• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.417-430
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• 2024

As modern technology advances and the proliferation of the internet continues, cyber threats are also on the rise. To effectively counter these threats, the importance of utilizing Cyber Threat Intelligence (CTI) is becoming increasingly prominent. CTI provides information on new threats based on data from past cyber incidents, but the complexity of data and changing attack patterns present significant analytical challenges. To address these issues, this study aims to utilize graph data that can comprehensively represent multidimensional relationships. Specifically, the study constructs a heterogeneous graph based on malware data, and uses the metapath2vec node embedding technique to more effectively identify cyber attack groups. By analyzing the impact of incorporating topology information into traditional malware data, this research suggests new practical applications in the field of cyber security and contributes to overcoming the limitations of CTI analysis.

• Tribology and Lubricants
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• v.40 no.3
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• pp.97-102
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• 2024

The next-generation Molten Salt Reactor is known for its high safety because it uses nuclear fuel dissolved in high-temperature molten salt, unlike traditional solid atomic fuel methods. However, the high-temperature molten salt causes severe corrosion in internal structural materials, threatening the reactor's safety. Therefore, it is crucial to investigate the high-temperature corrosion resistance and wear performance of materials used in reactors to ensure safety. In this study, the high-temperature corrosion resistances and wear performances of corrosion samples in a NaCl-MgCl2-KCl (20-40-40 [wt%]) molten salt are investigated to evaluate the applicability of economically viable stainless steels, 316SS and 304SS. Hastelloy C276 and a new alloy containing a small amount of Nb are used as reference samples for comparative analysis. The mass loss, mass loss rate per unit volume, and surface roughness of each sample are measured to understand the corrosion mechanisms. Scanning electron microscopy and energy-dispersive spectroscopy analyses are employed to analyze the corrosion mechanisms. Wear tests on the corroded samples are also conducted to assess the extent of corrosion. Based on the experimental results, we predict the lifespans of the materials and evaluate their suitability as candidate materials for molten salt reactors. The data obtained from the experiments provide a valuable database for structural materials that can enhance the stability of molten salt reactors and recommend high-temperature corrosion-resistant materials suitable for next-generation reactors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.141-156
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• 2023

This study aims to improve understanding of freight movement, crucial for logistics facility investment and policy making. It addresses the limitations of traditional freight truck traffic data, aggregated only at city and county levels, by developing a new methodology. This method uses trip chain data for more detailed, facility-level analysis of freight truck movements. It employs DTG (Digital Tachograph) data to identify individual truck visit locations and creates H3 system-based polygons to represent these visits spatially. The study also involves an algorithm to dynamically determine the optimal spatial resolution of these polygons. Tested nationally, the approach resulted in polygons with 81.26% spatial fit and 14.8% error rate, offering insights into freight characteristics and enabling clustering based on traffic chain characteristics of freight trucks and visited facility types.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.53-66
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• 2024

With the continuous advancement of computer technology, deep learning models have emerged as innovative tools in shaping various aspects of architectural design. Recognizing the distinctive perspective of children, which differs significantly from that of adults, this paper contends that conventional standards may not always be the most suitable approach in designing urban structures tailored for children. The primary objective of this study is to leverage neural style networks within the design process, specifically adopting the artistic viewpoint found in children's illustrations. By combining the aesthetic paradigm of urban architecture with inspiration drawn from children's aesthetic preferences, the aim is to unearth more creative and subversive aesthetics that challenge traditional norms. The selected context for exploration is the landmark buildings in Qingdao City, Shandong Province, China. Employing the neural style network, the study uses architectural elements of the chosen buildings as content images while preserving their inherent characteristics. The process involves artistic stylization inspired by classic children's illustrations and images from children's picture books. Acting as a conduit for deep learning technology, the research delves into the prospect of seamlessly integrating architectural design styles with the imaginative world of children's illustrations. The outcomes aim to provide fresh perspectives and effective support for the artistic design of contemporary urban buildings.

• International Journal of Heart Failure
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• v.6 no.1
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.1
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• pp.107-117
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• 2016

In this study, six historical parks in Seoul is analyzed. Based on the analysis, the representative values of the historical parks in Seoul draw three criteria such as historicity, educational attributes, and sociality, and also the type of historic and cultural resources, spatial functions and arrangements, circulation and accessibility, and the type and usage of introduced facilities. Consequently the current status and improvement plan is suggested as follows. First, cultural assets oriented historical parks such as Sayuksin Bongeun and Seonnongdan historical Park focus on the management based on the preservation of historic and cultural resources. Non designated cultural heritages oriented historic parks such as Shingye Yanghwajin Itaewon-bugundang historic park should focus on the usages the symbolize and commemorate historic and cultural resources. Second, Careful attitudes on the historicity of the park are needed in the mixed type of historic and cultural resources that determine the identity such as Yanghwajin Itaewon-bugundang historic park. Third, the rate of facilities in Bongeun and Shingye historical park is increased due to the renovation of religion facilities, rather than the neighborhood parks. The autonomy of regulations that does not have the limits of the area of park facilities weaken the publicity of the historical parks. Fourth, Shingye historical park suggest changing its name into Danggogae martyrs' shrine historical park. because its historic and cultural resources are included as the historic park is named. Fifth, the current problems such as numerous uncontrolled entrances, mixed uses in circulation, and accessible failure due to the closure are recognized. Therefore, the entrances and circulations should be articulated clearly in order to increase opportunities of experience for visitors in the historic and cultural resources, and also neighborhood facility should be suitably divided. Sixth, the park facilities in the neighborhood parks are introduced equally in Seoul historical parks. The uses and arrangements that considered the circumstance of the historical parks should be determined in the cultural facilities such as outdoor music hall and sporting facilities. Seventh, historic facilities that named historic hall, culture hall, memorial hall, and promotion hall in the historic parks are utilized for convenience and religion facilities. Institutional framework should be examined to keep publicity in the historic parks by spatial privatization of the specific group.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• Korean Journal of Heritage: History & Science
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• v.46 no.3
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• pp.230-250
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• 2013

