• Title/Summary/Keyword: 잠재적 수요

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Identification of a SNP in Chicken CaSR Gene and Its Effect on Economic Traits (닭의 CaSR 유전자내 단일 염기 변이 탐색 및 경제 형질간의 연관성 분석)

  • Hong, Y.S.;Oh, J.D.;Lee, J.H.;Kong, H.S.;Choi, C.H.;Lee, S.S.;Jeon, G.J.;Lee, H.K.
    • Korean Journal of Poultry Science
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    • v.34 no.2
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    • pp.151-156
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    • 2007
  • The Function of the calcium sensing receptor (CaSR) is to control calcium levels by altering PTH (parathyroid hormone) secretion and renal calcium resorption. The influence of calcium on the basal and stimulated release of several hormones from chicken pituitary glands has been determined in vitro. The objective of this study was to identify SNP in chicken CaSR gene and to investigate the effect of the SNP on economic traits. The sequencing analysis method was used to identify nucleotide polymorphisms within chicken CaSR gene. This study identified SNP at position 1949 bp(Genebank accession No : XM_416491) in the exon 1. The SNP changed the amino acid to alanine(GCC) from serine(TCC). This SNP showed three genotypes, AA, AS and SS by digestion with the restriction enzyme NcoⅠ using the PCR-RFLP method. The A963S showed significant effect only on the first lay day (P<0.05) in Leghorn population. Leghorn with the genotype AA had significantly faster the first lay day(137.6) than the genotype AS(143.0, P<0.05). Also, the A963S showed significant effect only on the first lay day(P<0.05) and mean of egg weight(P<0.05) in KNC population. KNC with the genotypes AA ans AS had significantly faster the first lay day (151.0 and 152.6, respectively) than the genotype SS(159.4, P<0.05). And the genotypes SS had significantly heavier the mean of egg weight(50.4 kg, P<0.05) than the genotype AA ans AS (47.5 and 47.8 kg, respectively). According to result of this study, an a allele of the A963S was found to have a significant effect on the first lay day. It will be possible to use this SNP marker on selecting chicken to improve the first lay day.

Analysis of CO2 Emission Intensity per Industry using the Input-Output Tables 2003 (산업연관표(2003년)를 활용한 산업별 CO2 배출 원단위 분석)

  • Park, Pil-Ju;Kim, Mann-Young;Yi, Il-Seuk
    • Environmental and Resource Economics Review
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    • v.18 no.2
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    • pp.279-309
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    • 2009
  • Greenhouse gas emissions should be precisely forecast to reduce the emissions from industrial production processes. This study calculated the direct and indirect $CO_2$ emission intensities of 401 industries using the Input-Output tables 2003 and statistical data on the amount of energy use. This study had some limitations in drawing study findings because overseas data were used given the lack of domestic data. Other limiting factors included the oil distribution problems in the oil refinery sector, re-review of carbon neutral, and insufficient consideration of waste treatment. Nonetheless, this study is very meaningful since the direct and indirect $CO_2$ emission intensities of 401 industries were calculated. Specifically, this study considered from the zero-waste perspective the effects of waste, which attract interest worldwide since coke gas and gas from the steel industry are obtained as byproducts for the first time in Korea. According to the results of the analysis of $CO_2$ emission intensity per industry, typical industries whose indirect $CO_2$ emission intensity is high include crude steel making, Remicon, steel wire rods & track rail, cast iron, and iron reinforcing rods & bar steel. These industries produce products using the raw materials produced in the industrial sector whose $CO_2$ emission intensity is high. The representative industries whose direct $CO_2$ emission intensity is high include cement, pig iron, lime & plaster products, andcoal-based compounds. These industries extract raw ore from nature and refine them into raw materials that are useful in other industries. The findings in this study can be effectively used for the following case: estimation of target $CO_2$ emission reduction level reflecting each industrial sector's characteristics, calculation of potential emission reduction of each policy to reduce $CO_2$ emissions, identification of a firm's $CO_2$ emission level, and setting of the target level of emission reduction. Moreover, the findings in this study can be utilized widely in fields such as System of integrated Environmental and Economic Accounting(SEEA) and Material Flow Analysis(MFA) as the current topic of research in Korea.

