• Journal of the Korean Geosynthetics Society
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• v.15 no.4
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• pp.71-78
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• 2016

In this study, execution costs of the foundation system are compared with PHC and steel pile in the same soil layers and load condition. Steel piles installed on the thin weathered rock are reduced as 12.5% in comparison with the number of PHC piles. Steel piles installed on the soft rock through weathered rock with 1.7m of thickness reduce the number of piles as 35.7% (STK 400) and 46.4% (STK 490), respectively, in comparison with PHC piles installed on the weathered rock. Also, they reduce the number of piles as 26.5% (STK 400) and 38.8% (STK 490), respectively, in comparison with steel piles (STK 400) installed on the weathered rock. When the thickness of footings is constant, steel piles installed on the soft rock may reduce the area of footings up to 12.2% (STK 400) and 45.4% (STK 490), respectively, in comparison with PHC piles installed on the weathered rock. Total cost of foundation system installed on the soft rock with steel piles (STK 400) increases as 12%, whereas in case of replaced with steel piles (STK 490), it reduces as 16% in comparison with PHC piles installed on the weathered rock. This is because the cost reduction due to the number of piles and footing area is more effective despite high cost of steel piles (STK 490). When the thickness of weathered rock is less than 5m, installing steel piles (STK 490) on the soft rock through it is more economic in comparison with installing PHC piles on the weathered rock.

• Journal of Dairy Science and Biotechnology
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• v.33 no.2
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• pp.119-127
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• 2015

To date, detection of microbial populations in dairy products has been performed using culture media, which is a time-consuming and laborious method. The recently developed chromogenic media could be more rapid and specific than classical culture media. However, the newly developed molecular-based technology can detect microbial populations with greater rapidity and sensitivity than the classical method involving culture media and chromogenic media. This molecular-based technology could provide various options for monitoring the characterization of different states of bacteria and cells. Thus, it could help upgrade the processing system of the dairy industry so as to maintain the safety and quality of dairy foods. Among the various newly developed molecular-based technologies, flow cytometry can potentially be used for monitoring microbiological populations in the dairy industry if official international standards are available for this purpose. When omics technology would have biomarker identification, it could be regarded as the rapid and sensitive analytical methods. Methods based on PCR, which has become a basic technique in microbiological research, can be developed and validated as alternative methods for quantification of dairy microorganisms. This review discusses methods for monitoring microbiological populations in dairy foods and the limitations of these studies, as well as the need for further research on such methods in the dairy industry.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.57-65
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• 2015

As information technology fusion is accelerated, the researches to improve the quality and productivity of crops inside a plant factory actively progress. Advanced growth environment management technology that can provide thermal environment and air flow suited to the growth of crops and considering the characteristics inside a facility is necessary to maximize productivity inside a plant factory. Currently running plant factories are designed to rely on experience or personal judgment; hence, design and operation technology specific to plant factories are not established, inherently producing problems such as uneven crop production due to the deviation of temperature and air flow and additional increases in energy consumption after prolonged cultivation. The optimization process has to be set up in advance for the arrangement of air flow devices and operation technology using computational fluid dynamics (CFD) during the design stage of a facility for plant factories to resolve the problems. In this study, the optimum arrangement and air flow of air circulation fans were investigated to save energy while minimizing temperature deviation at each point inside a plant factory using CFD. The condition for simulation was categorized into a total of 12 types according to installation location, quantity, and air flow changes in air circulation fans. Also, the variables of boundary conditions for simulation were set in the same level. The analysis results for each case showed that an average temperature of 296.33K matching with a set temperature and average air flow velocity of 0.51m/s suiting plant growth were well-maintained under Case 4 condition wherein two sets of air circulation fans were installed at the upper part of plant cultivation beds. Further, control of air circulation fan set under Case D yielded the most excellent results from Case D-3 conditions wherein air velocity at the outlet was adjusted to 2.9m/s.

• Journal of Korea Water Resources Association
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• v.41 no.12
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• pp.1219-1230
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• 2008

Stream inflows induced by flood runoffs have a higher density than the ambient reservoir water because of a lower water temperature and elevated suspended sediment(SS) concentration. As the propagation of density currents that formed by density difference between inflow and ambient water affects reservoir water quality and ecosystem, an understanding of reservoir density current is essential for an optimization of filed monitoring, analysis and forecast of SS and nutrient transport, and their proper management and control. This study was aimed to quantify the characteristics of inflow density current including plunge depth($d_p$) and distance($X_p$), separation depth($d_s$), interflow thickness($h_i$), arrival time to dam($t_a$), reduction ratio(${\beta}$) of SS contained stream inflow for different flood magnitude in Daecheong Reservoir with a validated two-dimensional(2D) numerical model. 10 different flood scenarios corresponding to inflow densimetric Froude number($Fr_i$) range from 0.920 to 9.205 were set up based on the hydrograph obtained from June 13 to July 3, 2004. A fully developed stratification condition was assumed as an initial water temperature profile. Higher $Fr_i$(inertia-to-buoyancy ratio) resulted in a greater $d_p,\;X_p,\;d_s,\;h_i$, and faster propagation of interflow, while the effect of reservoir geometry on these characteristics was significant. The Hebbert equation that estimates $d_p$ assuming steady-state flow condition with triangular cross section substantially over-estimated the $d_p$ because it does not consider the spatial variation of reservoir geometry and water surface changes during flood events. The ${\beta}$ values between inflow and dam sites were decreased as $Fr_i$ increased, but reversed after $Fr_i$>9.0 because of turbulent mixing effect. The results provides a practical and effective prediction measures for reservoir operators to first capture the behavior of turbidity inflow.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.3
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• pp.50-66
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• 2016

