• Journal of architectural history
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• v.1 no.1 s.1
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• pp.68-87
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• 1992

This study is the conditions of location according to formed the times in the clan village. The results of this study are as follows ; 1. in the 15th century, the characterestics of the village established residencial place where mountain stream flowed surrounded by the mountain and deep in the mountains with superior quality land. That's because Sa-dae-bu put equal importance on beautiful scenery and practical benefit for living. Stream House provided economical foundation for Sa-dae-bu to be able to keep confucial manners by putting limit their economic status to small medium sized land owner. Topographical condition such as valley or hollow separated from the exterior maintained unification of consanguineous village in self sufficient farming society and held on to independent territory against external to be able to stay away from turbulent days so that they formed residential area of Sa-dae-bu clan. And the valley where flowed clean water was considered as the connection of continuous place where distinctiveness of form in each curve and and factor of calm and dynamic scenery of the clean stream. Scholars in the middle of Chosun Dynasty located in the utopia as place for confucious retirement to study, a place for refinement by combination with the nature or as a way of spacial practice based on Confucious view of nature. 2. in the 16th-l7th century, Most of existing consanguineous villages adopt deep in the mountains for refuge. at that place, upward rank was established by settlement of the ancestor who entered in the village first, the principal was placed in the center of the village and since descendants became numerous, it was serialized as the space of descendants. So, it was arranged in the order of social rank. Most of the villages showed development step by step started from precaution by apperance of the mountain to the lower part. It's because the topography of valley around the village worked as the natural hedge against external force and genealogy of the clan, regularity of social status, order of entrance into the village were reflected into residencial destribution. Also, order of the rank coincided with the one of aspects on geomancy. Genealogical rank within the village represented spacial rank. Houses of descendants and branch families were placed lower than the principal which showed worship to the principal. 3. In 18th century after, as the village was settled nearby cultivated land considering economical loss caused by long distance between residencial area and cultivated land, direction of sect followed by development of village expanded from the front part of the village to the rear part. The principal that was poped out to the front presented frontage over exterior. Therefore, residencial area of branch families expanded to the rear starting from the principal. This represented a slice of social structure at that time. after 18th century, spirit was percieved superior over material, After then, development of cultivation and expantion of land created difference of economic strength within one village. In order to maintain and show off the status of Yang-ban, economic power of indigenous land owner became fundamental, so, sense to worship and to keep the principal became weak eventually. Taking advantage of that situation, residencial area of branch family expanded to the rear part of the principal which showed dual disposition conflicted with each other. However, these clan rules were destroyed and new rules were created after 18th century because of the situation and consciousness at that time.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.2
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• pp.111-123
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• 2022

Carbonate green rust (CGR) and sulfate green rust (SGR) commonly occur in nature. In this study, CGR and SGR were synthesized through co-precipitation, and their formation mechanisms and physicochemical properties were investigated. X-ray diffraction (XRD) and Rietveld refinement showed both CGR and SGR with layered double hydroxide structure were successfully synthesized without any secondary phases under each synthetic condition. Refined structural parameters (unit cell) for two green rusts were a (=b) = 3.17 Å and c = 22.52 Å for CGR and a (=b) = 5.50 Å and c = 10.97 Å for SGR with the crystallite size 57.8 nm in diameter from (003) reflection and 40.1 nm from (001) reflections, respectively. Scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDS) results showed that both CGR and SGR had typical hexagonal plate-like crystal morphologies but their chemical composition is different in the content of C and S. In addition, Fourier transform infrared (FT-IR) spectroscopy analysis revealed that carbonate (CO₃^2-) and sulfate (SO₄^2-) molecules were occupied as interlayer anions of CGR and SGR, respectively. These SEM/EDS and FT-IR results were in good agreement with XRD results. Changes in the solution chemistry (i.e., pH, Eh and residual iron concentrations (Fe(II):Fe(III)) of the mixed solution) were observed as a function of the injection time of hydroxyl ion (OH^-) into the iron solution. Three different stages were observed in the formation of both CGR and SGR; precursor, intermediator, and green rust in the formation of both CGR and SGR. This study provides co-precipitation methods for CGR and SGR in a way of the stable synthesis. In addition, our findings for the formation mechanisms of the two green rusts and their physicochemical properties will provide crucial information with researches and industrials in utilizing green rust.

• Economic and Environmental Geology
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• v.25 no.3
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• pp.337-349
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• 1992

