• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.72-74
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• 2022

The National Science and Technology Knowledge Information Service (NTIS) provides information on national R&D projects. Project information consists of meta-information such as 'project name', 'project performance institution', 'research manager name', and text explaining projects such as 'research goal', 'research content', and 'expected effect'. There is a problem that it takes a lot of time to find the desired project information by checking all of the "research goals" or "research contents" in the list of results of searching for 1 million project information. To solve this problem, this paper proposes a project information summary system that summarizes the parts consisting of long texts within the national R&D project information. By analyzing the linguistic characteristics of the Korean language, a preprocessor was built and a project information summary model based on natural language processing technology was developed to process preprocessed text information. Through this, project information composed of long sentences is provided in a compressed and summarized form, which will help users to easily and quickly infer the overall content with the summary information alone.

• Korean Journal of Cognitive Science
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• v.34 no.3
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• pp.197-226
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• 2023

Generative Text Summarization is one of the Natural Language Processing tasks. It generates a short abbreviated summary while preserving the content of the long text. ROUGE is a widely used lexical-overlap based metric for text summarization models in generative summarization benchmarks. Although it shows very high performance, the studies report that 30% of the generated summary and the text are still inconsistent. This paper proposes a methodology for evaluating the performance of the summary model without using the correct summary. AggreFACT is a human-annotated dataset that classifies the types of errors in neural text summarization models. Among all the test candidates, the two cases, generation summary, and when errors occurred throughout the summary showed the highest correlation results. We observed that the proposed evaluation score showed a high correlation with models finetuned with BART and PEGASUS, which is pretrained with a large-scale Transformer structure.

• Journal of Distribution Science
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• v.21 no.10
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• pp.39-49
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• 2023

Purpose: The purpose of this study is to explore areas that have been studied extensively in previous studies related to franchising as a platform for global distribution. Furthermore, franchising is a strategic distribution method that gives entrepreneurs the opportunity to replicate an established business model. In addition, franchisees benefit from the use of established branding and receive support from the franchisor. Research design, data, and methodology: This study used the Preferred Reporting Items Systematics Review and Meta-Analyses (PRISMA) method to analyse data from 2003 to 2023 in the Web of Science and Scopus databases. Results: A total of 79 articles were identified and analysed to see trends and related themes such as product distribution, business distribution, business strategy, emerging market, and franchising relationship. Also, publication trends by year related to franchises are also presented. Conclusions: Overall, the research trend related to franchising as a global distribution is well seen, and every year, many researchers begin to explore the topic of franchising as a method of distribution that can be explored from various aspects either quantitatively or qualitatively. Lastly, limitations and recommendations are made to provide guidance for future studies related to the topic broadly and deeply in enriching the findings.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.

• Clinical and Experimental Vaccine Research
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• v.13 no.2
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• pp.83-90
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• 2024

The emergence of coronavirus disease 2019 (COVID-19) vaccines has been a remarkable advancement. However, the efficacy, immunogenicity, and safety of these vaccines in individuals with liver cirrhosis require careful evaluation due to their compromised immune status and potential interactions with underlying liver disease. The present study aimed to evaluate the safety and efficacy of COVID-19 vaccines in liver cirrhosis patients. In the present study, we searched international databases, including Google Scholar, PubMed, Scopus, Embase, and Web of Science. The search strategy was carried out by using keywords and MeSH (Medical Subject Headings) terms. STATA ver. 15.0 (Stata Corp., USA) was used to analyze the data statistically. The analysis was performed using the randomeffects model. We also used the chi-square test and I² index to calculate heterogeneity among studies. For evaluating publication bias, Begg's funnel plots and Egger's tests were used. A total of 4,831 liver cirrhosis patients with COVID-19 were examined from 11 studies. The rate of hospitalization in the patients with liver cirrhosis was 17.6% (95% confidence interval [CI], 9%-44%). The rate of fever in the patients with liver cirrhosis was 4.5% (95% CI, 0.9%-8.1%). The rate of positive neutralizing antibodies in the patients with liver cirrhosis was 82.5% (95% CI, 69.8%-95.1%). Also, the rates of seroconversion after the second vaccination in patients with liver cirrhosis and the control group were 96.6% (95% CI, 92.0%-99.0%), and 99.7% (95% CI, 99.0%-100.0%), respectively. COVID-19 vaccines have demonstrated promising efficacy, immunogenicity, and safety profiles in individuals with liver cirrhosis, providing crucial protection against COVID-19-related complications.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3917-3925
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• 2012

Background: The relationship between postmenopausal hormone therapy (HT) and invasive breast cancer has been extensively investigated, but that with breast carcinoma in situ (BCIS) has received relatively little attention. The aim of our present study was to review and summarize the evidence provided by longitudinal studies on the association between postmenopausal HT use and BCIS risk. Methods: A comprehensive literature search for articles published up to May 2012 was performed. Prior to performing a meta-analysis, the studies were evaluated for publication bias and heterogeneity. Relative risk (RR) or odds ratio (OR) values were calculated using 14 reports (8 case-control studies and 6 cohort studies), published between 1986 and 2012. Results: There was evidence of an association between ever postmenopausal estrogen use and BCIS based on a random-effects model (RR = 1.25, 95% confidence interval (CI) = 1.01, 1.55). However, we found no strong evidence of an association between ever postmenopausal estrogen combined with progesterone use and BCIS using a randomeffects model (RR = 1.55, 95% CI = 0.95, 2.51). Furthermore, our analysis showed a strong association between "> 5 years duration" of estrogen or estrogen combined with progesterone use and BCIS. Furthermore, current use of any HT is associated with increased risk of BCIS in cohort studies. Additional well-designed large studies are now required to validate this association in different populations.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.12
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• pp.1684-1688
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• 2017