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Label Embedding for Improving Classification Accuracy UsingAutoEncoderwithSkip-Connections (다중 레이블 분류의 정확도 향상을 위한 스킵 연결 오토인코더 기반 레이블 임베딩 방법론)

  • Kim, Museong;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.27 no.3
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    • pp.175-197
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    • 2021
  • Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

The Effects of Desire for Independence, Possibility of Start-up Success and Employment Stability to Pull-entrepreneurship in the Middle Age: Focusing on Mediating Effects of Push-entrepreneurship (중장년층의 자립욕구, 창업성공가능성, 고용안정성이 Pull-창업의지에 미치는 영향 : Push-창업의지의 매개효과 중심으로)

  • Jung, Jong-Sik;Yang, Dong-Woo
    • Korean small business review
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    • v.42 no.3
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    • pp.221-243
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    • 2020
  • The purpose of this study is to research the effects of desire for independence, possibility of start-up success and employment stability to the push-entrepreneurship through the push-entrepreneurship by a survey of employees in the middle age. The purpose of the creating start-up environment was encouraged the qualitative expansion of opportunity-driven start-up rather than the quantitative expansion of necessity-driven start-up for the economic growth and expanding employment. In spit of the employees had their own careers, skills, opportunities and market-experiences, In reality it is necessity-driven start-up that the quantitative expansion. The purpose of this study was to reflect process change by desire for independence, push-entrepreneurship and pull-entrepreneurship based on the possibility of start-up success and employment stability perceived by individual founders, changed process factor rather than the fixed result factor. The results are as follows. First, it was found that desire for independence makes a positive (+) effect on the pull-entrepreneurship and the push-entrepreneurship. Second, it was found that the possibility of start-up success makes a positive (+) effect on the pull-entrepreneurship and not effect on push-entrepreneurship. third, it was found that the employment stability makes a positive (+) effect on the pull-entrepreneurship and not effect on the push-entrepreneurship. Forth, it was found that desire for independence makes a positive (+) effect on the pull-entrepreneurship and mediating negative (-) effect on the push-entrepreneurship. The implication of this study was that potential entrepreneurs with desire for independence, rather than necessity-driven start-up due to unemployment, could be developed as pull-entrepreneurship improved the situation of push-entrepreneurship. In addition, it seems that entrepreneurship education and activation of entrepreneurship programs for employee with desire for independence expand opportunity-driven start-up after retirement.

Assessment of water supply reliability in the Geum River Basin using univariate climate response functions: a case study for changing instreamflow managements (단변량 기후반응함수를 이용한 금강수계 이수안전도 평가: 하천유지유량 관리 변화를 고려한 사례연구)

  • Kim, Daeha;Choi, Si Jung;Jang, Su Hyung;Kang, Dae Hu
    • Journal of Korea Water Resources Association
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    • v.56 no.12
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    • pp.993-1003
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    • 2023
  • Due to the increasing greenhouse gas emissions, the global mean temperature has risen by 1.1℃ compared to pre-industrial levels, and significant changes are expected in functioning of water supply systems. In this study, we assessed impacts of climate change and instreamflow management on water supply reliability in the Geum River basin, Korea. We proposed univariate climate response functions, where mean precipitation and potential evaporation were coupled as an explanatory variable, to assess impacts of climate stress on multiple water supply reliabilities. To this end, natural streamflows were generated in the 19 sub-basins with the conceptual GR6J model. Then, the simulated streamflows were input into the Water Evaluation And Planning (WEAP) model. The dynamic optimization by WEAP allowed us to assess water supply reliability against the 2020 water demand projections. Results showed that when minimizing the water shortage of the entire river basin under the 1991-2020 climate, water supply reliability was lowest in the Bocheongcheon among the sub-basins. In a scenario where the priority of instreamflow maintenance is adjusted to be the same as municipal and industrial water use, water supply reliability in the Bocheongcheon, Chogang, and Nonsancheon sub-basins significantly decreased. The stress tests with 325 sets of climate perturbations showed that water supply reliability in the three sub-basins considerably decreased under all the climate stresses, while the sub-basins connected to large infrastructures did not change significantly. When using the 2021-2050 climate projections with the stress test results, water supply reliability in the Geum River basin was expected to generally improve, but if the priority of instreamflow maintenance is increased, water shortage is expected to worsen in geographically isolated sub-basins. Here, we suggest that the climate response function can be established by a single explanatory variable to assess climate change impacts of many sub-basin's performance simultaneously.