The Tertiary basins in Korea have widely been studied by numerous researchers producing individual results in sedimentology, paleontology, stratigraphy, volcanic petrology and structural geology, but interdisciplinary studies, inter-basin analysis and basin-forming process have not been carried out yet. Major work of this study is to elucidate evidences obtained from different parts of a basin as well as different Tertiary basins (Pohang, Changgi, Eoil, Haseo and Ulsan basins) in order to build up the correlation between the basins, and an overall picture of the basin architecture and evolution in Korea. According to the paleontologic evidences the geologic age of the Pohang marine basin is dated to be late Lower Miocence to Middle Miocene, whereas other non-marine basins are older as being either Early Miocene or Oligocene(Lee, 1975, 1978: Bong, 1984: Chun, 1982: Choi et al., 1984: Yun et al., 1990: Yoon, 1982). However, detailed ages of the Tertiary sediments, and their correlations in a basin and between basins are still controversial, since the basins are separated from each other, sedimentary sequence is disturbed and intruded by voncanic rocks, and non-marine sediments are not fossiliferous to be correlated. Therefore, in this work radiometric, magnetostratigraphic, and biostratigraphic data was integrated for the refinement of chronostratigraphy and synopsis of stratigraphy of Tertiary basins of Korea. A total of 21 samples including 10 basaltic, 2 porphyritic, and 9 andesitic rocks from 4 basins were collected for the K-Ar dating of whole rock method. The obtained age can be grouped as follows: $14.8{\pm}0.4{\sim}15.2{\pm}0.4Ma$, $19.9{\pm}0.5{\sim}22.1{\pm}0.7Ma$, $18.0{\pm}1.1{\sim}20.4+0.5Ma$, and $14.6{\pm}0.7{\sim}21.1{\pm}0.5Ma$. Stratigraphically they mostly fall into the range of Lower Miocene to Mid Miocene. The oldest volcanic rock recorded is a basalt (911213-6) with the age of $22.05{\pm}0.67Ma$ near Sangjeong-ri in the Changgi (or Janggi) basin and presumed to be formed in the Early Miocene, when Changgi Conglomerate began to deposit. The youngest one (911214-9) is a basalt of $14.64{\pm}0.66Ma$ in the Haseo basin. This means the intrusive and extrusive rocks are not a product of sudden voncanic activity of short duration as previously accepted but of successive processes lasting relatively long period of 8 or 9 Ma. The radiometric age of the volcanic rocks is not randomly distributed but varies systematically with basins and localities. It becomes generlly younger to the south, namely from the Changgi basin to the Haseo basin. The rocks in the Changgi basin are dated to be from $19.92{\pm}0.47$ to $22.05{\pm}0.67Ma$. With exception of only one locality in the Geumgwangdong they all formed before 20 Ma B.P. The Eoil basalt by Tateiwa in the Eoil basin are dated to be from $20.44{\pm}0.47$ to $18.35{\pm}0.62Ma$ and they are younger than those in the Changgi basin by 2~4 Ma. Specifically, basaltic rocks in the sedimentary and voncanic sequences of the Eoil basin can be well compared to the sequence of associated sedimentary rocks. Generally they become younger to the stratigraphically upper part. Among the basin, the Haseo basin is characterized by the youngest volcanic rocks. The basalt (911214-7) which crops out in Jeongja-ri, Gangdong-myon, Ulsan-gun is $16.22{\pm}0.75Ma$ and the other one (911214-9) in coastal area, Jujon-dong, Ulsan is $14.64{\pm}0.66Ma$ old. The radiometric data are positively collaborated with the results of paleomagnetic study, pull-apart basin model and East Sea spreading theory. Especially, the successively changing age of Eoil basalts are in accordance with successively changing degree of rotation. In detail, following results are discussed. Firstly, the porphyritic rocks previously known as Cretaceous basement (911213-2, 911214-1) show the age of $43.73{\pm}1.05$ 및 $49.58{\pm}1.13Ma$(Eocene) confirms the results of Jin et al. (1988). This means sequential volcanic activity from Cretaceous up to Lower Tertiary. Secondly, intrusive andesitic rocks in the Pohang basin, which are dated to be $21.8{\pm}2.8Ma$ (Jin et al., 1988) are found out to be 15 Ma old in coincindence with the age of host strata of 16.5 Ma. Thirdly, The Quaternary basalt (911213-5 and 911213-6) of Tateiwa(1924) is not homogeneous regarding formation age and petrological characteristics. The basalt in the Changgi basin show the age of $19.92{\pm}0.47$ and $22.05{\pm}0.67$ (Miocene). The basalt (911213-8) in Sangjond-ri, which intruded Nultaeri Trachytic Tuff is dated to be $20.55{\pm}0.50Ma$, which means Changgi Group is older than this age. The Yeonil Basalt, which Tateiwa described as Quaternary one shows different age ranging from Lower Miocene to Upper Miocene(cf. Jin et al., 1988: sample no. 93-33: $10.20{\pm}0.30Ma$). Therefore, the Yeonil Quarterary basalt should be revised and divided into different geologic epochs. Fourthly, Yeonil basalt of Tateiwa (1926) in the Eoil basin is correlated to the Yeonil basalt in the Changgi basin. Yoon (1989) intergrated both basalts as Eoil basaltic andesitic volcanic rocks or Eoil basalt (Yoon et al., 1991), and placed uppermost unit of the Changgi Group. As mentioned above the so-called Quarternary basalt in the Eoil basin are not extruded or intruaed simultaneously, but differentiatedly (14 Ma~25 Ma) so that they can not be classified as one unit. Fifthly, the Yongdong-ri formation of the Pomgogri Group is intruded by the Eoil basalt (911214-3) of 18.35~0.62 Ma age. Therefore, the deposition of the Pomgogri Group is completed before this age. Referring petrological characteristics, occurences, paleomagnetic data, and relationship to other Eoil basalts, it is most provable that this basalt is younger than two others. That means the Pomgogri Group is underlain by the Changgi Group. Sixthly, mineral composition of the basalts and andesitic rocks from the 4 basins show different ground mass and phenocryst. In volcanic rocks in the Pohang basin, phenocrysts are pyroxene and a small amount of biotite. Those of the Changgi basin is predominant by Labradorite, in the Eoil by bytownite-anorthite and a small amount pyroxene.

• 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.