• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.991-998
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• 2022

The purpose of this study is to develop GPU (Graphics Processing Unit) acceleration technique for 2-dimensional model and to assess the effectiveness for high resolution flood simulation in wide area In this study, GPU acceleration technique was implemented in the G2D (Grid based 2-Dimensional land surface flood model) model, using implicit scheme and uniform square grid, by using CUDA. The technique was applied to flood simulation in Jinju-si. The spatial resolution of the simulation domain is 10 m × 10 m, and the number of cells to calculate is 5,090,611. Flood period by typhoon Mitag, December 2019, was simulated. Rainfall radar data was applied to source term and measured discharge of Namgang-Dam (Ilryu-moon) and measured stream flow of Jinju-si (Oksan-gyo) were applied to boundary conditions. From this study, 2-dimensional flood model could be implemented to reproduce the measured water level in Nam-gang (Riv.). The results of GPU acceleration technique showed more faster flood simulation than the serial and parallel simulation using CPU (Central Processing Unit). This study can contribute to the study of developing GPU acceleration technique for 2-dimensional flood model using implicit scheme and simulating land surface flood in wide area.

• Design Convergence Study
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• v.16 no.6
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• pp.169-184
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• 2017

There is a lot of needs that are not expressed as much as the expressed needs in familiar products and services that are used in daily life such as a smartphone. Finding the 'Inconveniences in familiar use' make it possible to create opportunities for value expanding in the existing products and service area. There are a lot of related works, which have studied the definition of hidden needs and the methods to find it. But, they are making it difficult to address the hidden needs in the cases of familiar use due to focus on the new product or service developing typically. In this study, we try to redefine the hidden needs in the daily familiarity and approach it in the new way to find out. Because of the users' unability to express what they want and the complexity of needs which can not be explained clearly, we can not approach it as the quantitative issue. For this reason, the basic data type selected as the user behavior data excluding all description is the screen-shot of the smartphone. We try to apply the integrated rules and patterns to the individual data using the qualitative coding techniques to overcome the limitations of qualitative analysis based on unstructured data. From this process, We can not only extract meaningful clues which can make to understand the hidden needs but also identify the possibility as a way to discover hidden needs through the review of relevance to actual market trends. The process of finding hidden needs is not easy to systemize in itself, but we expect the possibility to be conducted a reference frame for finding hidden needs of other further studies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.3
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• pp.155-163
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• 2023

Real-time monitoring technology is critical for ensuring the safety and reliability of nuclear power plant structures. However, the current seismic monitoring system has limited system identification capabilities such as modal parameter estimation. To obtain global behavior data and dynamic characteristics, multiple sensors must be optimally placed. Although several studies on optimal sensor placement have been conducted, they have primarily focused on civil and mechanical structures. Nuclear power plant structures require robust signals, even at low signal-to-noise ratios, and the robustness of each mode must be assessed separately. This is because the mode contributions of nuclear power plant containment buildings are concentrated in low-order modes. Therefore, this study proposes an optimal sensor placement methodology that can evaluate robustness against noise and the effects of each mode. Indicators, such as auto modal assurance criterion (MAC), cross MAC, and mode shape distribution by node were analyzed, and the suitability of the methodology was verified through numerical analysis.

• Biomedical Science Letters
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• v.2 no.1
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• pp.91-108
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• 1996