possessed by Dong-A University Museum is designated as Treasure No. 595, and has been known for a more exquisite, delicate and realistic expression and a colorful three-dimensional structure compared to the 'grass and insect painting' work and its value in art history. However, it has not been analyzed and studied in fabric craft despite it being an embroidered work. This study used scientific devices to examine and analyze the Screen's fabric, thread colors, and embroidery techniques to clarify its patterns and fabric craft characteristics for its value in the history of fabric craft. As a result, consists of eight sides and its subject matters and composition are similar to those of the general paintings of grass and insects. The patterns on each side of the 'grass and insect painting' include cucumber, cockscomb, day lily, balsam pear, gillyflower, watermelon, eggplant, and chrysanthemums from the first side. Among these flowers, the balsam pear is a special material not found in the existing paintings of grass and insect. The eighth side only has the chrysanthemums with no insects and reptiles, making it different from the typical forms of the paintings of grass and insect. The fabric of the Screen uses black that is not seen in other decorative embroideries to emphasize and maximize various colors of threads. The fabric used the weave structure of 5-end satin called Gong Dan [non-patterned satin]. The threads used extremely slightly twisted threads that are incidentally twisted. Some threads use one color, while other threads use two or mixed colors in combination for three-dimensional expressions. Because the threads are severely deterioration and faded, it is impossible to know the original colors, but the most frequently used colors are yellow to green and other colors remaining relatively prominently are blue, grown, and violet. The colors of day lily, gillyflower, and strawberries are currently remaining as reddish yellow, but it is anticipated that they were originally orange and red considering the existing paintings of grass and insects. The embroidery technique was mostly surface satin stitch to fill the surfaces. This shows the traditional women's wisdom to reduce the waste of color threads. Satin stitch is a relatively simple embroidery technique for decorating a surface, but it uses various color threads and divides the surfaces for combined vertical, horizontal, and diagonal stitches or for the combination of long and short stitches for various textures and the sense of volume. The bodies of insects use the combination of buttonhole stitch, outline stitch, and satin stitch for three-dimensional expressions, but the use of buttonhole stitch is particularly noticeable. In addition to that, decorative stitches were used to give volume to the leaves and surface pine needle stitches were done on the scouring rush to add more realistic texture. Decorative stitches were added on top of gillyflower, strawberries, and cucumbers for a more delicate touch. is valuable in the history of paintings and art and bears great importance in the history of Korean embroidery as it uses outstanding technique and colors of Korea to express the Shin Sa-im-dang's 'Grass and Insect Painting'.

• Cartoon and Animation Studies
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• s.37
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• pp.509-537
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• 2014

This paper proposes 'postfilimc metamorphosis' and 'reanimation' as two concepts that aim at giving account to the aesthtetic tendencies and genealogies of what Suzanne Buchan calls 'pervasive animation', a category that refers to the unprecedented expansion of animation's formal, technological and experiential boundaries. Buchan's term calls for an interdisciplinary approach to animation by highlighting a range of phenomena that signal the growing embracement of the images and media that transcend the traditional definition of animation, including the lens-based live-action image as the longstanding counterpart of the animation image, and the increasing uses of computer-generated imagery, and the ubiquity of various animated images dispersed across other media and platforms outside the movie theatre. While Buchan's view suggests the impacts of digital technology as a determining factor for opening this interdisciplinary, hybrid fields of 'pervasive animation', I elaborate upon the two concepts in order to argue that the various forms of metamorphorsis and motion found in these fields have their historical roots. That is, 'postfilmic metamorphosis' means that the transformative image in postfimic media such as video and the computer differs from that in traditional celluloid-based animation materially and technically, which demands a refashioned investigation into the history of the 'image-processing' video art which was categorized as experimental animation but largely marginalized. Likewise, 'reanimation' cne be defined as animating the still images (the photographic and the painterly images) or suspending the originally inscribed movement in the moving image and endowing it with a neewly created movement, and both technical procedues, developed in experimental filmmaking and now enabled by a variety of moving image installations in contemporary art, aim at reconsidering the borders between stillness and movement, and between film and photography. By discussing a group of contemporary moving image artworks (including those by Takeshi Murata, David Claerbout, and Ken Jacobs) that present the aesthetic features of 'postfilmic metamorphosis' and 'reanimation' in relation to their precursors, this paper argues that the aesthetic implications of the works that pertain to 'pervasive animation' lie in their challenging the tradition dichotomies of the graphic/the live-action images and stillness/movement. The two concepts, then, respond to a revisionist approach to reconfigure the history and ontology of other media images outside the traditional boundaries of animation as a way of offering a refasioned understanding of 'pervasive animation'.