• Journal of the Korean Institute of Landscape Architecture
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• v.35 no.6
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• pp.84-96
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• 2008

This research investigated the landscape characteristics of the skyline and the cognitive characteristics of Mt. Mudeung (1,186m) from various viewpoints. Mt. Mudeung, the representative landscape of Gwangju City, has been recognized as a natural landmark and theme of paintings. By analyzing the perspective from 32 points with a digital terrain model, some landscape indices of the skyline were derived and the relationships are discussed. Assessment of the semantic differential scale with 21 adjective variables and representativeness to 15 landscape photographs of the mountain were accomplished. 1. Through regression analysis of the skyline indices, significant relationships were found between them the angle from the visual axis and number of skyline jumps, the vertical angle fluctuation and number of jumps per degree, the visual depth fluctuation and vertical angle fluctuation of skyline, and between the vertical angle mean and number of jumps per degree. Meaningful relations were found between the number of jumps of skyline to number of jumps per degree and the angle from visual axis to visual distance. However, in the representative assessment no difference was found on the angle from visual axis of viewpoints. On the other hand, it seemed to relate representativeness with visual clarity based on visual distance. 2. We found 4 factors "familiarity", "fluctuation of skylines", "openness", and "feeling of texture" in the results of factor analysis of semantic differential assessment. When considering the results of assessment for representativeness, adjective words for familiarity and openness seemed to have a close assessment. Specifically, the research showed that the landscape representation was highly assessed in a view which could be seen from the higher parts to the lower part of hills. This result indicates that the management of viewpoints which could get a scene from intermediate to distant, and locating a high elevation is important. 3. In the picturesque expression of Mt. Mudeung, various impressions from the different points, a skyline based on the top of Mt. Mudeung and a mono structure by overlapping hills were common characteristics. These common characteristics were also partially found through the analysis of topographical landscape indices and landscape images. Therefore, the viewpoints for the representative landscape management should be selected in natural or open spaces.

• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.3 s.43
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• pp.181-195
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• 1992

The purposes of this study were to investigate 1) the effect of body exposure and color of a woman's suit on the perception of modesty, and 2) the effect of perceiver's sex and age on impression formed by the function of clothing variables. The instrument of this study consisted of a response scale and stimuli. Thirteen items of 7-point semantic differential scales were developed to measure the perceiver's impression on wearer's modesty. Stimuli were color pictures of a model wearing one of 8 types of suit constructed by a 2 $\times$ 2 $\times$ 2 factorial design. The manipulation of each level of the clothing variables were: color of the suit by black and red, leg exposure by varying skirt lengths to a Chanel-line and mini skirt, and neck exposure by shirt collar blouse and scarf. Two models, representing typical female college students living in Seoul, were selected to eliminate model effect. The sample include 384 subjects, consisting of 4 groups of male and female college students and middle aged men and women. Eight experimental groups were randomly assigned to one of eight stimuli based on between-subject design. One half of each group responded to model 1 and the other half to model 2 of same stimulus. Responses to the semantic differential scales were factor analyzed (pc model, Varimax rotation) to identify factors constructing impression of modesty. Two factors emerged regard­less of subgroups; Elegance and Extroversion factor. The first factor was found to be dominant, accounting for 60 percent of the total variance. The other accounted for just 11 percent. Multidimensional ANOVA (5-way, 3-way) was conducted to test the effect of the clothing variables against two factors identified from the factor analysis. Leg exposure was the most powerful variable affecting the impression of Elegance and Extroversion factor for all per. ceiver subgroups. Neck exposure had primary effect on the impression of Elegance, whereas it partially influenced that of Extroversion. Color of suit had only partial effect on the impression of Extroversion. Hypothesis I was partially supported from the findings above. The effect of perceiver's age and sex on impression by the function of clothing variables was tested by comparing the result between four subgroups. In forming an impression of the wearer's modesty, male college students were least affected by the manipulation of clothing variables, while middle aged males were affected most. In the female groups, there was no age difference and they fell between the male groups in the degree to which they were affected. Hypothesis II was supported only by age difference in two male groups, and by sex difference in two student groups.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.871-884
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• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• Journal of Software Engineering Society
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• v.23 no.4
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• pp.151-163
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• 2010

