• Journal of The Geomorphological Association of Korea
• /
• v.18 no.2
• /
• pp.65-80
• /
• 2011

This is to find out geomorphological environment of optimum location for the object of dwelling site of Bronze Age (hereinafter called dwelling site) of total 177 unit confirmed in Yongducheon basin and Onyangcheon basin (hereinafter called investigation-object basin) in Asan-si Chungnam. To do this, hill of investigation-object basin where dwelling sites were confirmed was divided into 11 units of hillslope geomorphological and this was combined with the grade of 5 units of gradient and again subdivided into total 55 units of micro-landforms of Hillslope. Based on this, in the viewpoint of 'gradient of each of micro-landforms' analyzed 'number of dwelling sites' and 'dwelling site distribution density (unit: number of dwelling sites/1000m2)'. As the result, the optimum geomorphological environment where dwelling sites were located was largely confirmed be 5 units(① semi-gentle slope land of crest slope ②gentle slope land of crest slope ③ slope of upper sideslope ④ flat land of crest slope ⑤ flat land of crest flat). It is thought that this analysis data will be used in the future as basic data for the study of predicting distribution of dwelling sites in Bronze Age which might have been buried in the investigation-object basin.

• Economic and Environmental Geology
• /
• v.56 no.6
• /
• pp.765-779
• /
• 2023

The Daum Vent Field (DVF) was newly discovered in the Central Indian Ridge during the hydrothermal expedition by the Korea Institute of Ocean Science & Technology (KIOST) in 2021. In this paper, we describe the detailed mineralogy and geochemistry of hydrothermal chimney and mound to understand the nature of hydrothermal mineralization in the DVF. The mineral assemblages (pyrite±sphalerite±chalcopyrite) of dominant sulfides, FeS contents (mostly <20 mole %) of sphalerite, and (Cu+Zn)/Fe values (0.001-0.22) of bulk compositions indicate that the DVF has an strong affinity with basaltic-hosted seafloor massive sulfide (SMS) deposit along the oceanic ridge. Combined with the predominance of colloform and/or dendritic-textured pyrite and relatively Fe-poor sphalerite in chimneys, the fluid-temperature dependency of trace element systematics (Co, Mn, and Tl) between chimney and mound indicates that the formation of mound was controlled by relatively reducing and high-temperature fluids compared to chimney. The δ³⁴S values (+8.31 to +10.52‰) of pyrite reflect that sulfur and metals were mainly leached from the associated basement rocks (50.6-61.3%) with a contribution from reduced seawater sulfur (38.7-49.4%). This suggests that the fluid-rock interaction, with little effect of magmatic volatile influx, is an important metal source for the sulfide mineralization in the DVF.

• Information Systems Review
• /
• v.25 no.4
• /
• pp.27-45
• /
• 2023

The importance of information security has grown alongside the development of information and communication technology. However, companies struggle to select suitable countermeasures within their limited budgets. Sönmez and Kılıç (2021) proposed a model using AHP and mixed integer programming to determine the optimal investment combination for mitigating information security breaches. However, their model had limitations: 1) a lack of objective measurement for countermeasure efficacy against security threats, 2) unrealistic scenarios where risk reduction surpassed pre-investment levels, and 3) cost duplication when using a single countermeasure for multiple threats. This paper enhances the model by objectively quantifying countermeasure efficacy using the beta probability distribution. It also resolves unrealistic scenarios and the issue of duplicating investments for a single countermeasure. An empirical analysis was conducted on domestic SMEs to determine investment budgets and risk levels. The improved model outperformed Sönmez and Kılıç's (2021) optimization model. By employing the proposed effectiveness measurement approach, difficulty to evaluate countermeasures can be quantified. Utilizing the improved optimization model allows for deriving an optimal investment portfolio for each countermeasure within a fixed budget, considering information security costs, quantities, and effectiveness. This aids in securing the information security budget and effectively addressing information security threats.

• Journal of the Korean Chemical Society
• /
• v.67 no.6
• /
• pp.441-461
• /
• 2023

This study explore characteristics of teaching orientation and pck of science teachers in online-offline mixed learning environment. Data consisted of open-ended survey, semi-structured interview, class observation, field notes from 12 science teachers. We categorized teaching orientation considering both science education goals and science teaching·learning orientation. There were 8 different teaching orientations such as 'understanding science concepts-lecture centered' 'constructing science concepts-inquiry based' 'applying science concepts and inquiry-inquiry based' 'applying science concepts and inquiry-lectured centered' 'analyzing and judging science information-inquiry based' 'developing scientific attitude-inquiry based' 'developing scientific attitude-lecture centered' and 'developing perception of interrelationships among science, technology, and society-inquiry based'. Teachers with inquiry based teaching·learning orientation seemed to have knowledge of science curriculum specific to online learning environment for student inquiry. While teachers with 'understanding science concepts-lecture centered' teaching orientation appeared to have questioning strategy of checking student understanding and strategy of repeating a lecture, teachers with 'constructing science concepts-inquiry based' teaching orientation appeared to have knowledge of instructional strategies to perform online group activities targeting student construction of knowledge and to replace face-to-face group activities with virtual experiments and individual experiments. While teachers with 'understanding science concepts-lecture centered' teaching orientation did not show knowledge of student science learning, teachers with 'constructing science concepts-inquiry based' teaching orientation appeared to have knowledge of student difficulties in inquiry based learning.

