• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.4
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• pp.207-216
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• 2014

Total organic carbon (TOC) is an important indicator used as an direct biological index in the research field of the marine carbon cycle. It is possible to produce the sufficient TOC estimation data by using the Chemical Oxygen Demand(COD) data because the available TOC data is relatively poor than the COD data. The outlier detection and treatment (removal) should be carried out reasonably and objectively because the equation for a COD-TOC conversion is directly affected the TOC estimation. In this study, it aims to suggest the optimal regression model using the available salinity, COD, and TOC data observed in the Korean coastal zone. The optimal regression model is selected by the comparison and analysis on the changes of data numbers before and after removal, variation coefficients and root mean square (RMS) error of the diverse detection methods of the outlier and influential observations. According to research result, it is shown that a diagnostic case combining SIQR (Semi - Inter-Quartile Range) boxplot and Cook's distance method is most suitable for the outlier detection. The optimal regression function is estimated as the TOC(mg/L) = $0.44{\cdot}COD(mg/L)+1.53$, then determination coefficient is showed a value of 0.47 and RMS error is 0.85 mg/L. The RMS error and the variation coefficients of the leverage values are greatly reduced to the 31% and 80% of the value before the outlier removal condition. The method suggested in this study can provide more appropriate regression curve because the excessive impacts of the outlier frequently included in the COD and TOC monitoring data is removed.

• Korean Journal of Food Science and Technology
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• v.48 no.6
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• pp.536-541
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• 2016

The purpose of this study was to evaluate changes in the quality characteristics of mackerel fish fillet under freezing storage conditions. The changes in product quality were determined by measuring pH, volatile basic nitrogen (VBN); thiobarbituric acid (TBA), lightness ($L^*$), redness ($a^*$), yellowness ($b^*$) values, shearing force, and microbiological evaluation (Total Plate Count, TPC) during 21 days of storage at -20, -5, and $-1^{\circ}C$, and their shelf-lives were established. TPC as an effective quality indicator was used to estimate the shelf-life via linear regression analysis. The TPC increased steadily with increasing storage periods at every temperature (p<0.05) and showed a significant relationship with temperature at $-1^{\circ}C$ (r=0.949), $-5^{\circ}C$ (r=0.919), and $-20^{\circ}C$ (r=0.905). Therefore, based on TPC, the shelf life of the mackerel fillet was noted to be 32 days when stored at $-1^{\circ}C$, more than 51 days when stored at $-5^{\circ}C$, and about 2 months when stored at $-20^{\circ}C$.

• Land and Housing Review
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• v.12 no.3
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• pp.11-26
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• 2021

With the increasing demand for office space, there have been questions on how office rent distribution produces a change in the urban spatial structure in Seoul. The purpose of this paper is to investigate a relative price gradient and to present a time-series model that can quantitatively explain the dynamic changes in the urban spatial structure. The analysis was dealt with office rent above 3,306 m² for the past 10 years from 1Q 2010 to 4Q 2019 within Seoul. A modified repeat sales model was employed. The main findings are briefly summarized as follows. First, according to the estimates of the office price gradient in the three major urban centers of Seoul, the CBD remained at a certain level with little change, while those in the GBD and the YBD continued to increase. This result reveals that the urban form of Seoul has shifted from monocentric to polycentric. This shows that the spatial distribution of companies has gradually accelerated decentralized concentration implying that the business networks have become significant. Second, contrary to small and medium-sized office buildings that have undertaken no change in the gradient, large office buildings have seen an increase in the gradient. The relative price gradients in small and medium-sized buildings were inversely proportional among the CBD, the GBD, and the YBD, implying their heterogeneous submarkets by office rent movements. Presumably, those differences in the submarkets were attributed to investment attraction, industrial competition, and the credit and preference of tenants. The findings are consistent with the hierarchical system identified in the Seoul 2030 Plan as well as the literature about Seoul's urban form. This research claims that the proposed method, based on the modified repeat sales model, is useful in understanding temporal dynamic changes. Moreover, the findings can provide implications for urban growth strategies under rapidly changing market conditions.

