• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.603-611
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• 2022

In urban areas, the impermeable area continues to increase due to urbanization, which interferes with the surface penetrating and infiltrating of rainwater, causing most rainwater runoff to the surface, deepening the distortion of water circulation. Distortion of water circulation affects not only flood disasters caused by rainfall and runoff, but also various aspects such as dry stream phenomenon, deterioration of water quality, and destruction of ecosystem balance, and the Ministry of Environment strongly recommends the use of Low Impact development (LID) techniques. In order to apply the LID technique, it is necessary to set a rainwater management target to handle the increase in outflow after the development of the target site, and the current standard sets the rainwater management target using the 10-year daily rainfall. In this study, the difference from the current standards was analyzed through statistical analysis and classification of independent rainfall ideas using inter-event time definition (IETD) in setting the target amount of rainwater management to improve water circulation. Using 30-year rainfall data from 1991 to 2020, methods such as autocorrelation coefficient (AC) analysis, variation coefficient (VC) analysis, and annual average number of rainfall event (NRE) analysis were applied, and IETD was selected according to the target rainfall period. The more samples the population had, the more IETD tended to increase. In addition, by analyzing the duration and time distribution of independent rainfall according to the IETD, a plan was proposed to calculate the standard design rainfall according to the rainwater management target amount. Therefore, it is expected that it will be possible to set an improved rainwater management target amount if sufficient samples of independent rainfall ideas are used through the selection of IETD as in this study.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.4
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• pp.63-76
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• 2022

This study was conducted to compare the perceptions of the park use status, importance, and satisfaction of users in the first implemented and completed Uijeongbu Jikdong Neighborhood Park among the private park special projects carried out as a countermeasure for long-term non-execution of urban parks. To this end, in the initiated project, apartment residents and non-residents were classified according to the promotion plan, and a questionnaire research on importance and satisfaction was conducted to analyze the park use status and IPA(importance-performance analysis). First, as a result of the analysis of the current situation in terms of locational characteristics that occur during the promotion of special projects for private parks, unlike the mountainous areas, the targeted site was close to flat land, indicating that users' satisfaction with the landscape was high. Second, the access of the apartment residents in the initiated project site was easy. Thus, the use rate of residents was relatively higher than that of the non-residents. Third, differences in perception by item were identified through the analysis of IPA and the establishment of strategies. In quadrant I, among the facilities and services, installing restrooms was the priority for residents, and parking facilities and rest facilities were the priority than installing restrooms for non-residents. In quadrant II, overall scores for residents and non-residents were similar, but the distance to the park was in quadrant III due to the low level of satisfaction among non-residents. In this study, the difference in perception between residents and non-residents may cause problems in access and facilities in managing the park in the future. Therefore, it would be necessary to find a way to improve it by establishing a management strategy that takes into account the difference in the perception of residents after construction. In addition, through the results of this study, it was judged that the purpose of park development, the selection of types of parks, and the selection of plans and management indicators for each kind would be significant in the promotion of initiated projects in the planning of park development.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.

• Korean Journal of Ecology and Environment
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• v.45 no.2
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• pp.158-173
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• 2012

The objectives of this study were to analyze the longitudinal gradient and temporal variations of water quality in Daecheong Reservoir in relation to the major inflowing streams from the watershed, during 2001~2010. For the study, we selected 7 main-stream sites of the reservoir along the main axis of the reservoir, from the headwater to the dam and 8 tributary streams. In-reservoir nutrients of TN and TP showed longitudinal declines from the headwater to the dam, which results in a distinct zonation of the riverine ($R_z$, M1~M3), transition ($T_z$, M4~M6), and lacustrine zone ($L_z$, M7) in water quality, as shown in other foreign reservoirs. Chlorophyll-a (CHL) and BOD as an indicator of organic matter, were maximum in the $T_z$. Concentration of total phosphorus (TP) was the highest (8.52 $mg\;L^{-1}$) on March in the $R_z$, and was the highest (165 ${\mu}g\;L^{-1}$) in the $L_z$ on July. Values of TN was the maximum (377 ${\mu}g\;L^{-1}$) on August in the $R_z$, and was the highest (3.76 $mg\;L^{-1}$) in the $L_z$ on August. Ionic dilution was evident during September~October, after the monsoon rain. The mean ratios of TN : TP, as an indicator of limiting factor, were 88, which indicates that nitrogen is a surplus for phytoplankton growth in this system. Nutrient analysis of inflowing streams showed that major nutrient sources were headwater streams of T1~T2 and Ockcheon-Stream of T5, and the most influential inflowing stream to the reservoir was T5, which is located in the mid-reservoir, and is directly influenced by the waste-water treatment plants. The key parameters, influenced by the monsoon rain, were TP and suspended solids (SS). Empirical models of trophic variables indicated that variations of CHL in the $R_z$ ($R^2$=0.044, p=0.264) and $T_z$ ($R^2$=0.126, p=0.054) were not accounted by TN, but were significant (p=0.032) in the $L_z$. The variation of the log-transformed $I_r$-CHL was not accounted ($R^2$=0.258, p=0.110) by $I_w$-TN of inflowing streams, but was determined ($R^2$=0.567, p=0.005) by $I_w$-TP of inflowing streams. In other words, TP inputs from the inflowing streams were the major determinants on the in-reservoir phytoplankton growth. Regression analysis of TN : TP suggested that the ratio was determined by P, rather than N. Overall, our data suggest that TP and suspended solids, during the summer flood period, should be reduced from the eutrophication control and P-input from Ockcheon-Stream should be controlled for water quality improvement.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.21-37
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• 2013