This study attempts to understand and explain how "Directed Attention Restorative Environment (DARE)" is managed and fostered in "Gang-Hak (講學)" and "Yu-Sik (遊息)" spaces both inside and outside of Oksan Seowon Confucian Academy, Gyeongju. Directed Attention is a pivotal element in human information processing so that its restoration is crucial for effective thinking and learning. According to Kaplan & Kaplan's Attention Restoration Theory, an environment, in order to be restorative, should have four elements: 'Being Away,' 'Extent,' 'Fascination,' and 'Compatibility.' We could confirm OkSan Seowon Confucian Academy has an inner logic that integrates two basically different spacial concepts of "Jangsu" and "Yusik" and thus fosters the Attention Restorative Environment. Particularly, the Four Mountains and Five Platforms (四山五臺) surrounding the premises provides an excellent learning environment, and is in itself educational in terms of the Neo-Confucian epistemology with "Attaining Knowledge by way of Positioning Things (格物致知)" as its principle precept, and of its aesthetics with "Connectedness with Nature" as its central tenet. This study attempts to recapture the value of Korea's cultural heritage concerning the Human/Nature relationship; and it may provide useful insights and practical guidelines/grounds in designing today's schools and campuses, where the young people's needs for the Directed Attention- and Attention Restorative- Servicescapes seem to be greater than ever.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.31 no.4
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• pp.70-83
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• 2013

To widen prospect of villa as Korea traditional garden, the result of the research about constructional characteristics of Seogo-jeongsa and Sameun-jeong of Yeoju Lee family at Toero-ri, Miryang-si, Gyeongsangnam-do, Korea, is abstracted as below. Seogo-jeongsa(西皐精舍) and Sameun-jeong(三隱亭) was intended to practise retirement with sense of unity with the head residence, since Seogo-jeongsa was 340m far and sameun-jeong was 630m far away from head residence, which set within 1km. Although Seogo-jeongsa's basic prop, Sameun-jeong's timber, which are both from designer's pen name and head residence name, "Hangjae(恒齋)" and "Yongjae(庸齋)," and Hanseoam have each different exposure, it is homogeneous as space configurational side in order to optimize the gaze leading effect and appreciated domestic trees from the pond, which is center of the garden and the significant facility. Method of direction of Seogo-jeongsa's Hwalsudang(活水塘) and Sameunjeong's pond of Sameun-ji(三隱 池) gets attention by distinction. Especially, directing of 3 step water flow from behind to front and traditional pond method, called Bangji-Bangdo(方池方島), were very unique that any other place can be found. Also, the middle islet on the pond at both villa, which isn't more supper, but more for the symbolic molding that seek a stone pagoda(石假山), is more interesting directing method as considering the relationship of the 3 stone pagoda imageries on the surface of Ghwayukchon(果肉泉) At the same time, Seogo-jeongsa and Sameun-jeong shows very different characteristics from traditional villa that adopted both domestic trees and foreign trees. Particularly, it is extremely exceptional case to plant vigorous needleleaf tree, such as Chamaecyparis pisifera, C. obtusa, Pinus bungeana, P. palustris, Sciadopitys verticillata, Metasequoia glyptostroboides and Cedrus deodara, on Sameun-jeong. Moreover, adopting foreign wild needleleaf trees for landscaping trees, such as a Torreya nucifera, Taxus cuspidata, P. parviflora, and foreign landsacping trees, such as P. bungeana, Cryptomeria japonica and C. obtusa tells planting trend of the late Joseon dysnasty era. Also, as we can know from 2 Jipgyeong(集景), which is 'Seogo-jabyoung 17 young(西皐雜詠十七詠)', and 'Sameun-jeong 12 Gyoung(三隱亭十二景)' which are set on both villa, the intend to expand the garden area is strongly shown by the natural forest directing. As a result, Seogo-jeongsa and Sameun-jeong, located at Toero-li Miryang, are sharing the traditional Joseon dynasty era's custom, such as space and visual composition; however, it is different and attractive garden remains as a point of view of water directing, stone pagodas, and adopting foreign landscaping trees.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.43-54
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• 2023

Due to climate change, drought and flood occurrences have been increasing. Accurate projections of watershed discharges are imperative to effectively manage natural disasters caused by climate change. However, climate change and hydrological model uncertainty can lead to imprecise analysis. To address this issues, we used two lumped models, IHACRES and GR4J, to compare and analyze the changes in discharges under climate stress scenarios. The Hapcheon and Seomjingang dam basins were the study site, and the Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) were used for parameter optimizations. Twenty years of discharge, precipitation, and temperature (1995-2014) data were used and divided into training and testing data sets with a 70/30 split. The accuracies of the modeled results were relatively high during the training and testing periods (NSE>0.74, KGE>0.75), indicating that both models could reproduce the previously observed discharges. To explore the impacts of climate change on modeled discharges, we developed climate stress scenarios by changing precipitation from -50 % to +50 % by 1 % and temperature from 0 ℃ to 8 ℃ by 0.1 ℃ based on two decades of weather data, which resulted in 8,181 climate stress scenarios. We analyzed the yearly maximum, abundant, and ordinary discharges projected by the two lumped models. We found that the trends of the maximum and abundant discharges modeled by IHACRES and GR4J became pronounced as changes in precipitation and temperature increased. The opposite was true for the case of ordinary water levels. Our study demonstrated that the quantitative evaluations of the model uncertainty were important to reduce the impacts of climate change on water resources.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.