An Intelligence Support System Research on KTX Rolling Stock Failure Using Case-based Reasoning and Text Mining (사례기반추론과 텍스트마이닝 기법을 활용한 KTX 차량고장 지능형 조치지원시스템 연구)

  • Lee, Hyung Il;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.26 no.1
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    • pp.47-73
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    • 2020
  • KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

Simultaneous Determination of 8 Preservatives (6 Parabens, 2-Phenoxyethanol, and Chlorphenesin) in Cosmetics by $UPLC^{TM}$ ($UPLC^{TM}$를 이용한 화장품 중 보존제 8종(파라벤 6종, 페녹시에탄올, 클로페네신)의 동시분석)

  • Park, Jeong-Eun;Lee, So-Mi;Jeong, Hye-Jin;Chang, Ih-Seop
    • Journal of the Society of Cosmetic Scientists of Korea
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    • v.33 no.4
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    • pp.263-267
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    • 2007
  • Parabens are used in nearly all types of cosmetics and toiletries because they are formulated well and have broad spectrum of activity, interness, low costs and excellent chemical stability in relation to pH. 2-phenoxyethanol and chlorphenesin are common preservatives which are usually used in combination with parabens in cosmetics. Toxicity of parabens is generally low but application of parabens to damaged or broken skin has resulted in sensitization. Moreover, the possibility of their estrogenic potential, anesthetic effects and reproductive toxicity has been reported. Consequently there are some regulations in use of parabens. And the maximum permitted concentrations of chlorphenesin and 2-phenoxyethanol in cosmetic products are authorized by the same reasons. So it is important to control and estimate the amount of parabens in products. In this article, we proposed a valid method for the simultaneous determination of 8 preservatives including parabens in a short time using ultra performance liquid $chromatography^{TM}\;(UPLC^{TM})$. Separation of eight components was achieved in less than 10 min and resolutions were reasonable (USP resolution ${\geqq}\;2$). And limit of detection and quantification were evaluated. The method was suitably validated for specificity, linearity, precision (repeatability, intermediate precision) and accuracy for assay (recovery) based on International conference on harmonisation (ICH) guideline. The method was applicable to analysis of preservatives in cosmetic products.

Trophic State Characteristics in Topjeong Reservoir and Their Relations among Major Quality Parameters (탑정저수지의 부영양화 특성 및 주요 변수 간의 상호관계)

