• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.553-581
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• 2022

Shortly after tunnel boring machine (TBM) was introduced in the tunneling industry, the use of TBM has surprisingly increased worldwide due to its performance together with the benefit of being safely and environmentally friendly. One of the main cost items in the TBM tunneling in rock and soil is changing damaged or worn cutters. It is because that the cutter change is a time-consuming and costly activity that can significantly reduce the TBM utilization and advance rate and has a major effect on the total time and cost of TBM tunneling projects. Therefore, the importance of accurately evaluating the cutter life can never be overemphasized. However, the prediction of cutter wear in soil, rock including mixed face is very complex and not yet fully clarified, subsequently keeping engineers busy around the world. Various prediction models for cutter wear have been developed and introduced, but these models almost usually produce highly variable results due to inherent uncertainties in the models. In this study, a case study of design and construction of disc cutter change is introduced and analyzed, rather than proposing a prediction model of cutter wear. As the disc cutter is strongly affected by the geological condition, TBM machine characteristic and operation, authors believe it is very hard to suggest a generalized prediction model given the uncertainties and limitations therefore it would be more practical to analyze a real case and provide a detailed discussion of the difference between prediction and result for the cutter change. By doing so, up-to-date idea about planning and execution of cutter change in practice can be promoted.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.33-45
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• 2020

This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.2
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• pp.77-84
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• 2022

This study aimed to evaluate the growth, yield, and grain quality characteristics of rice varieties that were subjected to water stress during the tillering stage. We also sought to identify whether water stress could be indicated via the plants' response to chlorophyll fluorescence. In this study, we induced water stress by intentionally cutting off water for 30 days, starting 15 days after transplanting the rice varieties to a greenhouse. We analyzed nine rice varieties, including Ilpum, which is the most frequently cultivated variety in Gyeongsangbuk-do, South Korea. The control group was planted in a paddy field where irrigation was entirely dependent on rainfall. Our results revealed that the heading stage of the nine studied varieties occurred approximately ten days earlier in the rain shelter than in the field. Moreover, the rice yield, head rice rate, and palatability score decreased by 18.6%, 17.1%, and 8.3%, respectively, while protein content increased by 20.2% compared with the control group. The Saenuri and Haimi varieties showed the lowest reduction in yield under the water stress conditions, while the Daebo and Samkwang varieties showed the highest reduction in yield. The chlorophyll fluorescence response after re-irrigation was measured between July 30^th and August 17^th. The ratio of variable fluorescence to maximum chlorophyll fluorescence (F_V/F_M) values failed to recover to their baseline values, resulting in either no change or a reduction in fluorescent response, even after re-irrigation of Daebo and Samkwang varieties. These results can be utilized as empirical data for drought-affected farms to select resistant varieties that can respond to spring drought in the southern plains of Gyeongsangbuk-do.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.1-10
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• 2023

This study aimed to confirm the importance ratio of climate and management variables on production of orchardgrass in Korea (1982-2014). For the climate, the mean temperature in January (MTJ, ℃), lowest temperature in January (LTJ, ℃), growing days 0 to 5 (GD 1, day), growing days 5 to 25 (GD 2, day), Summer depression days (SSD, day), rainfall days (RD, day), accumulated rainfall (AR, mm), and sunshine duration (SD, hr) were considered. For the management, the establishment period (EP, 0-6 years) and number of cutting (NC, 2nd-5th) were measured. The importance ratio on production of orchardgrass was estimated using the neural network model with the perceptron method. It was performed by SPSS 26.0 (IBM Corp., Chicago). As a result, EP was the most important variable (100%), followed by RD (82.0%), AR (79.1%), NC (69.2%), LTJ (66.2%), GD 2 (63.3%), GD 1 (61.6%), SD (58.1%), SSD (50.8%) and MTJ (41.8%). It implies that EP, RD, AR, and NC were more important than others. Since the annual rainfall in Korea is exceed the required amount for the growth and development of orchardgrass, the damage caused by heavy rainfall exceeding the appropriate level could be reduced through drainage management. It means that, when cultivating orchardgrass, factors that can be controlled were relatively important. Although it is difficult to interpret the specific effect of climates on production due to neural networking modeling, in the future, this study is expected to be useful in production prediction and damage estimation by climate change by selecting major factors.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.2
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• pp.203-209
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• 2009