The present study was designed to examine effect of short term treadmill and weight-training on aging arophy in the rat skeletal muscle. Male rats of 24 months old were used. Each groups included control, treadmill and weight-training for 4 weeks by using treadmill apparatus and body press apparatus. The histo and cytochemical, ultrastructural and stereological changes in senile skeletal muscles of the rat were observed in the present study. During the training period the body weight and muscular weight in all groups remained constant. The volume density of muscle fiber type IIC and IIB were increased, that of type IIA was decreased, but type I remained constant in treadmill-training group. In weight-training rat, the density of type IIA and IIB were increased, both those of type IIC was decreased. But, all changes of muscle fiber type is not significant. Senile control group some usual formation of mild contraction band, liposuscin pigment and muscular splitting were observed. After treadmill-training, histological and ultrastructural changes occurred in the muscle fiber, such as irregularity of the sarcolemma, interfibrillar vacuolization, longitudinal splitting, and widened I-bond. After weight-training, the changes occurred in the trained muscle fiber, such as appearances of many lysosomes and autophagic vacuoles, severe contraction band, and breakup of myofibrils. Histo and cytochemical studies showed that the activities of succinic dehydrogenase and acid phosphatase remained constant, activities of $Mg^{++}$-ATPase decrease with training. Stereological changes were not observed in the volume and numerical density of all subject component, but the surface density of mitochondrial inner membrane was increased with treadmill-training. These experimental results suggested that endurance training during short-term may result in the adaptible response in senile skeletal muscles. On the other side, weight-training is bad for senile skeletal muscle.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.67-76
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• 2024

In this paper, we present a DIP-MLS testing method that combines digital image processing with a rigid body-based MLS differencing approach to measure mechanical variables and analyze the impact of target location and image resolution. This method assesses the displacement of the target attached to the sample through digital image processing and allocates this displacement to the node displacement of the MLS differencing method, which solely employs nodes to calculate mechanical variables such as stress and strain of the studied object. We propose an effective method to measure the displacement of the target's center of gravity using digital image processing. The calculation of mechanical variables through the MLS differencing method, incorporating image-based target displacement, facilitates easy computation of mechanical variables at arbitrary positions without constraints from meshes or grids. This is achieved by acquiring the accurate displacement history of the test specimen and utilizing the displacement of tracking points with low rigidity. The developed testing method was validated by comparing the measurement results of the sensor with those of the DIP-MLS testing method in a three-point bending test of a rubber beam. Additionally, numerical analysis results simulated only by the MLS differencing method were compared, confirming that the developed method accurately reproduces the actual test and shows good agreement with numerical analysis results before significant deformation. Furthermore, we analyzed the effects of boundary points by applying 46 tracking points, including corner points, to the DIP-MLS testing method. This was compared with using only the internal points of the target, determining the optimal image resolution for this testing method. Through this, we demonstrated that the developed method efficiently addresses the limitations of direct experiments or existing mesh-based simulations. It also suggests that digitalization of the experimental-simulation process is achievable to a considerable extent.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.345-361
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• 2005

A transform-space index indexes objects represented as points in the transform space An advantage of a transform-space index is that optimization of join algorithms using these indexes becomes relatively simple. However, the disadvantage is that these algorithms cannot be applied to original-space indexes such as the R-tree. As a way of overcoming this disadvantages, the authors earlier proposed the transform-space view join algorithm that joins two original- space indexes in the transform space through the notion of the transform-space view. A transform-space view is a virtual transform-space index that allows us to perform join in the transform space using original-space indexes. In a transform-space view join algorithm, the order of accessing disk pages -for which various space filling curves could be used -makes a significant impact on the performance of joins. In this paper, we Propose a new space filling curve called the adaptive row major order (ARM order). The ARM order adaptively controls the order of accessing pages and significantly reduces the one-pass buffer size (the minimum buffer size required for guaranteeing one disk access per page) and the number of disk accesses for a given buffer size. Through analysis and experiments, we verify the excellence of the ARM order when used with the transform-space view join. The transform-space view join with the ARM order always outperforms existing ones in terms of both measures used: the one-pass buffer size and the number of disk accesses for a given buffer size. Compared to other conventional space filling curves used with the transform-space view join, it reduces the one-pass buffer size by up to 21.3 times and the number of disk accesses by up to $74.6\%$. In addition, compared to existing spatial join algorithms that use R-trees in the original space, it reduces the one-pass buffer size by up to 15.7 times and the number of disk accesses by up to $65.3\%$.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.89-115
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• 2019

