• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.5
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• pp.66-77
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• 2019

This study investigated the feasibility of wintering evergreen broad-leaf trees in the Incheon coastal area through a climate analysis. The coldest monthly mean air temperature ranged from $-2.9^{\circ}C{\sim}-1.6^{\circ}C$. The warmth index of the coastal area of Incheon ranged from $98.89^{\circ}C{\cdot}month-109.03^{\circ}C{\cdot}month$, while the minimum air temperature year ranged from $-13.9^{\circ}C{\sim}-3.6^{\circ}C$. This proved that the Incheon coastal area was not suitable for evergreen broad-leaf trees to grow as the warmth index ranges from $101.0^{\circ}C{\cdot}month{\sim}117.0^{\circ}C{\cdot}month$, and the temperature year-round is $-9.2^{\circ}C$ or higher. This suggests the coastal areas of Incheon is not suitable for the growth of evergreen broad-leaf trees, however some evergreen broad-leaf trees lived in some parts of the area. Wind speed reduction and temperature effect simulations were done using Landschaftsanalyse mit GIS program. As a result of the simulations of wind speed reduction and temperature effects affecting the evergreen broad-leaf trees, it was discovered that a coastal wind velocity of 8.6m/sec was alleviated to be 5m/sec~7m/sec when the wind reached the areas where evergreen broad-leaf trees were present. It was also discovered that species that grew in contact with buildings benefited from a temperature increase of $1.1^{\circ}C{\sim}3.4^{\circ}C$ due to the radiant heat released by the building. Simulation results show that the weather factors affecting the winter growth damages of evergreen broad-leaved trees were wind speed reduction and local warming due to buildings. The wind speed reduction by shielding and local warming effects by buildings have enabled the wintering of evergreen broad-leaved trees. Also, evergreen broad-leaved trees growing in the coastal area of Incheon could be judged to be gradually adapting to low temperatures in winter. This study reached the conclusion that the blockage of wind, and the proximity of buildings, are required for successfully wintering evergreen broad-leaf trees in the coastal area of Incheon.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.1
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• pp.67-75
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• 2018

Purpose The purpose of this study is to investigate and analyze the external dose rates of $^{18}F-FDG$ Whole Body PET/CT patients by distance, and to identify the main factors that contribute to the reduction of radiation dose by checking the cumulative doses of nuclear medicine technologist(NMT). Materials and Methods After completion of the $^{18}F-FDG$ Whole Body PET/CT scan($75.4{\pm}3.3min$), the external dose rates of 106 patients were measured at a distance of 0, 10, 30, 50, and 100 cm from the chest. Gender, age, BMI(Body Mass Index), fasting time, diabetes mellitus, radiopharmaceutical injection information, creatine value were collected to analyze individual factors that could affect external dose rates from a patient's perspective. From the perspective of NMT, personal pocket dosimeters were worn on the chest to record accumulated dose of NMT who performed the injection task($T_1$, $T_2$ and $T_3$) and scan task($T_4$, $T_5$ and $T_6$). In addition, patient contact time with NMT was measured and analyzed. Results External dose rates from the patient for each distance were calculated as $246.9{\pm}37.6$, $129.9{\pm}16.7$, $61.2{\pm}9.1$, $34.4{\pm}5.9$, and $13.1{\pm}2.4{\mu}Sv/hr$ respectively. On the patient's aspect, there was a significant difference in the proximity of gender, BMI, Injection dose and creatine value, but the difference decreased as the distance increased. In case of dialysis patient, external dose rates for each distance were exceptionally higher than other patients. On the NMT aspect, the doses received from patients were 0.70, 1.09, $0.55{\mu}Sv/person$ for performing the injection task($T_1$, $T_2$, and $T_3$), and were 1.25, 0.82, $1.23{\mu}Sv/person$ for performing the scan task($T_4$, $T_5$, $T_6$). Conclusion we found that maintaining proper distance with patient and reducing contact time with patient had a significant effect on accumulated doses. Considering those points, efforts such as sufficient water intake and encourage of urination, maintaining the proper distance between the NMT and the patient(at least 100 cm), and reducing the contact time should be done for reducing dose rates not only patient but also NMT.