Yield monitoring is one of a precision agriculture technology that is used most widely. It is spatial variability analysis of yield information that should be attained with yield monitoring system development. This experiment was conducted to evaluate spatial variability of yield and grain moisture content in rice paddy field, and their relationships to rice productivity. It is necessary to minimize sampling interval for accurate yield map making or to control cutting width of rice combine. Considering small rice plots such as $0.2{\sim}0.4$ ha, optimum size of sampling plot was below 15 m more than 5 m in with and length. In variable rate treatment field, average yield was similar, but yield variation was reduced than conventional field. Gap of yield by another plot in same field was bigger than half of average yield than yield variation was significantly big. Therefore yield measuring flow sensor must be able to measure at least 300 kg/10a more than 1000 kg/10a. Variation of moisture content in same field was not big and spatial dependance did not appear greatly. But, variation between different field is appeared difference according to weather circumstance before harvesting. Change of spatial dependence of yield was not big, because of field variation of moisture content is not big.

• Horticultural Science & Technology
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• v.29 no.5
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• pp.456-460
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• 2011

This study was carried out to compare the pattern of mutant variation and to evaluate the characteristics of mutants obtained by gamma irradiation in rose 'Kardinal'. Forty four rooted cuttings of 'Kardinal' were irradiated at 70 Gy gamma-ray dose from a $^{60}Co$ source to induce mutants in 2002. The irradiated plants were planted in field, and observed spotting of petal color mutants from 2002 to 2004. Four different kinds of mutant twigs with each different color flower were obtained from the irradiated 'Kardinal' with red petal. After being identified to be a stable mutant from 2004 to 2008, each mutant line propagated by cutting was hydroponic-cultured to evaluate the characteristics in the greenhouse from 2008 to 2009. Four mutant lines obtained from 'Kardinal' with red petal (Red group, 44A, 45B) include KA1 with light pink petal (Red group, 55B-55D), KA2 with pink petal (Red group, 63A-63B), KA3 with deep pink (Red purple, N57A-N57C), and KA4 with orange red (Red group, 43A-43B). Diameters of each flower in four mutant lines were different from 'Kardinal'. The line KA1 was 9.5 cm wide, and it showed the smallest diameter when compared to other mutants. While the line KA2 was the largest one with 12.5 cm 'Kardinal'. Petal number per flower was also variable among the mutants. The line KA2 had 39.8 petals being the largest number among the mutants, while the line KA1 was the lowest one compared to 35.5 petals of 'Kardinal'. Petal color was measured by using colorimeter. Brightness (L) measured at each petal of four mutants increased more than 'Kardinal'. CIE Lab values, a and b decreased more than 'Kardinal' at the petal color of three mutants except the line KA4. Characteristics of shoot, leaf, etc. from four mutants were also different from the ones of 'Kardinal'. The line KA1 was shortest in shoot, node and peduncle length, and lowest in prickle number. The reverse side of leaves was reddish green color in 'Kardinal' as well as the line KA4, but green color in the line KA1, KA2, and KA3.

• Asian Journal of Turfgrass Science
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• v.21 no.1
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• pp.23-37
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• 2007

This study was carried out to investigate the characteristics of root growth in Kentucky bluegrass sod(KBS) and creeping bentgrass sod(BGS) transplanted in summer(August 9) and fall(September 19), respectively. Hydroponic system was also used to observe rooting development in the study. Root development differed in KBS by transplanting time. It reached to more than 5 cm after 100 days In summer and 50 days in fall. However, BGS's root grew over 6cm after 40 days, regardless of the season. There was no significant differences in BGS, regardless of any cutting treatment. In the case of KBS, it was best with sod culled with 0.5cm deep and 1.5cm long. But it grew beyond 5 cm in root growth under any treatment after 40 days in transplanting. In a hydroponic study, BGS produced root over 100cm for 80 days through a summer season. However, the root of KBS did only grow in condition below $20^{\circ}C$. These results indicated that root growth characteristics were variable in BGS and KBS. It was considered that rooting development of BGS might be improved with sufficient irrigation in summer, and KBS grows well in lower temperature of $10{\sim}18^{\circ}C$, as compared with BGS. As to establishing the lawn with a sodding method, it should be careful in transplanting time, especially KBS.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.160-168
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• 2012