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

• The Journal of the Acoustical Society of Korea
• /
• v.27 no.5
• /
• pp.229-238
• /
• 2008

Audio tuning improves not only the sound quality of the car audio but also the quality of the completed car itself. However without the subjective assessment on the users' preferences, it is hard to tune the car audio satisfying them. Even though there are lots of factors to be considered to assess the preferences, only a restricted number of factors should be included in the experiment because the total number of experiments increases rapidly as the number of factors in the experiment increases. A large number of factors make it hard to explore the relationship between the sound quality and the sound characteristics and also makes the panels exhausted. In this paper, 8 sound characteristics, each with 2 levels, are considered for the experiment. An orthogonal design of experiment is suggested to reduce the number of experiments from 256 to 16. The analysis of variance is applied to show that Treble is the most significant characteristic of the reproduced sound of the given pop music. Also Deep Bass, SAD, and the interaction between Treble and SAD are found to be significant. For the given classic music, SAD is the only characteristic which turns out to be significant.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.3
• /
• pp.163-171
• /
• 2024

Structural health monitoring for ships and offshore structures is important in various aspects. Ships and offshore structures are continuously exposed to various environmental conditions, such as waves, wind, and currents. In the event of an accident, immense economic losses, environmental pollution, and safety problems can occur, so it is necessary to detect structural damage or defects early. In this study, structural response data of multi-linked floating offshore structures under various wave load conditions was calculated by performing fluid-structure coupled analysis. Furthermore, the order reduction method with distortion base mode was applied to the structures for predicting the structural response by using the results of numerical analysis. The distortion base mode order reduction method can predict the structural response of a desired area with high accuracy, but prediction performance is affected by sensor arrangement. Optimization based on a genetic algorithm was performed to search for optimal sensor arrangement and improve the prediction performance of the distortion base mode-based reduced-order model. Consequently, a sensor arrangement that predicted the structural response with an error of about 84.0% less than the initial sensor arrangement was derived based on the root mean squared error, which is a prediction performance evaluation index. The computational cost was reduced by about 8 times compared to evaluating the prediction performance of reduced-order models for a total of 43,758 sensor arrangement combinations. and the expected performance was overturned to approximately 84.0% based on sensor placement, including the largest square root error.

• Information Systems Review
• /
• v.25 no.1
• /
• pp.129-143
• /
• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.217-225
• /
• 2024

To foster the core talents of the future, the development of diverse and substantial SW·AI education programs is required, and a systematic system that can assist public education in SW and AI must be established. In this study, we develop and combine SW·AI education modules to construct a SW and AI education program applicable to public education. We also establish a systematic education system and provide sustainable SW·AI education to elementary, middle, and high school students through 'Job's Garage Camp' based on various sharing platforms. By creating a sustainable follow-up educational environment, students are encouraged to continue their self-directed learning of SW and AI. As a result of conducting a pre-post survey of students participating in the 'Job's Garage Camp', the post-survey values improved compared to the pre-survey values in all areas of 'interest', 'understanding and confidence', and 'career aspirations'. Based on these results, it can be confirmed that students had a universal positive perception and influence on SW and AI. Therefore, if the operation case of 'Job's Garage Camp' is improved and expanded, it can be presented as a standard model applicable to other SW and AI education programs in the future.

• Information Systems Review
• /
• v.18 no.4
• /
• pp.69-88
• /
• 2016

Nowadays, organizations have adopted Smart Work to efficiently manage tasks, such as electronic document approval, customer management, and site inspection, without spatial-temporal constraints. Smartphones, which are commonly used in Smart Work, enable individuals to perform their jobs anytime and anywhere, thus blurring the boundary between work and non-work. To solve the problem of blurred work/non-work boundaries, a construct of self-control and affective factors needs to be considered because business style is changed from command to autonomy in the Smart Work context. Moreover, employees can convey their emotions easily over smartphones. Recent marketing studies have analyzed consumers' behavior based on the combination of cognitive, affective, and behavioral components, and researchers of information systems are also interested in these factors. However, previous research has some limitations, such as not classifying factors into cognitive, affective, and behavioral as well as not covering all three factors. Therefore, we explore the roles of cognitive, affective, and behavioral components in emotional exhaustion and education performance, and conduct a survey on undergraduate and graduate students, who are the major users of smartphones. Findings show that when individuals improve their cognitive capability (self-control) and usage experience (smartphone communication and internet usage), they can decrease emotional exhaustion and increase education performance. In the role of affective capability, increasing education performance is partially accepted. These results imply that organizations should not focus on controlling the usage of smartphones but on promoting appropriate smartphone usage.