for 60 weeks body weight, $168.43\pm12.94$ day for a9e at sexual maturity (SM), $214.52\pm29.82$ eggs , for total egg number to 60 weeks of age (TEN), $61.45\pm3.48$ g for average weight (AEW), $13180.7\pm1823.22$ g for total egg mass to 60 weeks of age(TEM). All traits, except 10 weeks body weight and AEW, were significant for the degrees of backcross (p＜0.01). 2. The pooled estimates of heritabilities derived from the sire, dam and combined variance components were 0.47~0.52 for age at sexual maturity (SM), 0.07~0.37 for total egg number (TEN), 0.40~0.54 for average egg weight (AEW), 0.18~0.27 for total egg mass (TEM). High heritability estimates were found for SM and AEW. TEN and TEM were estimated to be a lowly heritable traits. Heritability estimates from dam components were higher than those from sire components. These differences might be due to non-additive genetic effect and maternal effect. 3. The estimates of heritabilities and standard errors derived from combined variance components for different degrees of backcross were $0.47\pm0.11$ (BCO), $0.42\pm0.16$ (BC1), $0.51\pm0.29$ (BC2) for TEN, $0.59\pm0.20$ (BCO), $0.43\pm0.17$ (BC1), $0.35\pm0.18$ (BC2) for AEW, $0.28\pm0.12$(BC0), $0.20\pm0.11$(BC1), $0.18\pm0.14$ (BC2) for TEM. Heritability estimates for AEW and TEM were decreased by backcrossing while those for SM and TEN remained constant. Since backcrossing contributes to increased homozygosity, the genetic variation of the traits (AEW and TEM) decreased . 4. The pooled estimates of genetic correlation coefficients were -0.55 between SM and TEN, 0.20 between SM and AEW, -0.29 between TEN and AEW, 0.82 between TEM and TEN, 0.31 between TEM and AEW, -0.42 between TEM and SM. The genetic correlation between TEM and TEN was higher than that between TEM and AEW, and it was suggested that egg mass was strongly affected by egg number. Also, age at sexual maturity(SM) contributes to egg mass(TEM). 5. When backcrossing was carried out successively, the genetic correlation between TEM and TEN increased (BC0：0.79, BC1：0.82, BC2：0.91) but those between TEM and SM decreased (BC0：-0.54, BC1：-0.36, BC2：-0.09) with successive backcrosses.

• Magazine of the Korean Society of Agricultural Engineers
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• v.11 no.4
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• pp.1775-1782
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• 1969

The results of the study on the consumptine use of irrigated water in paddy fields during the growing season of rice plants are summarized as follows. 1. Transpiration and evaporation from water surface. 1) Amount of transpiration of rice plant increases gradually after transplantation and suddenly increases in the head swelling period and reaches the peak between the end of the head swelling poriod and early period of heading and flowering. (the sixth period for early maturing variety, the seventh period for medium or late maturing varieties), then it decreases gradually after that, for early, medium and late maturing varieties. 2) In the transpiration of rice plants there is hardly any difference among varieties up to the fifth period, but the early maturing variety is the most vigorous in the sixth period, and the late maturing variety is more vigorous than others continuously after the seventh period. 3) The amount of transpiration of the sixth period for early maturing variety of the seventh period for medium and late maturing variety in which transpiration is the most vigorous, is 15% or 16% of the total amount of transpiration through all periods. 4) Transpiration of rice plants must be determined by using transpiration intensity as the standard coefficient of computation of amount of transpiration, because it originates in the physiological action.(Table 7) 5) Transpiration ratio of rice plants is approximately 450 to 480 6) Equations which are able to compute amount of transpiration of each variety up th the heading-flowering peried, in which the amount of transpiration of rice plants is the maximum in this study are as follows: Early maturing variety ; Y=0.658+1.088X Medium maturing variety ; Y=0.780+1.050X Late maturing variety ; Y=0.646+1.091X Y=amount of transpiration ; X=number of period. 7) As we know from figure 1 and 2, correlation between the amount evaporation from water surface in paddy fields and amount of transpiration shows high negative. 