This study was conducted to present the stylistic differences between Arthur Conan Doyle and Agatha Christie, famous as writers of classical mystery novels, through data analysis, and further to present the analytical methodology of the study of style based on text mining. The reason why we chose mystery novels for our research is because the unique devices that exist in classical mystery novels have strong stylistic characteristics, and furthermore, by choosing Arthur Conan Doyle and Agatha Christie, who are also famous to the general reader, as subjects of analysis, so that people who are unfamiliar with the research can be familiar with them. The primary objective of this study is to identify how the differences exist within the text and to interpret the effects of these differences on the reader. Accordingly, in addition to events and characters, which are key elements of mystery novels, the writer's grammatical style of writing was defined in style and attempted to analyze it. Two series and four books were selected by each writer, and the text was divided into sentences to secure data. After measuring and granting the emotional score according to each sentence, the emotions of the page progress were visualized as a graph, and the trend of the event progress in the novel was identified under eight themes by applying Topic modeling according to the page. By organizing co-occurrence matrices and performing network analysis, we were able to visually see changes in relationships between people as events progressed. In addition, the entire sentence was divided into a grammatical system based on a total of six types of writing style to identify differences between writers and between works. This enabled us to identify not only the general grammatical writing style of the author, but also the inherent stylistic characteristics in their unconsciousness, and to interpret the effects of these characteristics on the reader. This series of research processes can help to understand the context of the entire text based on a defined understanding of the style, and furthermore, by integrating previously individually conducted stylistic studies. This prior understanding can also contribute to discovering and clarifying the existence of text in unstructured data, including online text. This could help enable more accurate recognition of emotions and delivery of commands on an interactive artificial intelligence platform that currently converts voice into natural language. In the face of increasing attempts to analyze online texts, including New Media, in many ways and discover social phenomena and managerial values, it is expected to contribute to more meaningful online text analysis and semantic interpretation through the links to these studies. However, the fact that the analysis data used in this study are two or four books by author can be considered as a limitation in that the data analysis was not attempted in sufficient quantities. The application of the writing characteristics applied to the Korean text even though it was an English text also could be limitation. The more diverse stylistic characteristics were limited to six, and the less likely interpretation was also considered as a limitation. In addition, it is also regrettable that the research was conducted by analyzing classical mystery novels rather than text that is commonly used today, and that various classical mystery novel writers were not compared. Subsequent research will attempt to increase the diversity of interpretations by taking into account a wider variety of grammatical systems and stylistic structures and will also be applied to the current frequently used online text analysis to assess the potential for interpretation. It is expected that this will enable the interpretation and definition of the specific structure of the style and that various usability can be considered.

• Korean Journal of Environmental Biology
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• v.40 no.4
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• pp.413-422
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• 2022

Barnea manilensis is a bivalve which bores soft rocks, such as, limestone or mudstone in the low intertidal zone. They make burrows which have narrow entrances and wide interiors and live in these burrows for a lifetime. In this study, the morphology and the microstructure of the valve of rock-boring clam B. manilensis were observed using a stereoscopic microscope and FE-SEM, respectively. The chemical composition of specific part of the valve was assessed by energy dispersive X-ray spectroscopy (EDS) analysis. 3D modeling and structural dynamic analysis were used to simulate the boring behavior of B. manilensis. Microscopy results showed that the valve was asymmetric with plow-like spikes which were located on the anterior surface of the valve and were distributed in a specific direction. The anterior parts of the valve were thicker than the posterior parts. EDS results indicated that the valve mainly consisted of calcium carbonate, while metal elements, such as, Al, Si, Mn, Fe, and Mg were detected on the outer surface of the anterior spikes. It was assumed that the metal elements increased the strength of the valve, thus helping the B. manilensis to bore sediment. The simulation showed that spikes located on the anterior part of the valve received a load at all angles. It was suggested that the anterior part of the shell received the load while drilling rocks. The boring mechanism using the amorphous valve of B. manilensis is expected to be used as basic data to devise an efficient drilling mechanism.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.