• Journal of the Korean association of regional geographers
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• v.12 no.4
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• pp.449-460
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• 2006

This study aims to analyze distribution of natural lakes including lagoonal lake(lagoon) and tributary dammed lake(tributary lake) and calculate the size, morphology in order to interpret time-serial change of lakes using methodology of remote sensing images(1990s), GIS and topographic maps(1920s) in east coast of Korean Peninsular. Analysis results show that in 1990s, there are 57 natural lakes, with the total size of $75.62km^2$ over size $0.01km^2$. marine-origin lagoons are 48 with total size of $64.85km^2$, composing 85% of total natural lake, and the largest lagoon is Beonpo in Raseon City. Tributary lakes have been formed by damming of tributary channels by fluvial sand bars from main stream, located nearby at coastal zone, similar to lagoon sites. Large tributary lake, Jangyeonho, is developed in lava plateau dissection valley of Eorang Gun, Hamnam Province. There are more distributed at Duman River mouth$\sim$Cheongjin City, Heungnam City$\sim$Hodo Peninsular and Anbyeon Gun$\sim$Gangreung City. Geomorphometrically, correlation of size to circumference is very high, but correlation of size to shape irregularity is very low. The direction of lagoonal coast, NW-SE and NE-SW are predominated due to direction of tectonic structure and longshore currents. The length of the river into lake are generally short, maximum under 15km, and lake size is smaller, degree of size decreasing is higher. Geomorphic patterns of the lake location are classified as coast-hill range, coastal plain, coastal plain-channel valley, coastal plain-hill range and channel valley-hill range. During from 1920s to 1990s, change with lake size decreasing is highest at coastal plain-channel valley, next is coastal plain. Causes of the size decreasing are fluvial deposition from upper rivers and human impacts such as reclamation.

• Journal of Family Resource Management and Policy Review
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• v.25 no.3
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• pp.87-102
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• 2021

Child abuse and neglect are recently increasing in Korea, and although the government has actively improved the child protection system, the number of abused children and the rate of cases judged as abuse have continuously risen. Given that 75% of child abusers are parents, child abuse and neglect are expected to recur. To prevent such a recurrence, various intervention programs for abused children and their parents are required. The purpose of this study were to design a recovery support service process and investigate the effectiveness of pilot program for families of origin, including neglected(protected) children, to improve the system by which these programs are operated, and formulate policy alternatives that reinforce "family preservation" principles. The pilot program was implemented from June to November 2020 in 4-local healthy family support center. The number of program participants and the frequency of participation in each other differed, because of the difference in number of confirmed coronavirus cases in each region and the requirement for social distancing. Through the program, a community-based service process was developed for neglected(protected) children and their parents, and cooperative networks between related facilities and institutions were established. The study formulated the following recommendations: First, a cooperation system among government departments mandated to provide different services to neglected(protected) children is needed. Second, wider and various channels through which abused children can avail of protective services should be developed within communities. Third, more stable environments for program operation should be cultivated, and cooperative partnerships should be sought for knowledge sharing among relevant government departments. Another necessary measure is for a center to develop its own business model, in which the duplication of services provided by involved organizations is avoided. Finally, clear guidelines, administrative standards, and specific plans for program operation should be arranged. Also regional characteristics are maintained, but services should be standardized.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.27-40
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• 2020

The marathon has long been established as a representative lifestyle for all ages. With the recent expansion of the Work and Life Balance trend across the society, marathon with a relatively low barrier to entry is gaining popularity among young people in their 20s and 30s. By analyzing the issues and related words of the marathon event, we will analyze the spottainment elements of the marathon event that is popular among young people through keywords, and suggest a development plan for the differentiated event. In order to analyze keywords and related words, blogs, cafes and news provided by Naver and Daum were selected as analysis channels, and 'JTBC Marathon' and 'Culture' were extracted as key words for data search. The data analysis period was limited to a three-month period from August 13, 2019 to November 13, 2019, when the application for participation in the 2019 JTBC Marathon was started. For data collection and analysis, frequency and matrix data were extracted through social matrix program Textom. In addition, the degree of the relationship was quantified by analyzing the connection structure and the centrality of the degree of connection between the words. Although the marathon is a personal movement, young people share a common denominator of "running" and form a new cultural group called "running crew" with other young people. Through this, it was found that a marathon competition culture was formed as a festival venue where people could train together, participate together, and escape from the image of a marathon run alone and fight with themselves.