  • Park, Yu-Mi;Lee, Eui-Haeng;Lee, Sang-Jae;An, Kwang-Guk
    • Korean Journal of Ecology and Environment
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    • v.42 no.3
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    • pp.382-393
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    • 2009
  • The objectives of this study were to characterize long-term annual and seasonal trophic state of Topjeong Reservoir using conventional variables of Trophic State Index (TSI) and to determine the empirical relations between the trophic parameters. For the analysis, we used water quality dataset of 1995$\sim$2007, which is obtained from the Ministry of Environment, Korea and the number of parameters was 9. Annual ambient mean values of TN and TP were 1.78 mg $L^{-1}$ and 0.03 mg $L^{-1}$, respectively and TN : TP ratios averaged 76, indicating that this system was nitrogen-rich hypertrophic, and was probably phosphorus-limitation for algal growth. Therefore, nitrogen varied little with seasons and years, and total phosphorus (TP) varied depending on season and year. Monsoon dilutions of TP occurred in August and monthly fluctuations of suspended solid (SS) was similar to those of chlorophyll-$\alpha$ (CHL). Annual mean values of BOD and $COD_{Mn}$ were 1.61 mg $L^{-1}$ and 4.23 mg $L^{-1}$, respectively and the interannual values were directly influenced by the intensity of annual rainfall. There were no significant differences in the trophic variables between the two sampling sites. Mean values of Trophic State Index (TSI, Carlson, 1977), based on TN, TP, CHL, and SD (Secchi depth), turned out as eutrophic state, except for the TN (hypertrophic). Regression analyses of log-transformed seasonal CHL against TP and TN showed that variation of the CHL was explained 37% by the variation of TP ($R^2$=0.37, p<0.001, r=0.616), but not by TN ($R^2$=0.03, p>0.05). Regression coefficient of $Log_{10}$CHL vs $Log_{10}SD$ was 0.330 (p<0.003, r=0.580), indicating that transparency is regulated by the organic matter in the system. Results, data suggest that one of the ways controlling the eutrophication would be a reduction of phosphorus from the watershed.

Evaluating of the Effectiveness of RTK Surveying Performance Based on Low-cost Multi-Channel GNSS Positioning Modules (다채널 저가 GNSS 측위 모듈기반 RTK 측량의 효용성 평가)

  • Kim, Chi-Hun;Oh, Seong-Jong;Lee, Yong-Chang
    • Journal of Cadastre & Land InformatiX
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    • v.52 no.2
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    • pp.53-65
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    • 2022
  • According to the advancement of the GNSS satellite positioning system, the module of hardware and operation software reflecting accuracy and economical efficiency is implemented in the user sector including the multi-channel GNSS receiver, the multi-frequency external antenna and the mobile app (App) base public positioning analysis software etc., and the multichannel GNSS RTK positioning of the active configuration method (DIY, Do it yourself) is possible according to the purpose of user. Especially, as the infrastructure of multi-GNSS satellite is expanded and the potential of expansion of utilization according to various modules is highlighted, interest in the utilization of multi-channel low-cost GNSS receiver module is gradually increasing. The purpose of this study is to review the multi-channel low-cost GNSS receivers that are appearing in the mass market in various forms and to analyze the utilization plan of the "address information facility investigation project" of the Ministry of Public Administration and Security by constructing the multi-channel low-cost GNSS positioning module based RTK survey system (hereinafter referred to as "multi-channel GNSS RTK module positioning system"). For this purpose, we constructed a low-cost "multi-channel GNSS RTK module positioning system" by combining related modules such as U-blox's F9P chipset, antenna, Ntrip transmission of GNSS observation data and RTK positioning analysis app through smartphone. Kinematic positioning was performed for circular trajectories, and static positioning was performed for address information facilities. The results of comparative analysis with the Static positioning performance of the geodetic receivers were obtained with 5 fixed points in the experimental site, and the good static surveying performance was obtained with the standard deviation of average ±1.2cm. In addition, the results of the test point for the outline of the circular structure in the orthogonal image composed of the drone image analysis and the Kinematic positioning trajectory of the low cost RTK GNSS receiver showed that the trajectory was very close to the standard deviation of average ±2.5cm. Especially, as a result of applying it to address information facilities, it was possible to verify the utility of spatial information construction at low cost compared to expensive commercial geodetic receivers, so it is expected that various utilization of "multi-channel GNSS RTK module positioning system"

Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments (Smart Store in Smart City: 소비자 감성기반 상권분석 시스템 개발)

  • Yoo, In-Jin;Seo, Bong-Goon;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.24 no.1
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    • pp.25-52
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    • 2018
  • This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.