• Korean Journal of Food Science and Technology
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• v.48 no.4
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• pp.301-305
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• 2016

This study was conducted to investigate changes in pH, volatile basic nitrogen (VBN), thiobarbituric acid (TBA), $a^*$ (redness), shearing force, and microbial content (total plate count; TPC) in beef during freezing storage at -1, -5, and $-20^{\circ}C$ for 21 days. TPC as an effective quality indicator was used to identify the decay point by regression analysis. The pH, TBA values, and TPC significantly increased with storage at all tested temperatures (p<0.05). VBN significantly increased and shearing force significantly decreased with storage at -1 and $-5^{\circ}C$ (p<0.05). TPC showed significant correlation with temperature at $-1^{\circ}C\;(R^2=0.891)$, $-5^{\circ}C\;(R^2=0.856)$, and $-20^{\circ}C\;(R^2=0.444)$. The decay points at -1, -5, and $-20^{\circ}C$, were 27, 52, and 84 days, respectively. The results suggest that a freezing temperature of $-5^{\circ}C$ is effective for short-term storage of beef.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.677-687
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• 2016

Among lots of sabermetric statistics for baseball batters' ability, the wins above replacement (WAR) is the most popular statistic in MLB. However, there exists a difficulty applying WAR to KBO, since KBO data do not have position adjustment, league adjustment and park factor which are essential in calculating WAR. In this paper, using five statistics for both KBO and MLB qualified batters, we propose hitting ability index (HAI), an alternative sabermetric indices to represent batters' ability. Comparing HAI with WAR of MLB batters, we evaluate the validity of HAI and then applied HAI to 2015 KBO data in which HAI is analyzed statistically with respect to different teams, ages, and positions. Moreover, the linear relationship between KBO batter's HAI and their annual salary is discussed. Grouping 46 KBO batters based on confidence region of the regression model for annual salary, we also statistically investigate batter's annual salary in these groups with respect to several factors.

• Radiation Oncology Journal
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• v.22 no.4
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• pp.298-306
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• 2004

Purpose: The purpose of this study was to identify Radiosensitivity of proteins in tissues with different radiosensitivity. Materials and Methods: C3H/HeJ mice were exposed to 10 Gy. The mice were sacrifiud 8 hrs after radiation. Their spleen and liver tissues were collected and analyzed histologicaly for apoptosis. The expressions of radiosusceptibillty protein were analyzed by 2-dimensional electrophoresis and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Resilts: The Peak of apoptosis levels were $35.3{\pm}1.7{\%}$ in spleen and $0.6{\pm}0.2{\%}$ in liver at 8 hrs after radiation. Liver, radioresistant tissues, showed that the levels of ROS metabolism related to proteins such as cytochromm c, glutathione S transferase, NADH dehydrogenase, riken cDNA and peroxiredoxin Vl increased after radiation. The expression of cytochrome c increased significantly in spleen and liver tissues after radiation. In spleen, radiosensitivity tissue, the identified proteins showed a significantly quantitative alteration following radiation. It was categorized as signal transduction, apoptosis, cytokine, Ca signal related protein, stress-related protein, cytoskeletal regulation, ROS metabolism, and others. Conclusion: Differences of radiation-induced apoptosis by tissues specifted were coupled with the induction of related radiosensitivity and radioresistant proteins. The result suggests that apoptosis relate protein and redox proteins play important roles in this radiosusceptibility.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.719-728
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• 2019