In the defense software domain where large-scale software products in various application areas need to be built, reusing software is regarded as one of the important practices to build software products efficiently and economically. There have been many efforts to apply various methods to support software reuse in the defense software domain. However, developers in the defense software domain still experience many difficulties and face obstacles in reusing software assets. In this paper, we analyze practical problems of software reuse in the defense software domain, and define core requirements to solve those problems. To meet these requirements, we are currently developing the Component Grid system, a reuse-support system that provides a developer-centric software reuse environment. We have designed an architecture of Component Grid, and defined essential elements of the architecture. We have also developed the core approaches for developing the Component Grid system: a semantic-tagging-based requirement tracing method, a reuse-knowledge representation model, a social-network-based asset search method, a web-based asset management environment, and a wiki-based collaborative and participative knowledge construction and refinement method. We expect that the Component Grid system will contribute to increase the reusability of software assets in the defense software domain by providing the environment that supports transparent and efficient sharing and reuse of software assets.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.93-114
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• 2023

Information extraction can facilitate the intensive analysis of documents by providing semantic triples which consist of named entities and their relations recognized in the texts. However, most of the research so far has been carried out separately for named entity recognition and relation extraction as individual studies, and as a result, the effective performance evaluation of the entire information extraction systems was not performed properly. This paper introduces two models of end-to-end information extraction that can extract various entity names in clinical records and their relationships in the form of semantic triples, namely pipeline and joint models and compares their performances in depth. The pipeline model consists of an entity recognition sub-system based on bidirectional GRU-CRFs and a relation extraction module using multiple encoding scheme, whereas the joint model was implemented with a single bidirectional GRU-CRFs equipped with multi-head labeling method. In the experiments using i2b2/VA 2010, the performance of the pipeline model was 5.5% (F-measure) higher. In addition, through a comparative experiment with existing state-of-the-art systems using large-scale neural language models and manually constructed features, the objective performance level of the end-to-end models implemented in this paper could be identified properly.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.39-50
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• 2024

Color is an essential visual element that has a significant impact on the formation of a city's image and people's perceptions. Quantitative analysis of color in urban environments is a complex process that has been difficult to implement in the past. However, with recent rapid advances in Machine Learning, it has become possible to analyze city colors using photos shared by tourists. This study selected Dali City, a popular tourist destination in China, as a case study. Photos of Dali City shared by tourists were collected, and a method to measure large-scale city colors was explored by combining machine learning techniques. Specifically, the DeepLabv3+ model was first applied to perform a semantic segmentation of tourist sharing photos based on the ADE20k dataset, thereby separating artificial elements in the photos. Next, the K-means clustering algorithm was used to extract colors from the artificial elements in Dali City, and an adjacency matrix was constructed to analyze the correlations between the dominant colors. The research results indicate that the main color of the artificial elements in Dali City has the highest percentage of orange-grey. Furthermore, gray tones are often used in combination with other colors. The results indicated that local ethnic and Buddhist cultures influence the color characteristics of artificial elements in Dali City. This research provides a new method of color analysis, and the results not only help Dali City to shape an urban color image that meets the expectations of tourists but also provide reference materials for future urban color planning in Dali City.

• Journal of Korean Academy of Nursing
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• v.18 no.3
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• pp.269-280
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• 1988

