• Proceedings of the Korean Society for the Gifted Conference
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• 1994.08a
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• pp.1.2-37
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• 1994

Currently Korea encourages gifted high schoolers and junior high schoolers to participate in international achievement contests such as International Olympiads. Participants for these contests are selected nationwide among gifted students in areas of mathematics, physics, chemistry, and others. They go through a series of screening tests and programs. One of the screening processes IS Korean Olympiad School, which provides study programs each summer for student-candidates prior to following year's International Olympiads. Approximately 40 students of high schools and junior high schools, in each subject of study, gather at Korean Olympiad Summer School, and they go through intensive study programs during a short period of time. Out of 40 candidates,' less than 10 students are finally selected to participate in International Olympiads. In this study, a psycho-educational program called "Situation Coping Training Program" was developed to enhance ahievement motivation for these student-candidates. This study was to see if this training program actually improved their cognitive, emotive motivation factors, and to see how this training program affected their achievement level. Training was administered for five days. This training program was found effective for participants to increase self-efficacy, internal locus of control, and anxiety copmg. These cognitive and emotive motivation factors, other than intelligence, were found to have positive relationship with achievement level, of which self-efficacy and attribution style of students were found as two best predictors of achievement. This training program was perceived as necessary. by participants, and helpful for recovering self-confidence and self-control as well as coping pressure. Suggestions were made that this kind of training program be administered as a regular curriculum in preparative study programs such as Korean Olympiads, since cognitive, emotive motivation factors are related with achievement, and furthermore, be utilized in all gifted education programs in Korea. in Korea.

• Sleep Medicine and Psychophysiology
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• v.12 no.1
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• pp.50-57
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• 2005

Objectives: This study was designed to evaluate the differences in clinical characteristics and severity of symptoms between panic patients with and without comorbid major depressive disorder, and to ascertain the differences in the function of the autonomic nerve system measured by heart rate variability (HRV). Methods: The subjects were 60 patients who have panic disorder without major depressive disorder and 19 patients who met DSMIV criteria for both panic disorder and major depressive disorder. First, they drew up symptom checklists and self-rating scales, and were measured by Anxiety Disorder Inventory Schedule-Panic Attack & Agoraphobia (ADIS-P&A), Clinical Global Impression (CGI), Hamilton Rating Scale for Depression (HAM-D), Panic Disorder Severity Scale (PDSS) and Heart Rate Variability (HRV). For statistical analysis, we performed t-test to compare the scores of self reported scales and clinician’s rating scales in panic patients with comorbid major depressive disorder and those without major depressive disorder. ANCOVA was used to compare the variables of HRV, considering age as a covariate. Results: The subjective severities of depression and anxiety that comorbid patients complained of were higher than those of patients with only panic disorder. Futhermore, comorbid patients were more sensitive to anxiety and physical sensations, and they tend to be more negative in their thinking. The scores of clinician-rating scales such as CGI and PDSS were also higher in the comorbid patients. However, there were no significant differences in HRV variables between both groups, despite a tendency to low heart rate variability in the comorbid group. Conclusion: This study suggests that patients with panic disorder and comorbid major depressive disorder tend to complain of more symptoms and to be more sensitive to various symptoms than those with panic disorder without comorbid depression. However, in this study comorbid major depressive disorder did not have a significant impact on the HRV variables of patients with panic disorder.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.227-237
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• 2020

Microclimatic data were measured, and the human thermal sensation was analyzed at 10 local climate zones based on the major land cover classification to investigate the thermal environment of urban areas during summer nighttime. From the results, the green infrastructure areas (GNIAs) showed an average air temperature of 1.6℃ and up to 2.4℃ lower air temperature than the gray infrastructure areas (GYIAs), and the GNIAs showed an average relative humidity of 9.0% and up to 15.0% higher relative humidity. The wind speed of the GNIAs and GYIAs had minimal difference and showed no significance at all locations, except for the forest location, which had the lowest wind speed owing to the influence of trees. The local winds and the surface roughness, which was determined based on the heights of buildings and trees, appeared to be the main factors that influenced wind speed. At the mean radiant temperature, the forest location showed the maximum value, owing to the influence of trees. Except at the forest location, the GNIAs showed an average decrease of 5.5℃ compared to GYIAs. The main factor that influenced the mean radiant temperature was the sky view factor. In the analysis of the human thermal sensation, the GNIAs showed a "neutral" thermal perception level that was neither hot nor cold, and the GYIAs showed a "slightly warm" level, which was a level higher than those of the GNIAs. The GNIAs showed a 3.2℃ decrease compared to the GYIAs, except at the highest forest location, which indicated a half-level improvement in the human thermal environment.