Objective: From a review of published information on genetic association studies, a meta-analysis was conducted to determine the influence of the genes ${\kappa}-casein$ (CSN3) and ${\beta}-lactoglobulin$ (LGB) on milk yield traits in Holstein, Jersey, Brown Swiss, and Fleckvieh. Methods: The GLIMMIX procedure was used to analyze milk production and percentage of protein and fat in milk. Models included the main effects and all their possible two-way interactions; not estimable effects and non-significant (p>0.05) two-way interactions were dropped from the models. The three traits analyzed used Poisson distribution and a log link function and were determined with the Interactive Data Analysis of SAS software. Least square means and multiple mean comparisons were obtained and performed for significant main effects and their interactions (p<0.0255). Results: Interaction of breed by gene showed that Holstein and Fleckvieh were the breeds on which CSN3 ($6.01%{\pm}0.19%$ and $5.98%{\pm}0.22%$), and LGB ($6.02%{\pm}0.19%$ and $5.70%{\pm}0.22%$) have the greatest influence. Interaction of breed by genotype nested in the analyzed gene indicated that Holstein and Jersey showed greater influence of the CSN3 AA genotype, $6.04%{\pm}0.22%$ and $5.59%{\pm}0.31%$ than the other genotypes, while LGB AA genotype had the largest influence on the traits analyzed, $6.05%{\pm}0.20%$ and $5.60%{\pm}0.19%$, respectively. Furthermore, interaction of type of statistical model by genotype nested in the analyzed gene indicated that CSN3 and LGB genes had similar behavior, maintaining a difference of more than 7% across analyzed genotypes. These results could indicate that both Holstein and Jersey have had lower substitution allele effect in selection programs that include CSN3 and LGB genes than Brown Swiss and Fleckvieh. Conclusion: Breed determined which genotypes had the greatest association with analyzed traits. The mixed model based in Bayesian or Ridge Regression was the best alternative to analyze CSN3 and LGB gene effects on milk yield and protein and fat percentages.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.549-564
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• 2017

Not only services that are provided by a single system have been various with the development of the Internet of Things and autonomous software but also new services that are not possible before are provided through collaboration between systems. The collaboration between autonomous systems is similar to the ecosystem configuration in terms of biological viewpoints. Thus, it is called the IT Ecosystem, and this concept has arisen newly in recent years. The IT Ecosystem refers to a concept that achieves a mission of each of a number of heterogeneous systems rather than a single system utilizing their own autonomy as well as achieving the objectives of the overall system simultaneously in order to meet a single common goal. In our previous study, we proposed architecture of elementary level and as well as basic several meta-models to implement the IT Ecosystem. This paper proposes comprehensive reference architecture framework to implement the IT Ecosystem by cleansing the previous study. Among them, a utility function based on cost-benefit model is proposed to solve the dynamic re-configuration problem of system components. Furthermore, a measure of using genetic algorithm is proposed as a solution to reduce the dynamic re-configuration overhead that is increased exponentially according to the expansion of the number of entities of components in the IT Ecosystem. Finally, the utilization of the proposed orchestration framework is verified quantitatively through probable case studies on IT Ecosystem for unmanned forestry management.

• Spatial Information Research
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• v.5 no.2
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• pp.147-160
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• 1997

"The "Map Algebra", beeing recognized as a viable theoretical framework for GIS (Geographica Infonnation System), models map layers as "operands" which are the basic unit of geo-processing, and a variety of GIS commands as "operators." In this paper, we attempt at lifting some limitations of map algebras proposed in GIS literature. First, we model map layer as "function" such that we may employ the notion of meta operator (or, higher-order funtion) available in the functional programming paradigm. This approach provides map algebraic language with "programmability" needed in GIS user language. Second, we extend the semantics of, and improve on the sytactic structure of map algebraic language. Mer the data model and language associated with map algebra are formalized, we proceed to design and implement a prototype of map algebraic processor. The parser of the language in our prototype plays the role of transforming the native and heterogeneous user language of current GISs into a canonical map algebraic language. The prototype, named "MapSee" is a proof-of-concept system for the ideas we propsed in this paper. We believe that the uniform interface based on the map algebraic language will make promising infrastructure to support "Internet GIS." This is because the uniform but powerful interface through the Web clients allow access to both geo-data and geo-processing resources distributed over the network.to both geo-data and geo-processing resources distributed over the network.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.11
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• pp.1123-1129
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• 2015

A pair of two-way valves typically is used in automotive washing machines, where the water flow direction is frequently reversed and highly pressurized clean water is sprayed to remove the oil and dirt remaining on machined engine and transmission blocks. Although this valve system has been widely used because of its competitive price, its application is sometimes restricted by surging effects, such as pressure ripples occurring in rapid changes in water flow caused by inaccurate valve control. As an alternative, one three-way reversing valve can replace the valve system because it provides rapid and accurate changes to the water flow direction without any precise control device. However, a cavitation effect occurs because of the complicated bottom plug shape of the valve. In this study, the cavitation index and percent of cavitation (POC) were introduced to numerically evaluate fluid flows via computational fluid dynamics (CFD) analysis. To reduce the cavitation effect generated by the bottom plug, the optimal shape design was carried out through a parametric study, in which a simple computer-aided engineering (CAE) model was applied to avoid time-consuming CFD analysis and difficulties in achieving convergence. The optimal shape design process using full factorial design of experiments (DOEs) and an artificial neural network meta-model yielded the optimal waist and tail length of the bottom plug with a POC value of less than 30%, which meets the requirement of no cavitation occurrence. The optimal waist length, tail length and POC value were found to 6.42 mm, 6.96 mm and 27%, respectively.