Although everyone grows old, perception about the aging process and aging as measured physiologically vary widely. Perecptions of aging have psychologically influence on physical aging. This study was to examine the relationships between, self-concept, perception of aging, and physical aging in the elderly and to contribute to the theory development which may direct nursing intervention to promote well-being of the aged. Subjects were 70 women residents of a nursing home for the elderly in Seoul. Data collection was done from May 15 to June 15, 1988 using interview schedules and mechanical instruments. The instruments were selected items from the Health Self Concept Scale developed by Jacox and Stewart for self concept, and Secord and Jourad's Body Cathexis Scale and Osgood's Semantic Differential Scale for perception of aging. Physical aging was measured by mechanical instruments, inspection, questions, and palpation. The data were analysed for mean, 1-test, ANOVA, and Pearson Correlation Coefficient using an S.P.S.S computerized program. The results of the analysis were as follows. 1. The mean level of self concept for the subject group was 16.97(SD＝$\pm$6.17)in a range from 6-30. The mean level of perception of aging was 39.6. (SD＝$\pm$6.51) in a range from 13-65. The mean level of physical aging was 14.09(SD＝$\pm$2.05)in a range from 8-40. 2. Relationships among self - concept, perception of aging, and physical aging. 1) There was a positive relationship between self-concept and perception of aging(r＝0.4461, p＝0.000). 2) There was a negative relationship between physical aging and perception of aging(r＝-0.2975, p＝0.006). 3) There was a tendancy toward a negative relationship between physical aging and self -concept, but not a significant relationship (r＝-0.1033, p＝0.197). 3. 1) No general charcteristic variables were related to self concept. 2) The general characteristic variable related to the level of perception of aging was religion (t＝4. 17, p＝0.001). 3) The general characteristic variable related to the level of physical aging was age (F＝12.008, p＝0.000). There was a significant relationship between self - cencept and perception of aging, and between physical aging and perception of aging. Therefore nursing intervention should focus on promoting a positive perception of aging and strengthening self- concept during the physical aging process.

• Journal of Korean Society of Forest Science
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• v.89 no.2
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• pp.145-160
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• 2000

This study is focused to the national park of Korean typical Sea Hally$\check{o}$ Haesang, and its visual resources and practiced inspect course by the way of suppositions and tests, to show the visual resource management objectively, and that of qualitative basic data. Accordingly by measuring the physical amount spatial structure with the visual amount originated from the Mesh Analyzing Method and the Visual Preference from the Scenic Beauty Estimation(S.B.E.) method and analyzed the valuation of the visual resource by Iverson method. Spatial image structure measured by Semantic Differential(S.D.) Scale was shown through the factor analysis algorithm for the analyzing psychological amount and examined the flowing out of decisive factor and the objective importance related to the mutual factors by appling the measurement of the visual quality. As a national Park, the visual factors that have natural landscape harmonized with forest, sky, surface of the water, curious stones and rocks, and temples should be escalated their values affirmatively so as to be the scenery of pointed direction and enjoyable, and it is of more needed for visual resource and its' controlling technique to make artificial structures more intentional planning and systemical setting. When we are viewing the improvement for the national park along with the visual resource management, reasonable level of development is needed, because when men interference surpass plantations and leasts will be damaged and the quality of natural landscape can be lowered, so it is needed to set up a management end, tangibly or clearly; and it is permitted limit coming and going ablably by accounting the suitable number for availing. But the controling end should be set in every level, positive management, very actively within the permissive varcability. It is the main business for the national park to prevent the damage from human for their gay life or to prevent the damage of a land carpet, and to restorate for the visual resource management.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.557-566
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• 2024

Ultrasound-guided regional anesthesia is one of the most common techniques used in peripheral nerve blockade by enhancing pain control and recovery time. However, accurate Brachial Plexus (BP) nerve detection and identification remains a challenging task due to the difficulty in data acquisition such as speckle and Doppler artifacts even for experienced anesthesiologists. To mitigate the issue, we introduce a BP nerve small target segmentation network by incorporating BP object detection and U-Net based semantic segmentation into a single deep learning framework based on the multi-scale approach. To this end, the current BP detection and identification was estimated: 1) A RetinaNet model was used to roughly locate the BP nerve region using multi-scale based feature representations, and 2) U-Net was then used by feeding plural BP nerve features for each scale. The experimental results demonstrate that our proposed model produces high quality BP segmentation by increasing the accuracies of the BP nerve identification with the assistance of roughly locating the BP nerve area compared to competing methods such as segmentation-only models.