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CFD Simulation of Changesin NOX Distribution according to an Urban Renewal Project (CFD 모델을 이용한 도시 재정비 사업에 의한 NOX 분포 변화 모의)

  • Kim, Ji-Hyun;Kim, Yeon-Uk;Do, Heon-Seok;Kwak, Kyung-Hwan
    • Journal of Environmental Impact Assessment
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    • v.30 no.3
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    • pp.141-154
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    • 2021

  • In this study, the effect of the restoration of Yaksa stream and the construction of an apartment complex by the urban renewal project in the Yaksa district of Chuncheon on air quality in the surrounding area was evaluated using computational fluid dynamics (CFD) model simulations. In orderto compare the impact of the project, wind and pollutant concentration fields were simulated using topographic data in 2011 and 2017, which stand for the periods before and after the urban renewal project, respectively. In the numerical experiments, the scenarios were set to analyze the effect of the construction of the apartment complex and the effect of stream restoration. Wind direction and wind speed data obtained from the Chuncheon Automated Synoptic Observing System (ASOS) were used as the inflow boundary conditions, and the simulation results were weighted according to the frequencies of the eight-directional inflow wind directions. The changes in wind speed and NOX concentration distribution according to the changes in building and terrain between scenarios were compared. As a result, the concentration of NOX emitted from the surrounding roads increased by the construction of the apartment complex, and the magnitude of the increase was reduced as the result of including the effect of stream restoration. The concentration of NOX decreased around the restored stream, while the concentration increased significantly around the constructed apartment complex. The increase in the concentration of NOX around the apartment complex was more pronounced in the place located in the rear of the wind direction to the apartment complex, and the effect remains up to the height of the building. In conclusion, it was confirmed that the relative arrangement of apartment complex construction and stream restoration in relation to the main wind direction of the target area was one of the major factors in determining the surrounding air quality.

  • https://doi.org/10.14249/eia.2021.30.3.141 인용 PDF KSCI

A study on the classification of research topics based on COVID-19 academic research using Topic modeling (토픽모델링을 활용한 COVID-19 학술 연구 기반 연구 주제 분류에 관한 연구)

  • Yoo, So-yeon;Lim, Gyoo-gun
    • Journal of Intelligence and Information Systems
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    • v.28 no.1
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    • pp.155-174
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    • 2022

  • From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (

    ) were the topic modeling results for each research topic (
    ) was found to be derived from For example, as a result of topic modeling for papers related to 'vaccine', a new topic titled Topic 05 'neutralizing antibodies' was extracted. A neutralizing antibody is an antibody that protects cells from infection when a virus enters the body, and is said to play an important role in the production of therapeutic agents and vaccine development. In addition, as a result of extracting topics from papers related to 'treatment', a new topic called Topic 05 'cytokine' was discovered. A cytokine storm is when the immune cells of our body do not defend against attacks, but attack normal cells. Hidden topics that could not be found for the entire thesis were classified according to keywords, and topic modeling was performed to find detailed topics. In this study, we proposed a method of extracting topics from a large amount of literature using the LDA algorithm and extracting similar words using the Skip-gram method that predicts the similar words as the central word among the Word2vec models. The combination of the LDA model and the Word2vec model tried to show better performance by identifying the relationship between the document and the LDA subject and the relationship between the Word2vec document. In addition, as a clustering method through PCA dimension reduction, a method for intuitively classifying documents by using the t-SNE technique to classify documents with similar themes and forming groups into a structured organization of documents was presented. In a situation where the efforts of many researchers to overcome COVID-19 cannot keep up with the rapid publication of academic papers related to COVID-19, it will reduce the precious time and effort of healthcare professionals and policy makers, and rapidly gain new insights. We hope to help you get It is also expected to be used as basic data for researchers to explore new research directions.

  • https://doi.org/10.13088/jiis.2022.28.1.155 인용 PDF KSCI

    • Analysis of media trends related to spent nuclear fuel treatment technology using text mining techniques (텍스트마이닝 기법을 활용한 사용후핵연료 건식처리기술 관련 언론 동향 분석)

    • Jeong, Ji-Song;Kim, Ho-Dong
      • Journal of Intelligence and Information Systems
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      • v.27 no.2
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      • pp.33-54
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      • 2021

    • With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

    • https://doi.org/10.13088/jiis.2021.27.2.033 인용 PDF KSCI

    The Effects of Perceived Quality Factors on the Customer Loyalty: Focused on the Analysis of Difference between PB and NB (지각된 품질요인이 고객충성도에 미치는 영향: PB와 NB간의 차이분석)

    • Ye, Jong-Suk;Jun, So-Yon
      • Journal of Distribution Research
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      • v.15 no.2
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      • pp.1-34
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      • 2010

    • Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as

    , and moderating effects is shown as
    . Results This study is designed with 16 research hypotheses, Results from analyzing their main effects show that 9 of 11 hypotheses were supported and other 2 hypotheses were rejected. On the other hand, results from analyzing their moderating effects show that 3 of 5 hypotheses were supported and other 2 hypotheses were rejected. H1-1: (SPC: Standardized Path Coefficient)=-0.04, t-value=-1.04, p>. 05). H1-2: (${\Delta}\chi^2$=1.10, df=1, p> 0.05). H1-1 and H1-2 are rejected, so it is prove that perceived price is not a significant decision variable having influence on perceived quality and there is no significant variable between PB and NB in terms of influence of perceived price on perceived quality. H2-1: (SPC=0.31, t-value=3.74, p<. 001). H2-2: (${\Delta}\chi^2$=3.93, df=1, p< 0.05). H2-1 and H2-2 are supported, so it is proved that company reputation is a significant decision variable having influence on perceived quality and, in terms of influence of company reputation on perceived quality, PB has relatively stronger influence than NB. H3-1: (SPC=0.26, t-value=5.30, p< .001). H3-2: (${\Delta}\chi^2$=16.81, df=1, p< 0.01). H3-1 and H3-2 are supported, so it is proved that brand reputation is a significant decision variable having influence on perceived quality and, in terms of influence of brand reputation on perceived quality, NB has relatively stronger influence than PB. H4-1: (SPC=0.31, t-value=2.65, p< .05). H4-2: (${\Delta}\chi^2$=1.26, df=1, p> 0.05). H4-1 is supported, but H4-2 is rejected, Therefore, it is proved that product experience is a significant decision variable having influence on perceived quality and, on the other hand, there is no significant different between PB and NB in terms of influence of product experience on product quality. H5-1: (SPC=0.24, t-value=3.00, p<. 05). H5-2: (${\Delta}\chi^2$=5.10, df=1, p< 0.05). H5-1 and H5-2 are supported, so it is proved that brand familiarity is a significant decision variable having influence on perceived quality and, in terms of influence of brand familiarity on perceived quality, NB has relatively stronger influence than PB. H6: (SPC=0.91, t-value=19.06, p< .001). H6 is supported, so a fact that customer satisfaction increases as perceived quality increases is proved. H7: (SPC=0.81, t-value=7.44, p<. 001). H7 is supported, so a fact that customer trust increases as perceived quality increases is proved. H8: (SPC=0.57, t-value=7.87, p< .001). H8 is supported, so a fact that customer loyalty increases as perceived quality increases is proved. H9: (SPC=0.08, t-value=0.76, p> .05). H9 is rejected, so it is proved influence of customer satisfaction on customer trust is not significant. H10: (SPC=0.21, t-value=4.34, p< .001). H10 is supported, so a fact that customer loyalty increases as customer satisfaction increases is proved. H11: (SPC=0.40, t-value=5.68, p< .001). H11 is supported, so a fact that customer loyalty increases as customer trust increases is proved. Implications Although most of existing studies have used function, price, brand, design, service, brand name, store name as antecedent variables for perceived quality, this study used different antecedent variables in order to analyze and distinguish purchase group PB and NB through preliminary research. Therefore, this study may be used as preliminary data for a empirical study that is designed to be helpful for practical jobs. Also, this study is made to be easily applied to any practical job because SEM(Structural Equation Modeling), most strongly explaining the relation between observed variable and latent variable, is used for this study. This study suggests a new strategic point that, in order to increase customer loyalty, customer's perceived quality level should satisfied for inducing customer satisfaction, customer trust, and customer loyalty. Therefore, after finding an effective differentiating factors in perceived quality in order to increase customer loyalty through increasing perceived quality, this factor was made to be applied to PB and NB. Because perceived quality factors which is recognized as being important by consumers is different between PB and NB, this study suggests how to efficiently establish marketing strategy by enhancing a factor. Companies have mostly focused on profitability in terms of analyzing customer loyalty, but this study included positive WOM(word of mouth). Hence, this study suggests that it would be helpful for establishing customer loyalty when consumers have cognitive satisfaction and emotional satisfaction together. Limitations This study used variables perceived price, company reputation, brand reputation, product experience, brand familiarity in order to determine whether each constituent factor has different influence on perceived quality between purchase group PB and NB. These characteristic variables are made up on the basis of the preliminary research, but it is expected that more precise research result would be obtained if additional various variables are included in study. This study selected a practical product that is non-durable, low-priced and bestselling product in a discount store through the preliminary research because it can be easily estimated by consumers. Therefore. generalization of study would be more easily obtained if more various product characteristics is included. Regarding a sample used in this study, it was only based on consumers who purchase products in a large-scale discount store located in Seoul and in the capital area. Accordingly, this sample has some geographical limitation, If a study is expanded by including more areas, more representative research results may be produced. Because this study is only designed to analyze consumers who purchase a product in a large-scale discount store, some difference may be found according to characteristics of each business type. In other words, there is certainly some application limitation, so research result from this study may not be applied to other business types. Future research may have fruitful results if it adjusts a variable to each business type.

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