• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.306-306
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• 2023

인공강우 실험은 일반적으로 호우 기간 중 수행한다. 과거 연구(국립기상과학원, 2020)에서 설정된 인공강우 실험 기준은 "일 강우량 20 mm를 초과하는 경우"로, 이는 경험적으로 결정된 기준이다. 이에 본 연구에서는 가용한 장기간 관측자료를 수집하고 자료를 분석하여 인공강우 실험을 위한 댐 유역의 기상학적 조건과 수문학적 조건을 평가하였다. 인공강우 실험 조건 평가를 위한 대상 유역으로 한반도 남부지방에 위치한 보령댐, 용담댐, 남강댐, 안동댐, 임하댐 유역을 고려하였다. 기상학적 조건은 인공강우 발생 조건과 밀접한 연관이 있다. 기상학적 조건을 평가하기 위한 자료로 장기간 자료가 가용하며 수집이 용이한 일 강우량, 상대습도, 운량 등의 자료를 수집하였다. 강우량과 운량의 경우 상위 10%, 20%, 30%에 대응하는 기준치를 산정하고, 이를 초과하는 연 횟수를 비교하여 유역의 조건을 평가하는데 반영하였다. 상대습도의 경우 연중 80%, 85%, 90%를초과하는 횟수를 비교하고 추가로 이 기준치를 초과함에도 강우는 발생하지 않은 횟수도 고려하였다. 수문학적 조건은 인공강우의 필요성, 즉 수자원 공급량 차원에서 고려한 조건으로 볼 수 있다. 수문학적 조건을 평가하기 위한 자료로는 댐의 일 및 월 단위 저수용량을 수집하고, 댐의 총 저수용량과 유효저수용량에 대한 비를 산정하여 평가에 고려하였다. 댐 유역의 인공강우 실험 조건을 평가한 결과는 다음과 같이 요약된다. 기상조건을 고려한다면 보령댐과 용담댐이 적절한 것으로 판단되었다. 특히 보령댐의 경우, 상대습도가 90% 이상으로 매우 높음에도 대기중의 수증기가 응결되지 못하여 강우량이 발상하지 않는 경우가 있어 Seeding의효과가 극대화될 여지가 높은 것으로 판단된다. 수문학적 조건을 고려하는 경우, 보령댐 유역, 안동댐 유역, 임하댐 유역이 유효 저수율이 낮은 것으로 나타나 인공강우를 활용한 수자원 확보의 필요성이 높은 유역으로 평가된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.935-943
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• 2006

Key factors affecting the mode share of Soul metro are investigated. The log-regression model, which can describe the elasticity of the factors with ease, is established rather than the conventional mode choice model is used. The log-regression model requires lower level of data availability for calibration and identifies the impact of the factors on mode share straightforwardly. As a result, it is found that the main reasons why the current mode share of railway is low are due to several problems such as winding lines, inconvenient transfers and unnecessary bypasses. The calibrated model is adopted to evaluate the network reshuffling alternatives. The network reshuffling is to rearrange the existing inefficient railway lines that have frequent transfers and many winding segments. The proposed network reshuffling, which includes straightening winding lines and changing grade separated transfers into cross-platform transfers, turned out to be a good measure to tackle the problems.

• Journal of the Korean Society of Food Culture
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• v.21 no.6
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• pp.629-637
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• 2006

This exploratory study is to analyze the status of star menus and suggest the development strategy of star menus in the western restaurants of the tourism hotels in Seoul. The data was collected from the chefs who have been working over 15-years in tourism hotels in Seoul by judgement sampling. The questionnaire was composed of Miller and Pavesic's 'Fifty tips for a successful menu' and Khan's 'The evaluation of menu item development', The 30-menu items were selected from the 1st survey on the menu items that were high in both popularity and contribution margin by menu engineering method. The selected menu items were analyzed by 14 lists: simplicity, ready availability of ingredients, quality, flavor, presentation, preparation, service method, nutrition quality, preference, profitability, serving temperature, descriptive copy of menu and publicity. As a result of the study, appetizer category was recorded the highest score by 4.09. Smoked salmon, Tomato and mozzarella cheese, Cream of mushroom, Cream of asparagus, French onion soup, Grilled beef tenderloin, Grilled rib-eye steak, Roasted lamb rack, King prawn, Seafood spaghetti, Chef's salad, Caesar salad, Organic salad, Fruit plate, Italian tiramisu and Yogurt ice cream were recorded high score. The development strategy of star menu is belows : the menu writer must consider the productivity, effectiveness, popularity and profitability, the regular customers want varieties and creativity in the menu and the operators have to include the star menu items in the set menu.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.75-75
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• 2022

