• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Korean Journal of Environment and Ecology
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• v.33 no.5
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• pp.525-538
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• 2019

The purpose of this study was to analyze the habitat status and species diversity of benthic diatoms and estimate the applicability of TDI (Trophic Diatom Index) to obtain the basic data for the identification and management of created wetlands in the Nakdong River. We observed a total of 38 families and 173 species of benthic diatom during the survey period, and spring and autumn showed a similar number of species of 156 and 154, respectively. The result of the SOM (Self-Organizing Map) analysis showed that the distribution of benthic diatom was sensitive to environmental factors such as nutrient concentration and rainfall in each wetland. The cluster 1 was characterized by the survey sites of autumn mostly and consisted of points of high TDI, although the nutrients such as total phosphorus and total nitrogen were low, and the species number and abundance of diatoms were low. Conversely, cluster 4 was characterized by the survey sites of spring mostly and consisted of points of low TDI, even though total nitrogen was high. Considering that most of the created wetlands had the reduced inflow and outflow, the increased flow rate in the summer lowers nutrient values in autumn, and the species number and abundance of benthic diatom decreases due to the increase of turbidity, which reduces the light penetrations to the substrates. On the contrary, the TDI value is low in spring because the low water level causes insufficient substrate surface to the benthic diatoms, and it is too early for the establishment and development of saprophilous species. Although various studies have used TDI as an indicator for evaluating the habitat environment and water quality, it is not a good evaluation indicator in this study since the nutrient concentration in the wetlands mostly high as they have a low flow rate and are close to the stagnant area. Nevertheless, additional periodic surveys that comprehensively reflect the fact that the summer rainfall and inflow/outflow regulating function might affect the species diversity and distribution of benthic diatoms are necessary.

• Korean Journal of Ichthyology
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• v.21 no.3
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• pp.177-190
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• 2009

Species composition and abundance of marine organism in the Nakdong River estuary were investigated seasonally from November 2007 to November 2008. During the study period, a total of 169 species, 93 families, and 6 taxa were collected in the study area. Species included were 2 species in Bivalvia, 11 in Cephalopoda, 43 in Crustecea, 8 in Echinodermata, 6 in Gastropoda, and 99 in Pisces. The seasonal dominant species in number of individuals were Acropoma japonicum in autumn, Apogon lineatus in winter, Siphonalia spadicea fuscolineata in spring, and Crangon hakodatei in summer. Dominant species in abundance were Chelidonichthys spinosus in autumn, Liphius litulon in winter and spring, and Raja kenojei in summer. The number of species and their abundance, the species composition, and the diversity indices fluctuated with seasons. The organisms were divided into 17 groups by seasonal variation using a self-organizing map.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.7-16
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• 2000

This paper is focused on verifying time-space repetition of the highway accident and finding the their causes and deterrents. We classify all months into several seasonal groups, develop the model for each seasonal group and analyze the results of these models for Joong-bu highway. The existence of seasonal effect is verified by the analysis or self-organizing map and the accident indices. Agglomerative hierarchical cluster analysis which is used to decide the seasonal groups in accordance with accident patterns, winter group, spring-fall group. and summer group. The accident features of winter group are that the accident rate is high but the severity rate is low. while those of summer group are that the accident rate is low but the severity rate is high. Also, the regression model which is developed to identify the accident Pattern or each seasonal group represents that the season-related factors, such as the amount of rainfall, the amount of snowfall, days of rainfall, days of snowfall etc. are strongly related to the accident pattern of evert seasonal group and among these factors the traffic volume, amount of rainfall. the amount of snowfall and days of freezing importantly affect the local accident Pattern. So, seasonal effect should be considered to the identification of high-risk road section. the development of descriptive and Predictive accident model, the resource allocation model of accident in order to make safety management plan efficient.