• Asia Marketing Journal
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• v.13 no.4
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• pp.227-251
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• 2012

This study analyzed Woori Investment and Securities' industry-leading marketing from both a brand management and a marketing decision-making perspective. By executing a different marketing strategy from its competitors, Woori Investment and Securities recognized recent changes in the asset management and investment markets as an open opportunity, and quickly responded to the market changes. First, the company launched the octo brand as a multi-account product, two years before its competitors offered their own products. In particular, it created a differentiated brand image, using the blue octopus character, which became familiar to the general financial community, and was consistently employed as part of an integrated marketing communications strategy. Second, it executed a brand expansion strategy by sub-branding octo in a variety of new financial products, responding to rapid changes in the domestic financial and asset management markets. Through this strategic evolution, the octo brand became a successful wealth management brand and representative of Woori Investment & Securities. Third, it has converged market research, demand and trend analysis, and customer needs acquired through various customer contact channels into a marketing perspective. Thus, marketing has participated in the product development stage, a rarity in the finance industry. Woori Investment and Securities has a leading marketing system. The heart of the successful product creation lies in a collaboration of their customer bases among the finance companies in the Woori Financial Group. The present study suggested a corresponding strategy for octo brand, which is expected to enter into the maturity stage of its product life cycle. In addition, this study found a need to modify the current positioning strategy in order to position and preserve sustainability in the increasingly competitive asset management market. It also suggested the need for an offensive strategy to counter the number one M/S company, and address the issue of cannibalism in the Woori Financial Group.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1553-1563
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• 2023

The Geostationary Ocean Color Imager-II (GOCI-II) is a satellite designed for ocean color observation, covering the Northeast Asian region and the entire disk of the Earth. It commenced operations in 2020, succeeding its predecessor, GOCI, which had been active for the previous decade. In this study, we aimed to enhance the atmospheric correction algorithm, a critical step in producing satellite-based ocean color data, by performing cross-calibration on the GOCI-II near-infrared (NIR) band using the GOCI NIR band. To achieve this, we conducted a cross-calibration study on the top-of-atmosphere (TOA) radiance of the NIR band and derived a vicarious calibration gain for two NIR bands (745 and 865 nm). As a result of applying this gain, the offset of two sensors decreased and the ratio approached 1. It shows that consistency of two sensors was improved. Also, the Rayleigh-corrected reflectance at 745 nm and 865 nm increased by 5.62% and 9.52%, respectively. This alteration had implications for the ratio of Rayleigh-corrected reflectance at these wavelengths, potentially impacting the atmospheric correction results across all spectral bands, particularly during the aerosol reflectance correction process within the atmospheric correction algorithm. Due to the limited overlapping operational period of GOCI and GOCI-II satellites, we only used data from March 2021. Nevertheless, we anticipate further enhancements through ongoing cross-calibration research with other satellites in the future. Additionally, it is essential to apply the vicarious calibration gain derived for the NIR band in this study to perform vicarious calibration for the visible channels and assess its impact on the accuracy of the ocean color products.