• The Journal of Engineering Geology
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• v.34 no.2
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• pp.229-248
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• 2024

This investigated the hydrogeochemical and isotopic characteristics of geothermal waters, groundwaters, and surface waters in Dongrae-gu, Busan, South Korea, in order to determine the origins of the salinity components in the geothermal waters, and their formation mechanisms and heat sources The geothermal waters are Na-Cl-type, distinct from surrounding groundwaters (Na-HCO₃^- and, Ca-HCO₃^- (SO₄, Cl)-type) and surface waters (Ca-HCO₃(SO₄, Cl)-type). This indicates the geothermal waters formed at depth as compared with the groundwaters. δ¹⁸O and δD values of the geothermal waters are relatively depleted as compared with the groundwaters, due to altitude effects and deep circulation of the geothermal waters. Helium and neon isotope ratios (³ He/⁴He and, ⁴He/²⁰Ne) of the geothermal waters plot on a single mixing line between mantle (³He = 3.76~4.01%) and crust (⁴He = 95.99~96.24 %), indirectly suggesting that the heat source is due to the decay of radioactive elements in rocks. The geothermal reservoir temperatures were calculated using the silica-enthalpy and Giggenbach models, yielding values of 82~130℃, and the depth of the geothermal reservoir is estimated to be 1.7~2.9 km below the surface. The correlation between Cl/Na and Cl/HCO₃ for the Dongrae geothermal waters requires the input of salty water. The supply of saline composition is interpreted due to the dissolution of residual paleo-seawater.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.38 no.1
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• pp.115-131
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• 2020

Through literature research and field investigation, this paper attempts to study the distribution, morphology and the typification of the visual and perceptual stone inscription in Wuyi-Gugok of China. The results are as follows: First, there are 350 stone inscriptions in total from the 1^st Gok to 9^th Gok in Wuyi-Gugok. Second, according to the analysis of the stone inscription distribution, 74(21.2%) stone inscriptions in the 5^th Gok, 67(19.2%) in the 6^th Gok, 65(18.6%) in the 1^st Gok, 60(17.2%) in the 2^nd Gok and 53(15.2%) in the 4^th Gok are confirmed. The above five Goks contain 319(91.1%) stone inscriptions, so they have rich cultural landscape. Third, according to the survey, the number of the stone inscriptions existed in the Sugwangseok of the 1^st Gok are 41(22.6%), in the Homagan of Cheonyubong of the 6^th Gok are 29(8.3%), in the Jesiam of the 4^th Gok are 23(6.6%), in the Nyeongam of the 2^nd Gok are 22(6.3%), in the Hyangseongam of the 6^th Gok are 21(6%), in the Unwa of the 5^th Gok are 19(5.4%), in the Bokhoam of the 5^th Gok are 18(5.1%), in the Eunbyeongbong of the 5^th Gok are 17(4.9%), in the Daejangbong of the 4^th Gok are 14(4%), in the Daewangbong of the 1^st Gok and the Geumgokam of the 4^th Gok are 12(3.4%). Thus, a total of 228 (65.1%) stone inscriptions are concentrated in these 11 sites, which represent the popularity and cultural value of these rocks. Fourth, the stone inscription of Wuyi-Gugok, praising the landform and topographical geological landscape of Mount Wuyi, mainly describe the scenic name of each Gok related to Zhu Xi's Gugok culture, appreciate Zhu Xi's tracks and the stone inscription in the sacred land of Neo-Confucianism culture, and also record the Confucian edification of mencius thoughts, Muigun(武夷君) and the myths and legends related to the site names of Wuyi mountain, which can remind people of the worldview of the celestial paradise where the gods live and the fairyland of the land of peach blossoms. In addition, it indicates that the historical and cultural landscape, which is full of colorful history and myths and legends, including allusions related to Confucian, buddhist and Taoist celebrities and the ancestor ancient things related to traditional culture of China is very diverse. Fifth, the results of the classification, based on the content of the stone inscription in Wuyi-Gugok, are classified as the scenery name inscription, the praise scene inscription, the recording travel inscription, the recording event inscription, the philosophy inscription, the expressing emotion inscription, the religion inscription, the inscription for auspiciousness, the slogan and expressing ambition inscription and the official document notice inscription, among which there are 102(29.1%) praise scene inscriptions, 93(26.6%) scenery name inscriptions and 61(17.4%) recording travel inscriptions. The stone inscriptions of Wuyi-Gugok have the characteristics of the special emphasis on scenery names, landscape praise and commemorative tours. Sixth, the analysis of the intertext between the 「Figure of Wuyi-Gugok」 and Wuyi-Gugok rock letters, in the study found that the method of propagation between media was mostly the method of propagation of quotations and maintained intermedia through extension, repetition, extension, and compression.

• 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.