8) It is possible to calculate the amount of evaporation from the water surface in the paddy field for varieties used in this study on the base of ratio of it to amount of evaporation by atmometer(Table 11) and Table 10. Also the amount of evaporation from the water surface in the paddy field is to be computed by the following equations until the period in which it is the minimum quantity the sixth period for early maturing variety and the seventh period for medium or late maturing varieties. Early maturing variety ; Y=4.67-0.58X Medium maturing variety ; Y=4.70-0.59X Late maturing variety ; Y=4.71-0.59X Y=amount of evaporation from water surface in the paddy field X=number of period. 9) Changes in the amount of evapo-transpiration of each growing period have the same tendency as transpiration, and the maximum quantity of early maturing variety is in the sixth period and medium or late maturing varieties are in the seventh period. 10) The amount of evapo-transpiration can be calculated on the base of the evapo-transpiration intensity (Table 14) and Tablet 12, for varieties used in this study. Also, it is possible to compute it according to the following equations with in the period of maximum quantity. Early maturing variety ; Y=5.36+0.503X Medium maturing variety ; Y=5.41+0.456X Late maturing variety ; Y=5.80+0.494X Y=amount of evapo-transpiration. X=number of period. 11) Ratios of the total amount of evapo-transpiration to the total amount of evaporation by atmometer through all growing periods, are 1.23 for early maturing variety, 1.25 for medium maturing variety, 1.27 for late maturing variety, respectively. 12) Only air temperature shows high correlation in relation between amount of evapo-transpiration and climatic conditions from the viewpoint of Korean climatic conditions through all growing periods of rice plants. 2. Amount of percolation 1) The amount of percolation for computation of planning water requirment ought to depend on water holding dates. 3. Available rainfall 1) The available rainfall and its coefficient of each period during the growing season of paddy fields are shown in Table 8. 2) The ratio (available coefficient) of available rainfall to the amount of rainfall during the growing season of paddy fields seems to be from 65% to 75% as the standard in Korea. 3) Available rainfall during the growing season of paddy fields in the common year is estimated to be about 550 millimeters. 4. Effects to be influenced upon percolation by transpiration of rice plants. 1) The stronger absorbtive action is, the more the amount of percolation decreases, because absorbtive action of rice plant roots influence upon percolation(Table 21, Table 22) 2) In case of planting of rice plants, there are several entirely different changes in the amount of percolation in the forenoon, at night and in the afternoon during the growing season, that is, is the morning and at night, the amount of percolation increases gradually after transplantation to the peak in the end of July or the early part of August (wast or soil temperature is the highest), and it decreases gradually after that, neverthless, in the afternoon, it decreases gradually after transplantation to be at the minimum in the middle of August, and it increases gradually after that. 3) In spite of the increasing amount of transpiration, the amount of daytime percolation decreases gadually after transplantation and appears to suddenly decrease about head swelling dates or heading-flowering period, but it begins to increase suddenly at the end of August again. 4) Changs of amount of percolation during all growing periods show some variable phenomena, that is, amount of percolation decreases after the end of July, and it increases in end August again, also it decreases after that once more. This phenomena may be influenced complexly from water or soil temperature(night time and forenoon) as absorbtive action of rice plant roots. 5) Correlation between the amount of daytime percolation and the amount of transpiration shows high negative, amount of night percolation is influenced by water or soil temperature, but there is little no influence by transpiration. It is estimated that the amount of a daily percolation is more influenced by of other causes than transpiration. 6) Correlation between the amount of night percoe, lation and water or soil temp tureshows high positive, but there is not any correlation between the amount of forenoon percolation or afternoon percolation and water of soil temperature. 7) There is high positive correlation which is r=+0.8382 between the amount of daily percolation of planting pot of rice plant and amount and amount of daily percolation of non-planting pot. 8) The total amount of percolation through all growin. periods of rice plants may be influenced more from specific permeability of soil, water of soil temperature, and otheres than transpiration of rice plants.