Recently, as the advent of the knowledge-based society, there are more people getting interested in the intellectual property. Especially, the ICT companies leading the high-tech industry are working hard to strive for systematic management of intellectual property. As we know, the patent information represents the intellectual capital of the company. Also now the quantitative analysis on the continuously accumulated patent information becomes possible. The analysis at various levels becomes also possible by utilizing the patent information, ranging from the patent level to the enterprise level, industrial level and country level. Through the patent information, we can identify the technology status and analyze the impact of the performance. We are also able to find out the flow of the knowledge through the network analysis. By that, we can not only identify the changes in technology, but also predict the direction of the future research. In the field using the network analysis there are two important analyses which utilize the patent citation information; citation indicator analysis utilizing the frequency of the citation and network analysis based on the citation relationships. Furthermore, this study analyzes whether there are any impacts between the size of the company and patent citation relationships. 74 S&P 500 registered companies that provide IT and communication services are selected for this study. In order to determine the relationship of patent citation between the companies, the patent citation in 2009 and 2010 is collected and sociomatrices which show the patent citation relationship between the companies are created. In addition, the companies' total assets are collected as an index of company size. The distance between companies is defined as the absolute value of the difference between the total assets. And simple differences are considered to be described as the hierarchy of the company. The QAP Correlation analysis and MR-QAP analysis is carried out by using the distance and hierarchy between companies, and also the sociomatrices that shows the patent citation in 2009 and 2010. Through the result of QAP Correlation analysis, the patent citation relationship between companies in the 2009's company's patent citation network and the 2010's company's patent citation network shows the highest correlation. In addition, positive correlation is shown in the patent citation relationships between companies and the distance between companies. This is because the patent citation relationship is increased when there is a difference of size between companies. Not only that, negative correlation is found through the analysis using the patent citation relationship between companies and the hierarchy between companies. Relatively it is indicated that there is a high evaluation about the patent of the higher tier companies influenced toward the lower tier companies. MR-QAP analysis is carried out as follow. The sociomatrix that is generated by using the year 2010 patent citation relationship is used as the dependent variable. Additionally the 2009's company's patent citation network and the distance and hierarchy networks between the companies are used as the independent variables. This study performed MR-QAP analysis to find the main factors influencing the patent citation relationship between the companies in 2010. The analysis results show that all independent variables have positively influenced the 2010's patent citation relationship between the companies. In particular, the 2009's patent citation relationship between the companies has the most significant impact on the 2010's, which means that there is consecutiveness regarding the patent citation relationships. Through the result of QAP correlation analysis and MR-QAP analysis, the patent citation relationship between companies is affected by the size of the companies. But the most significant impact is the patent citation relationships that had been done in the past. The reason why we need to maintain the patent citation relationship between companies is it might be important in the use of strategic aspect of the companies to look into relationships to share intellectual property between each other, also seen as an important auxiliary of the partner companies to cooperate with.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.3
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• pp.15-24
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• 2022

In 2000, a total of 23,794ha of forest was lost due to the East Coast forest fire, and about 70% of the damaged area was concentrated in Samcheok. In 2001, artificial restoration and natural restoration were implemented in the damaged area. This study was conducted to understand the current vegetation structure 21 years after the restoration of forest fire damage in the Samcheok, Gumbong Mountain area. As a result of classifying the vegetation community, it was divided into three communities: Quercus variabilis-Pinus densiflora community, Pinus densiflora-Quercus mongolica community, and Pinus thunbergii community. Quercus variabilis, Pinus densiflora, and Pinus thunbergii planted in the artificial restoration site were found to continue to grow as dominant species in the local vegetation after restoration. As for the species diversity index of the community, the Quercus variabilis-Pinus densiflora community dominated by deciduous broad-leaf trees showed the highest, and the coniferous forest Pinus thunbergii community showed the lowest. Vegetation in areas affected by forest fires is greatly affected by reforestation tree species, and 21 years later, it has shown a tendency to recover to the forest type before forest fire. In order to establish DataBase for effective restoration and to prepare monitoring data, it is necessary to construct data through continuous vegetation survey on the areas affected by forest fires.

• Investigative Magnetic Resonance Imaging
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• v.8 no.2
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• pp.79-85
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• 2004

Purpose : To evaluate the usefulness and reproducibility of $^1H$ MRS in different 1.5 T MR machines with different coils to compare the SNR, scan time and the spectral patterns in different brain regions in normal volunteers. Materials and Methods : Localized $^1H$ MR spectroscopy ($^1H$ MRS) was performed in a total of 10 normal volunteers (age; 20-45 years) with spectral parameters adjusted by the autoprescan routine (PROBE package). In all volunteers, MRS was performed in a three times using conventional MRS (Signa Horizon) with 1 channel coil and upgraded MRS (Echospeed plus with EXCITE) with both 1 channel and 8 channel coil. Using these three different machines and coils, SNRs of the spectra in both phantom and volunteers and (pre)scan time of MRS were compared. Two regions of the human brain (basal ganglia and deep white matter) were examined and relative metabolite ratios (NAA/Cr, Cho/Cr, and mI/Cr ratios) were measured in all volunteers. For all spectra, a STEAM localization sequence with three-pulse CHESS $H_2O$ suppression was used, with the following acquisition parameters: TR=3.0/2.0 sec, TE=30 msec, TM=13.7 msec, SW=2500 Hz, SI=2048 pts, AVG : 64/128, and NEX=2/8 (Signa/Echospeed). Results : The SNR was about over $30\%$ higher in Echospeed machine and time for prescan and scan was almost same in different machines and coils. Reliable spectra were obtained on both MRS systems and there were no significant differences in spectral patterns and relative metabolite ratios in two brain regions (p>0.05). Conclusion : Both conventional and new MRI systems are highly reliable and reproducible for $^1H$ MR spectroscopic examinations in human brains and there are no significant differences in applications for $^1H$ MRS between two different MRI systems.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.