• Journal of Venture Innovation
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• v.5 no.2
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• pp.49-66
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• 2022

This study intends to examine how the perception of covid-19 risk affects consumers' consumption behavior based on previous studies in a situation where the spread of covid-19 is prolonged. This study demonstrates how consumers' perception of covid-19 risk affects online and offline consumption behavior through the perceived severity, perceived vulnerability, coping effectiveness, and self-efficacy of the revised protective motivation theory (Rogers, 1983). We want to test it through analysis. In order to achieve the purpose of this study, consumers living in Seoul and Gyeonggi Province who have purchased within the past 3 months were selected as a sample. In addition, variable data such as risk perception of covid-19, perceived severity, perceived vulnerability, coping effectiveness, self-efficacy, online purchase attitude and purchase intention, offline purchase attitude and purchase intention were collected through the questionnaire.A total of 363 copies of valid responses were tested to test the hypothesis of the relationship between variables through the covariance structure model. The analysis results of this study were first, that covid-19 risk perception had a significant positive (+) effect on perceived severity, perceived vulnerability, and coping effectiveness. Second, perceived severity and perceived vulnerability were found to have a significant positive (+) effect on offline purchasing attitude. Third, perceived severity, perceived vulnerability, coping plan effectiveness, and self-efficacy were all found to have significant positive (+) effects on online purchase attitude. Finally, it was found that offline purchase attitude and online purchase attitude had a significant positive (+) effect on offline purchase intention and online purchase intention, respectively. Also, it was found that online purchase attitude had a negative (-) effect on offline purchase intention. The results of this analysis will provide meaningful implications for the establishment of strategies for distribution channels according to the social risk of infectious diseases.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.33-37
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• 2010

Purpose: In the early stage of using PET/CT, it was used to damper revision but recently shows that CT with MDCT is commonly used and works well for an anatomical diagnosis. This hospital makes the accuracy and convenience more higher in the diagnosis and evaluate of coronary heart disease through concurrently running myocardial perfusion SPECT examination, myocardial PET examination with FDG, and CT coronary artery CT angiography(coronary CTA) used PET/CT with 64-slice. This report shows protocol and image based on results from about 400 coronary heart disease examinations since having 64 channels PET/CT in July 2007. Materials and Methods: An Equipment for this examination is 64-slice CT and Discovery VCT (DVCT) that is consisted of PET with BGO ($Bi_4Ge_3O_{12}$) scintillation crystal by GE health care. First myocardial perfusion SPECT with pharmacologic stress test to reduce waiting time of a patient and get a quick diagnosis and evaluation, and right after it, myocardial FDG PET examination and coronary CTA run without a break. One-stop evaluation protocol of ischemic heart disease is as follows. 1)Myocardial perfusion SPECT with pharmacologic stress: A patient is injected with $^{99m}Tc$-MIBI 10 mCi and does not have any fatty food for myocardial PET examination and drink natural water with ursodeoxcholic acid 100 mg and we get SPECT image in an hour. 2)Myocardial FDG PET: To reduce blood fatty content and to increase uptake of FDG, we used creative oral glucose load using insulin and Acipimox to according to blood acid content. A patient is injected with $^{18}F$-FDG 5 mCi for reduction of his radiation exposure and we get a gated image an hour later and get delay image when we need. 3) Coronary CTA: The most important point is to control heart rate and to get cooperation of patient's breath. In order to reduce a heart rate of him or her below 65 beats, let him or her take beta blocker 50 mg ~ 200 mg after a consultation with a doctor about it and have breath-practices then have the examination. Right before the examination, we spray isosorbide dinitrate 3 to 5 times to lower tension of bessel wall and to extension a blood wall of a patient. It makes to get better the shape of an anatomy. At filming, a patient is injected CT contrast with high pressure and have enough practices before the examination in order to have no problem. For reduction of his radiation exposure, we have to do ECG-triggered X-ray tube modulation exposure. Results: We evaluate coronary artery stenosis through coronary CTA and study correlation (culprit vessel check) of a decline between stenosis and perfusion from the myocardial perfusion SPECT with pharmacologic stress, coronary CTA, and can check viability of infarction or hibernating myocardium by FDG PET. Conclusion: The examination makes us to set up a direction of remedy (drug treatment, PCI, CABG) because we can estimate of effect from remedy, lesion site and severity. In addition, we have an advantage that it takes just 3 hours and one-stop in that all of process of examinations run in succession and at the same time. Therefore it shows that the method is useful in one stop evaluation of ischemic heart disease.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.