• Korean Journal of Ecology and Environment
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• v.53 no.1
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• pp.11-21
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• 2020

In this study, we compared the spatio-temporal differences of phytoplankton communities among 4 lakes in the Boseong River basin. Field research was conducted quarterly from 2014 to 2017 for this study. A total of 345 species of phytoplankton were identified including 107 Bacillariophyceae, 175 Chlorophyceae, 27 Cyanophyceae and 36 other phytoplankton taxa. Lake Boseong showed higher species numbers and density of phytoplankton than other lakes (Dunn's test, P<0.01). Bacillariophyceae such as Asterionella formosa, Aulacoseira granulata, Fragilaria crotonensis was dominated in most research periods, whereas Scenedesmus ecornis and Coelastrum cambricum belonging to Chlorophyceae were dominant species on August. The self-organizing map (SOM) classified 3 clusters with 10 × 7 grid and showed spatio-temporal variation of phytoplankton communities based on significant difference among each clusters. Total 31 species of phytoplankton were chosen as a indicator species using indicator species analysis(ISA) and reflected seasonal phytoplankton succession and diversity and density of phytoplankton according to nutrient concentration. Water temperature, Secchi depth, conductivity and DO were identified as important factors affecting the differences of phytoplankton communities in the studied lakes in Boseong River basin using non-metric multidimensional scaling (NMDS).

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.4
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• pp.428-444
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• 2012

The species composition and seasonal variation of fisheries resources in Jinhae bay and Jinju bay, were studied using shrimp beam trawl through a year of 2010. During the study period, a total of 117 species were collected in Jinhae bay. Species included were 63 species in Pisces and 24 in Crustacea. And a total of 106 species were collected in Jinju bay. Species included were 57 species in Pisces and 31 in Crustacea. The dominant species were Zoarces gilli, Crangon hakodatei and Oratosquilla oratoria in Jinhae bay, and Crangon hakodatei, Leiognathus nuchalis and Charybdis bimaculata in Jinju bay. The samples were mainly grouped according to the location and season on the SOM. Group 1 with sample sites in Jinju bay, was characterized by high values of Parapenaeopsis tenella, Leiognathus nuchalis and Hexagrammos otakii. Group 2 with sample sites in April, were dominant Crangon hakodatei and Luidia quinaria. The samples in Group 3 were high values of Charybdis bimaculata and Pleuronichthys cornutus. Group 4 with sample sites in Jinhae bay, was characterized by high densities of Zoarces gilli and Pholis fangi. The dominant species, Crangon hakodatei, were catched egg-bearing females until June. Zoarces gilli and Leiognathus nuchalis were presented small size individuals during study period. It represented that study area is an important role in spawning and nursery ground for fisheries resources.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.193-200
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• 2014

Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user's features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user's features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.197-205
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• 2020

Korean language has the characteristics that the pronunciation of phoneme units such as vowels and consonants are fixed and the pronunciation associated with a notation does not change, so that foreign learners can approach rather easily Korean language. However, when one pronounces words, phrases, or sentences, the pronunciation changes in a manner of a wide variation and complexity at the boundaries of syllables, and the association of notation and pronunciation does not hold any more. Consequently, it is very difficult for foreign learners to study Korean standard pronunciations. Despite these difficulties, it is believed that systematic analysis of pronunciation errors for Korean words is possible according to the advantageous observations that the relationship between Korean notations and pronunciations can be described as a set of firm rules without exceptions unlike other languages including English. In this paper, we propose a visualization framework which shows the differences between standard pronunciations and erratic ones as quantitative measures on the computer screen. Previous researches only show color representation and 3D graphics of speech properties, or an animated view of changing shapes of lips and mouth cavity. Moreover, the features used in the analysis are only point data such as the average of a speech range. In this study, we propose a method which can directly use the time-series data instead of using summary or distorted data. This was realized by using the deep learning-based technique which combines Self-organizing map, variational autoencoder model, and Markov model, and we achieved a superior performance enhancement compared to the method using the point-based data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.337-352
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• 2023

In general, Rainfall-Runoff model parameter set is optimized using the entire data to calculate unique parameter set. However, Korea has a large precipitation deviation according to the season, and it is expected to even worsen due to climate change. Therefore, the need for hydrological data considering seasonal characteristics. In this study, we conducted regional sensitivity analysis(RSA) using the conceptual Rainfall-Runoff model, GR4J aimed at the Soyanggang dam basin, and clustered combining the RSA results with hydrometeorological data using Self-Organizing map(SOM). In order to consider the climate characteristics in parameter estimation, the data was divided based on clustering, and a calibration approach of the Rainfall-Runoff model was developed by comparing the objective functions of the Global Optimization method. The performance of calibration was evaluated by statistical techniques. As a result, it was confirmed that the model performance during the Cold period(November~April) with a relatively low flow rate was improved. This is expected to improve the performance and predictability of the hydrological model for areas that have a large precipitation deviation such as Monsoon climate.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.