• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.6
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• pp.138-147
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• 2018

With the recent series of damage caused by earthquakes in Korea, such as Gyeongju and Pohang, we know that Korea is no longer a safe zone for earthquakes and that we need to be prepared for them. In addition, bridges built prior to the introduction of seismic design concepts remain without adequate seismic reinforcement measures, and earthquake reinforcement should be performed efficiently considering economic and structural safety. Preliminary assessment of seismic performance of existing bridges is divided into four seismic groups, taking into account seismicity, vulnerability and Impact, considering the magnitude of the existing bridge's seismic, and prioritization for further evaluation of seismic performance. In this study, unlike the existing anti-seismic reinforcement priority method, scores are calculated based on the seismic design criteria applied to bridges, importance coefficient of the bridge including the zone coefficient and the Importance, vulnerability index of the bridge including the soil condition and the elapsed years, detail coefficient of the bridge including the superstructure form, the span length, the width, the height, the design load, and the daily traffic volume. The calculated score items will be weighted and grouped according to the results. Using this, a simpler and more efficient algorithm was proposed to determine the priority of seismic reinforcement on a bridge.

• Journal of Information Technology and Architecture
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• v.9 no.3
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• pp.323-331
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• 2012

With the advancement of the area of knowledge and information, Intellectual Property, especially, patents have captured attention more and more emergent. The increasing need for efficient way of patent information search has been essential, but the prevailing patent search engines have included too many noises for the results due to the Boolean models. This has occasioned too much time for the professional experts to investigate the results manually. In this paper, we reveal the differences between the conventional document search and patent search and analyze the limitations of existing patent search. Furthermore, we propose a specialized in patent search, so that the relationship between the keywords within each document and their significance within each patent document search keyword can be identified. Which in turn, the keywords and the relationships have been appointed a ranking for this patent in the upper ranks and the noise in the data sub-ranked. Therefore this approach is proposed to significantly reduce noise ratio of the data from the search results. Finally, in, we demonstrate the superiority of the proposed methodology by comparing the Kipris dataset.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.246-251
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• 2016

Recently, the influence of SNS and online media is rapidly growing with a consequent increase in the interest of marketing using these tools. Blog marketing can increase the ripple effect and information delivery in marketing at low cost by prioritizing keyword search results of influential portal sites. However, because of the tough competition to gain top ranking of search results of specific keywords, long-term and proactive efforts are needed. Therefore, we propose a new method that recommends associated keyword groups with the possibility of higher exposure of the blog. The proposed method first collects the documents of blog including search results of target keyword, and extracts and filters keyword with higher association considering the frequency and location information of the word. Next, each associated keyword is compared to target keyword, and then associated keyword group with the possibility of higher exposure is recommended considering the information such as their association, search amount of associated keyword per month, the number of blogs including in search result, and average writhing date of blogs. The experiment result shows that the proposed method recommends keyword group with higher association.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.11
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• pp.925-933
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• 2014

Today, computational fluid dynamics (CFD) is widely used in industry because of the availability of high-performance computers. However, full-scale analysis poses problems owing to the limited resources and time. In this study, the performance and optimal size of a heat exchanger were calculated using the effectiveness-number of transfer units (${\varepsilon}-NTU$) method and a database of characteristics heat exchanger. Information about the geometry and performance of various heat exchangers is collected, and the performance of the heat exchanger is calculated under the given operating conditions. To determine the optimal size of the heat exchanger, a Genetic Algorithm (GA) is used, and MATLAB and REFPROP are used for the calculation.

• Journal of Information Science Theory and Practice
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• v.3 no.2
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• pp.42-57
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• 2015

Purpose. : The present study is an attempt to find out the impact of self-citations on Impact Factor (IF) across disciplines. The study examines the number of research articles published across 27 major subject fields covered by SCImago, encompassing as many as 310 sub-disciplines. The study evaluates aspects like percentage of self-citations across each discipline, leading self-citing countries and continents, and the impact of self-citation on their IF. Scope. : The study is global in nature, as it evaluates the trend of self-citation and its impact on IF of all the major subject disciplines of the world, along with countries and continents. IF has been calculated for the year 2012 by analyzing the articles published during the years 2010 and 2011. Methodology/Approach. : The study is empirical in nature; as such, statistical and mathematical tools and techniques have been employed to work out the distribution across disciplines. The evaluation has been purely under-taken on the secondary data, retrieved from SCImago Journal and Country Ranking. Findings. : Self-citations play a very significant part in inflating IF. All the subject fields under study are influenced by the practice of self-citation, ranging from 33.14% to 52.38%. Compared to the social sciences and the humanities, subject fields falling under the purview of pure and applied sciences have a higher number of self-citations, but a far lesser percentage than the social sciences and humanities. Upon excluding self-citations, a substantial amount of change was observed in the IF of subject fields under study, as 18 (66.66%) out of 27 subjects fields faced shuffle in their rankings. Variation in rankings based on IF with and without self-citation was observed at subject level, country level, and continental level.