Traditional agriculture mostly focused on activity in the field, but current agriculture faces problems such as reduction of agricultural inputs, labor shortage and so on. Accordingly, traditional agricultural experiments generally considered the simple treatment effects, but current agricultural experiments need to consider the several and complicate treatment effects. To analyze such several and complicate treatment effects, data collection has the first priority. Remote sensing is a quite effective tool to collect information in agriculture, and recent easier availability of UAVs (Unmanned Aerial Vehicles) enhances the effectiveness. LAI (Leaf Area Index) is one of the most important information for evaluating the condition of crop growth. In this study, we utilized UAV with multispectral camera to evaluate plant-based LAI of sweetcorn in a small-scale field experiment and discussed the feasibility of a new experimental design to analyze the several and complicate treatment effects. The plant-based SR measured by UAV showed the highest correlation coefficient with LAI measured by a canopy analyzer in 2018 and 2019. Application of linear mix model showed that plant-based SR data had higher detection power due to its huge number of data although SR was inferior to evaluate LAI than the canopy analyzer. The distribution of plant-based data also statistically revealed the border effect in treatment plots in the traditional experimental design. These results suggest that remote sensing with UAVs has the advantage even in a small-scale experimental plot and has a possibility to provide a new experimental design if combined with various analytical applications such as plant size, shape, and color.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.6
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• pp.861-869
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• 2023

This study aimed to evaluate the effectivity of full-fat black soldier fly Hermetia illucens (BSF) and defatted mealworm Tenebrio molitor (MW) larvae meal as a fish meal (FM) substitute in a low-fish meal (LFM) diet for juvenile olive flounder Paralichthys olivaceus. The LFM diet comprising 45% FM with tankage meal, poultry byproduct meal, soy protein concentrate and wheat gluten was the control diet. Three experimental diets were 10% FM in Con with BSF, MW and a mixture of both at the same ratio (designated as B10, M10 and B5M5, respectively). Four hundred and forty-fourth juvenile P. olivaceus (34.3±0.1 g) were randomly distributed into 12 tanks (425 L) in three replicate groups per treatment and fed the experimental diets for 15 weeks. At the end of the feeding trial, growth performance, survival, biological indices (condition factor, viscerosomatic index, hepatosomatic index), non-specific immune responses (lysozyme, myeloperoxidase) and intestinal histology (villi length and goblet cells) were not significantly affected by treatments. Feed utilization was significantly decreased in M10 compared to the control group. Alanine aminotransferase level was significantly higher in M10 than in the control group. Glucose level was significantly lower in B10 than in the control group. These results suggest that BSF and MW can be used as FM substitutes. However, considering feed conversion ratio and AST level, MW availability is thought to be lower than that of BSF, and feeding fish with a diet containing MW for an extended period is thought to adversely affect fish growth.

• Steel and Composite Structures
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• v.52 no.3
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• pp.293-312
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• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.

• The Korean Journal of Nuclear Medicine Technology
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• v.12 no.1
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• pp.13-18
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• 2008

