• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.277-285
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• 1997

In this paper, the definition to flexibility is examined through the literature and re-classified to set up an evaluation model. Flexibility is classified into three categories to find the flexibility types for evaluation. The flexibility type called as manufacturing flexibility is defined and a model is developed to make the performance evaluation possible, The manufacturing flexibility has a heavy relationship to the machine flexibility; and 5 flexibility types out of 8 have relationship to the machine flexibility. This indicates that it is possible to have a pretty good evaluation measure if the machine flexibility related types could be evaluated using a model. There are four different inter-arrival times in the model. A big time saving is observed if the processing time is set equal to 72 second. This indicates that a flexibility affects the system a lot if the inter-arrival time is close to the processing time. The model used in this paper includes multi-processes in a production line with machine failure. However, development of realistic models with buffer between processes and some of the flexibility types not included in this model are remained for further research.

• Journal of information and communication convergence engineering
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• v.15 no.4
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• pp.217-226
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• 2017

Korea has the tenth largest film industry in the world; however, detailed analyses using the factors contributing to successful film commercialization have not been approached. Using big data, this paper analyzed both internal and external factors (including genre, release date, rating, and number of screenings) that contributed to the commercial success of Korea's top 10 ranking films in 2011-2015. The authors developed a WebCrawler to collect text data about each movie, implemented a Hadoop system for data storage, and classified the data using Map Reduce method. The results showed that the characteristic of "release date," followed closely by "rating" and "genre" were the most influential factors of success in the Korean film industry. The analysis in this study is considered groundwork for the development of software that can predict box-office performance.

• The Journal of Bigdata
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• v.3 no.1
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• pp.63-71
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• 2018

In the last five years (2013~2017), we analyzed what factors influenced Korean films that have surpassed 10 million viewers in the Korean movie industry, where the total number of moviegoers is over 200 million. In general, many people consider the number of screens and ratings as important factors that affect the audience's success. In this study, four additional factors, including the number of screens and ratings, were established to establish a hypothesis and correlate it with the presence of 10 million spectators through big data analysis. The results were significant, with 91 percent accuracy in predicting 10 million viewers and 99.4 percent accuracy in estimating cumulative attendance.

• Journal of Integrative Natural Science
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• v.10 no.4
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• pp.202-208
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• 2017

Xanthine Oxidase is an enzyme, which oxidizes hypoxanthine to xanthine, and xanthine to uric acid. It is widely distributed throughout various organs including the liver, gut, lungs, kidney, heart, brain and plasma. It is involved in gout pathogenesis. Hence, in the present study, Hologram based Quantitative Structure Activity Relationship Study was performed on a series of Xanthine Oxidase antagonist named 2-(indol-5-yl) thiazole derivatives. The best HQSAR model was obtained using Atoms, Bonds, Connection, Hydrogen, Chirality and Donor Acceptor as fragment distinction parameter using hologram length 71 and 4 components with fragment size of minimum 2 and maximum 5. Significant cross-validated correlation coefficient ($q^2$= 0.563) and non cross-validated correlation coefficients ($r^2$= 0.967) were obtained. The model was then used to evaluate the six external test compounds and its $r^2{_{pred}}$ was found to be 0.798. Contribution map show that presence of propyl ring in indole thiazole makes big contributions for improving the biological activities of the compounds. We hope that our HQSAR model and analysis will be helpful for future design of xanthine oxidase antagonists.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.190-198
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• 2023

Sentiment analysis can be applied to all texts generated from websites, blogs, messengers, etc. The study fulfills an artificial intelligence sentiment analysis estimating visiting evaluation opinions (reviews) and visitor ratings, and suggests a deep learning model which foretells either an affirmative or a negative inclination for new reviews. This study operates review big data about Jeju tourist attractions which are extracted from Google from October 1st, 2021 to November 30th, 2021. The normalization data used in the propensity prediction modeling of this study were divided into training data and test data at a 7.5:2.5 ratio, and the CNN classification neural network was used for learning. The predictive model of the research indicates an accuracy of approximately 84.72%, which shows that it can upgrade performance in the future as evaluating its error rate and learning precision.

• Composites Research
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• v.31 no.5
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• pp.251-259
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• 2018

In this paper, statistical volume element modeling method was developed for multi-scale progressive failure analysis of fiber reinforced composite materials. Big-size statistical volume elements (BSVEs) was considered to minimize the size effect in the micro-scale, by including as many fibers as possible. For that purpose, a mesh cutting method is suggested and adapted into the fiber model generator that creates finite element domain rapidly. The fiber defect model was also developed based on the experimental distribution of the fiber strength. The size effects from the local load sharing (LLS) are evaluated by increasing the fiber inclusion in the micro-scale model. Finally, continuum damage mechanics (CDM) model to the fiber direction was extracted from numerical analysis on BSVEs. And it was compared with strength prediction from typical representative volume element (RVE) model.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Journal of Korean Library and Information Science Society
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• v.44 no.3
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• pp.5-27
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• 2013

The purpose of this study is to examine the characteristics and continuity of 'One Book, One City' community reading campaign in the U.S.A. An analysis of 584 'One Book' projects registered in the Library of Congress, the Center for the Books shows that this innovative model of reading campaign has been diffused through three stages: the beginning(1998-2001), the growth (2002-2006), and the second take-off(2007-present). The stability of more than fifty 'One Book' projects was confirmed and the continuing diffusion of 'One Book' model through 'The Big Read' Initiative and some projects' attempt to try new directions seem worth observing.

• The Journal of Economics, Marketing and Management
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• v.9 no.1
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• pp.1-14
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• 2021

Purpose: The article deals with the proposition that consumers' fashion consumption behavior will still follow the consumption behavior of fast fashion, despite recognizing the importance of slow fashion. Research design, data and methodology: The research model to verify this proposition is topic modelling with big data including unstructured textual data. we combined 5,506 news articles posted on Naver news search platform during the 2003-2019 period about fast fashion and slow fashion, high-frequency words have been derived, and topics have been found using LDA model. Based on these, we examined consumers' perception and consumption behavior on slow fashion through the analysis of Topic Network. Results: (1) Looking at the status of annual article collection, consumers' interest in slow fashion mainly began in 2005 and showed a steady increase up to 2019. (2) Term Frequency analysis showed that the keywords for slow fashion are the lowest, with consumers' consumption patterns continuing around 'brand.' (3) Each topic's weight in articles showed that 'social value' - which includes slow fashion - ranked sixth among the 9 topics, low linkage with other topics. (4) Lastly, 'brand' and 'fashion trend' were key topics, and the topic 'social value' accounted for a low proportion. Conclusion: Slow fashion was not a considerable factor of consumption behavior. Consumption patterns in fashion sector are still dominated by general consumption patterns centered on brands and fast fashion.

• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.1-7
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• 2024

In this paper, we compare three models (logistic regression, Random Forest, and XGBoost) for predicting stroke occurrence using data from the Korea National Health and Nutrition Examination Survey (KNHANES). We evaluated these models using various metrics, focusing mainly on recall and F1 score to assess their performance. Initially, the logistic regression model showed a satisfactory recall score among the three models; however, it was excluded from further consideration because it did not meet the F1 score threshold, which was set at a minimum of 0.5. The F1 score is crucial as it considers both precision and recall, providing a balanced measure of a model's accuracy. Among the models that met the criteria, XGBoost showed the highest recall rate and showed excellent performance in stroke prediction. In particular, XGBoost shows strong performance not only in recall, but also in F1 score and AUC, so it should be considered the optimal algorithm for predicting stroke occurrence. This study determines that the performance of XGBoost is optimal in the field of stroke prediction.