• Journal of Information Technology Services
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• v.18 no.3
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• pp.53-74
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• 2019

In this paper, a theoretical measurement model for comparing cultural power of nations as a form of national power is built and applied to the G20 nations. The measurement model is drawn from the literature of the state theories regarding culture, theories of epistemology, and debates about the sources of national power. Taking the multiple indicator approach, indicators reflecting diverse aspects of cultural power are developed are developed. Examined with empirical data, this model is proved as appropriate as a tool for measuring and comparing the cultural power of nations. Diverse aspects of the cultural power of the G20 nations are ranked and interesting points regarding the state and potential of Korean culture and her cultural governance in these respects are elaborated. The overall ranking of the cultural power of Korea is found as the $11^{th}$ while the U.S., U.K. and France respectively as the $1^{st}$, the $2^{nd}$, and the $3^{rd}$. The U.S. is ranked as the 1st in all three aspects of the cultural power of nation: cultural attractiveness, efforts to enhance the national cultural power, and cultural base of a nation. Korea is ranked as $14^{th}$, $11^{th}$, and $6^{th}$ respectively in these three respects of the national cultural power. Based upon typological analysis of the cultural power of Korea, it is found that Korea belongs to Type III. Trying to move eventually toward Type I state via Type II is suggested as a strategy movement for future cultural governance by Korean government and people to enhance her national cultural power.

• Journal of Information Science Theory and Practice
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• v.7 no.2
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• pp.40-53
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• 2019

Although fake news has been present in human history at any time, nowadays, with social media, deceptive information has a stronger effect on society than before. This article answers two research questions, namely (1) Is the dissemination of fake news supported by machines through the automatic construction of filter bubbles, and (2) Are echo chambers of fake news manmade, and if yes, what are the information behavior patterns of those individuals reacting to fake news? We discuss the role of filter bubbles by analyzing social media's ranking and results' presentation algorithms. To understand the roles of individuals in the process of making and cultivating echo chambers, we empirically study the effects of fake news on the information behavior of the audience, while working with a case study, applying quantitative and qualitative content analysis of online comments and replies (on a blog and on Reddit). Indeed, we found hints on filter bubbles; however, they are fed by the users' information behavior and only amplify users' behavioral patterns. Reading fake news and eventually drafting a comment or a reply may be the result of users' selective exposure to information leading to a confirmation bias; i.e. users prefer news (including fake news) fitting their pre-existing opinions. However, it is not possible to explain all information behavior patterns following fake news with the theory of selective exposure, but with a variety of further individual cognitive structures, such as non-argumentative or off-topic behavior, denial, moral outrage, meta-comments, insults, satire, and creation of a new rumor.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.99-105
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• 2021

In addition to the OTT video production service represented by Nexflix and YouTube, a personalized recommendation system for content with artificial intelligence has become common. YouTube's personalized recommendation service system consists of two neural networks, one neural network consisting of a recommendation candidate generation model and the other consisting of a ranking network. Netflix's video recommendation system consists of two data classification systems, divided into content-based filtering and collaborative filtering. As the online platform-led content production is activated by the Corona Pandemic, the field of virtual influencers using artificial intelligence is emerging. Virtual influencers are produced with GAN (Generative Adversarial Networks) artificial intelligence, and are unsupervised learning algorithms in which two opposing systems compete with each other. This study also researched the possibility of developing AI platform based on individual recommendation and virtual influencer (metabus) as a core content of OTT in the future.

• Journal of Environmental Science International
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• v.29 no.12
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• pp.1249-1260
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• 2020

Non-point source pollutants have characteristics the render them difficult to manage owing to the uncertainty of flow paths. As agricultural non-point sources account for more than 57% of non-point source pollutants, the necessity for management is increasing. This study examines the possibility of utilizing land cover maps to suggest a more appropriate method of setting management priority for agricultural non-point sources in the Daecheong Lake area and draws implications by comparing the results derived using the cadastral map, as mentioned in the TMDL Basic Policy. To define the prioritized areas for management, the pollution load was calculated for each subbasin using the formula from the TMDL technical guidelines. As a result, the difference in the average pollution load between the land cover map and cadastral map ranged from 11.6% to 21% among the subbasins. In almost all subbasins, there were differences in the ranking of management priorities depending on the land information that was used. In addition, it was found that it was reasonable to use the level 3 land cover map to calculate the load generated by the land system for examining the implementation goals and methods of each data and comparing them with satellite images.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.1-11
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• 2022

With the development of deep learning technology, various research and development are underway to estimate preference rankings through learning, and it is used in various fields such as web search, gene classification, recommendation system, and image search. Approximation algorithms are used to estimate deep learning-based preference ranking, which builds more than k comparison sets on all comparison targets to ensure proper accuracy, and how to build comparison sets affects learning. In this paper, we propose a k-disjoint comparison set generation algorithm and a k-chain comparison set generation algorithm, a novel algorithm for generating paired comparison sets for crowd-sourcing-based deep learning affinity measurements. In particular, the experiment confirmed that the k-chaining algorithm, like the conventional circular generation algorithm, also has a random nature that can support stable preference evaluation while ensuring connectivity between data.