Purpose: The image processing method due to the algorism which is various portion nuclear medical image decision is important it makes holds. The purpose of this study is it applies hereupon new image processing method SIEMENS (made by Pixon co.) Onco. flash processing reconstruction and the comparison which use the image control technique of existing the clinical usefulness it analyzes with it evaluates. Materials & Methods: 1. Whole body bone scan-scan speed 20 cm/min, 30 cm/min & 40 cm/min blinding test 2. Bone static spot scan-regional view 200 kcts, 400 kcts for chest, pelvis, foot blinding test 3. 4 quadrant-bar phantom-20000 kcts visual evaluation 4. LSF-FWHM resolution comparison ananysis. Results: 1. Raw data (20 cm/min) & processing data (30 cm/min)-similar level image quality 2. Low count static image-image quality clearly improved at visual evaluation result. 3. Visual evaluation by quadrant bar phantom-rising image quality level 4. Resolution comparison evaluation (FWHM)-same difference from resolution comparison evaluation Conclusion: The study which applies a new method Onco. flash processing reconstruction, it will be able to confirm the image quality improvement which until high level is clearer the case which applies the method of existing better than. The new reconstruction improves the resolution & reduces the noise. This enhances the diagnostic capabilities of such imagery for radiologists and physicians and allows a reduction in radiation dosage for the same image quality. Like this fact, rising of equipment availability & shortening the patient waiting move & from viewpoint of the active defense against radiation currently becomes feed with the fact that it will be the useful result propriety which is sufficient in clinical NM.

• Korean Journal of Soil Science and Fertilizer
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• v.12 no.1
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• pp.15-23
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• 1979

In the first paper of the series the five soil fertility factors were evaluated by means of principal component analysis and varimax method. They are interpreted as representing, 1) skeletal available phosporus status, 2) organnic matter status, 3) salt status 4) base status, and 5) free oxide status. In order to resynthesize such fragmented information for the overall soil fertility evaluation, the method of multiple regression analysis was adopted, using the five factor scores and yield data for Korean paddy soils as independent and dependent variables respectively. As test of linear models with different combinations of independent variables the results of t-test of regression coefficient were revealed that the organic matter status (FII) has no relevance to the yield of paddy and that the free oxides and salt supply has by it self only an insignificant contribution to the yield. The multiple correlation coefficient (R) revealed its multiple regression analysis was as low as 0.43. Introduction of quadratic terms to the linear model bettered the result. Thus multiple correlation coefficient (R) was increased as 0.59. Therefore, a coefficient of determination 0.35 was obtained by a quadratic model with interaction terms among the five fertility constituents. Generally we think that the fertility factor has more contribution to raise the rice yield in paddy and that the failure of yield prediction by fertility factor scores was caused by one of follows; 1) the roughness of the yield inspection, and 2) missextraction of fertility constituents. The second step in this study, assuming that the residuals by multiple regression analysis were due to factors other than soil fertility, we can now proceed to predicting the yield from the field characters with the classified fertility groups by means of Hayashi's theory of quantification No. 1. Such variables as fertility groups (FTYG), water availability (WATER), soil drainage (DRNG), climatic zone (CLIZ), surface soil's stickiness (STCKT), surface soil's dry consistence (DCNST), and surface soil's texture (FTEXT) are taken up as the explanatory variables. The quantification appears reasonable; the well to extremely well in soil drainage, very sticky of surface soil, inefficiency in water availability, coarse texture, and very hard to extremely hard dry consistence in soil are detrimental to the rice yield. The R was as high as 0.90 for the set of variables. But the given explanatory variables in this study were not quite effective in explaining rice yield. The method developed seems to be promising only if properly collected data are available. Conditions that should be satisfied in the yield inspection obtained from common cultivator for the purpose of deriving a prediction equation were put forward.

• Journal of The Korean Society of Grassland and Forage Science
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• v.33 no.4
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• pp.298-303
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• 2013

This study investigates the effect of a feed concentrate on the growth performance, feed requirement, and rearing expenses of growing black goats. Forty female growing black goats were divided into four groups and fed rice straw (ad libitum) mixed with different levels of the feed concentrate: T1 (1.5%), T2 (1.8%), T3 (2.1%), and T4 (0%, control). The average daily weight gain (body weight per day) was as follows: T1 < T2 < T3 < T4 (50.23, 60.37, 71.46 and 98.90 g/day, respectively) (p<0.05). The intake of rice straw decreased significantly with increase in the concentrate level (p<0.05). Rearing expenses amounted to 46,704, 49,998, 54,701, and 74,613 won for feed concentrate levels of 1.5%, 1.8%, 2.1%, and ad libitum, respectively. Increase in feed concentrate levels led to an increase in rearing expenses. In conclusion, a concentrate level of 2.1% was determined to be idea for a rice-straw-based feed for growing black goats.