This study generated regression models to estimate the carbon storage and uptake from the urban deciduous landscape trees through a direct harvesting method, and established essential information to quantify carbon reduction from urban greenspace. Tree species for the study included Acer palmatum, Zelkova serrata, Prunus yedoensis, and Ginkgo biloba, which are usually planted as urban landscape trees. Tree individuals for each species were sampled reflecting various diameter sizes at a given interval. The study measured biomass for each part including the roots of sample trees to compute the total carbon storage per tree. Annual carbon uptake per tree was quantified by analyzing radial growth rates of stem samples at breast height. The study then derived a regression model easily applicable in estimating carbon storage and uptake per tree for the 4 species by using diameter at breast height(dbh) as an independent variable. All the regression models showed high fitness with $r^2$ values of 0.94~0.99. Carbon storage and uptake per tree and their differences between diameter classes increased as the diameter sizes got larger. The carbon storage and uptake tended to be greatest with Zelkova serrata in the same diameter sizes, followed by Prunus yedoensis and Ginkgo biloba in order. A Zelkova serrata tree with 15cm in dbh stored about 54kg of carbon and annually sequestered 7 kg, based on a regression model for the species. The study has broken new grounds to overcome limitations of the past studies which substituted, due to a difficulty in direct cutting and root digging of urban landscape trees, coefficients from the forest trees such as biomass expansion factors, ratios of below ground/above ground biomass, and diameter growth rates. Study results can be useful as a tool or skill to evaluate carbon reduction by landscape trees in urban greenspace projects of the government.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.5
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• pp.13-21
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• 2014

This study generated regression models to quantify storage and annual uptake of carbon from five native landscape tree species through a direct harvesting method, and established essential information to estimate carbon reduction effects from urban greenspaces. Tree species for the study included the Chionanthus retusus, Prunus armeniaca, Abies holophylla, Cornus officinalis, and Taxus cuspidata, which are usually planted in cities of middle Korea, but for which no information on carbon reduction is available. Ten tree individuals for each species were sampled reflecting various stem diameter sizes at a given interval. The study measured biomass for each part including the roots of sample trees to compute total carbon storage per tree. The annual carbon uptake per tree was quantified by analyzing the radial growth rates of stem samples at breast height or ground level. Regression models were developed using diameter at breast height (dbh) or ground level (dg) as an independent variable to easily estimate storage and annual uptake of carbon per tree for each species. All the regression models showed high fitness with $r^2$ values of 0.92~0.99. Storage and annual uptake of carbon from a tree with dbh of 10 cm were greatest with C. retusus (20.0 kg and 5.9 kg/yr, respectively), followed by P. armeniaca (17.5 kg and 4.5 kg/yr) and A. holophylla (13.2kg and 1.8 kg/yr) in order. A C. officinalis tree and T. cuspidata tree with dg of 10 cm stored 9.3 and 6.3 kg of carbon and annually sequestered 3.2 and 0.6 kg, respectively. The above-mentioned carbon storage equaled the amount of carbon emitted from gasoline consumption of about 23~35 L for C. retusus, P. armeniaca, and A. holophylla, and 11~16 L for C. officinalis and T. cuspidata. A tree with the diameter size of 10 cm annually offset carbon emissions from gasoline use of about 6~10 L for C. retusus, P. armeniaca, and C. officinalis, and 1~3 L for A. holophylla and T. cuspidata. The study breaks new ground to easily quantify biomass and carbon reduction for the tree species by overcoming difficulties in direct cutting and root digging of urban landscape trees.