Valve in water distribution network (WDN), that controls the flow in pipes, is used to isolate a segment (a part of WDN) under abnormal water supply conditions (e.g., pipe breakage, water quality failure event). The segment isolation degrades pressure and water serviceability in neighboring area during the water service outage of the segment. Recent hydraulic and water quality failure events reported encouraging WDN valve installation based on various abnormal water supply scenarios. This study introduces a scenario-based optimal valve installation approach to optimize the number of valves, the amount of undelivered water, and a shortest water supply path indicator (i.e., Hydraulic Geodesic Index). The proposed approach is demonstrated in the valve installation of Pescara network, and the optimal valve sets are obtained under multiple scenarios and compared to the existing valve set. Pressure-driven analysis (PDA) scheme is used for a network hydraulic simulation. The optimal valve set derived from the proposed method has 19 fewer valves than the existing valve set in the network and the amount of undelivered water was also lower for the optimal valve set. Reducing the reservoir head requires a greater number of valves to achieve the similar functionality of the WDN with the optimal valve set of the original reservoir head. This study also compared the results of demand-driven analysis (DDA) and the PDA and confirmed that the latter is required for optimal valve installation.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.2
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• pp.221-226
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• 2012

For the investigation of a possibility as a useful functional material, 6 cultivars (Takinosawa Gold, Kawanakawase Hakuto, Madoka, Yumefuji, Nagasawa Hakuho, Hong Bak) of Prunus persica L. Batsch were studied at unripe stage to determine the physicochemical properties and chemical compositions. The cultivars were picked in late May, and all samples were analyzed for external properties, physicochemical properties, pH, Brix value, Hunter's color value, hardness, vitamin C, and reducing sugar. The size of the fruit from all six cultivars was compared, and it was determined that cultivars, fruit from Madoka was the largest, while that from Yumefuji was the smallest. Comparing fresh weight, the fruit from Yumefuji was lowest in moisture contents (89.13~89.96%), and that from Nagasawa Hakuho had significantly higher crude protein (1.02~1.62%). The contents of crude lipids (0.18~0.23%) and carbohydrates (8.00~9.35%) were not significantly different between cultivars and Madoka included higher crude ash contents (0.32~0.69%) than other cultivars. The pH of 6 cultivars from unripe peaches were significantly higher from Kawanakawase Hakuto, and the Brix value was also highest from Kawanakawase Hakuto, followed by Yumefuji, Madoka, Nagasawa Hakuho, Takinosawa Gold, and Hong Bak. In chromaticity, the L value, the indicator of brightness, was significantly higher in fruit from Nagasawa Hakuho. The a value, the indicator of redness, was the highest with Hong Bak and overall lower than -5. The b value, the indicator of yellowness, was the highest in fruit from Madoka and ranged from 16.51 to 18.33. In physical characteristics, the hardness of the unripe peaches was the highest in fruit from Hong Bak, and overall, white peaches have a higher hardness value than yellow peaches. The vitamin C content of the fruit didn't show any significant differences between cultivars, and the reducing sugar showed a higher percent than 6.34% in fruit from all cultivars. These results suggest that unripe peaches were commensurate with the development of natural pigment and as a functional foods.

• Journal of the Korean Wood Science and Technology
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• v.44 no.1
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• pp.85-95
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• 2016

In this study, furfural, which is one of the value-added chemicals, was produced from the hydrolyzate of Quercus mongolica using dilute acid pretreatment, and the optimal pretreatment condition was determined by Response Surface Methodology (RSM) to obtain high yield of furfural. Based on Central Composite Design, the pretreatment experiment was designed with parameters such as reaction temperature ($X_1$), acid concentration ($X_2$), and reaction time ($X_3$) as independent variables, while dependent variable was furfural concentration (Y), and furfural yield (Z) was shown as percentage of Y per a dry weight basis. According to results of RSM, it was confirmed that reaction temperature ($X_1$) was the most influence factor and reaction temperature ($X_1$)-acid concentration ($X_2$) was the most significant interaction factor on furfural yield. Also, the optimal condition for the highest furfural yield was predicted at reaction temperature of $184^{\circ}C$, acid concentration of 1.17%, and reaction time of 5 min by RSM, and expected maximum yield of furfural was 6.37%. Experimentally, the maximum yield of furfural produced at above optimal condition was 6.21%, and it was considerably similar with the predicted value, and therefore the model for furfural production from the hydrolyzate of Quercus mongolica during dilute acid pretreatment could be built using RSM.