• Korean Journal of Soil Science and Fertilizer
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• v.32 no.4
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• pp.398-404
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• 1999

Factor analysis technique was employed to screen the principal indicators influencing soil and water qualities in the intensively cultivated areas of the Han River Basin. Soil chemical parameters were analyzed for the soil samples collected at intensive farming area in Pyungchang-Gun, and water quality monitoring data were obtained from the agricultural small catchments of Han River Basin during 1996 and 1997. Among the $11{\times}11$ cross correlation matrix, 29 correlations were significant out of 55 soil quality indicator pairs. The overall Kaiser's measure of sampling adequacy(KMS) value was acceptable with 0.60. Most indicators except iron were acceptable. Among soil indicators, the first factors showing high factor loadings were pH, Ca and Mg. The factor loading was the highest for Ca. The second factor could be characterized as phosphate and micronutrient. The third factor was organic matter and EC, and the fourth factor was potassium and Fe. Out of 190 water quality indicators, 86 correlations were significant. Overall KMS value was 0.74, but the KMS values for pH, TSS, Cd, Cu and Fe were lower than 50. The first factor of EC accounts 27.1 percents of the total variance, and showed high factor loadings with Na, Ca, $SO_4$, Mg, K, Cl, $NO_3$, and T-N. The second factor showed high loadings with Zn, Fe, Mn and Cd. The third to seventh factors could be characterized as $PO_4$, TSS, inorganic nitrogen, pH and T-P, and Cu factors, respectively. The factor score for EC was the highest in Kuri, followed by Chunchon, Dunnae and Daegwanryng. The factor score for heavy metals were the highest in the Daegwanryng. The results demonstrated that the factor analysis could be useful to select the most principal factor influencing soil and water qualities in the agricultural watershed.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.89-115
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• 2019

This study was conducted to present the stylistic differences between Arthur Conan Doyle and Agatha Christie, famous as writers of classical mystery novels, through data analysis, and further to present the analytical methodology of the study of style based on text mining. The reason why we chose mystery novels for our research is because the unique devices that exist in classical mystery novels have strong stylistic characteristics, and furthermore, by choosing Arthur Conan Doyle and Agatha Christie, who are also famous to the general reader, as subjects of analysis, so that people who are unfamiliar with the research can be familiar with them. The primary objective of this study is to identify how the differences exist within the text and to interpret the effects of these differences on the reader. Accordingly, in addition to events and characters, which are key elements of mystery novels, the writer's grammatical style of writing was defined in style and attempted to analyze it. Two series and four books were selected by each writer, and the text was divided into sentences to secure data. After measuring and granting the emotional score according to each sentence, the emotions of the page progress were visualized as a graph, and the trend of the event progress in the novel was identified under eight themes by applying Topic modeling according to the page. By organizing co-occurrence matrices and performing network analysis, we were able to visually see changes in relationships between people as events progressed. In addition, the entire sentence was divided into a grammatical system based on a total of six types of writing style to identify differences between writers and between works. This enabled us to identify not only the general grammatical writing style of the author, but also the inherent stylistic characteristics in their unconsciousness, and to interpret the effects of these characteristics on the reader. This series of research processes can help to understand the context of the entire text based on a defined understanding of the style, and furthermore, by integrating previously individually conducted stylistic studies. This prior understanding can also contribute to discovering and clarifying the existence of text in unstructured data, including online text. This could help enable more accurate recognition of emotions and delivery of commands on an interactive artificial intelligence platform that currently converts voice into natural language. In the face of increasing attempts to analyze online texts, including New Media, in many ways and discover social phenomena and managerial values, it is expected to contribute to more meaningful online text analysis and semantic interpretation through the links to these studies. However, the fact that the analysis data used in this study are two or four books by author can be considered as a limitation in that the data analysis was not attempted in sufficient quantities. The application of the writing characteristics applied to the Korean text even though it was an English text also could be limitation. The more diverse stylistic characteristics were limited to six, and the less likely interpretation was also considered as a limitation. In addition, it is also regrettable that the research was conducted by analyzing classical mystery novels rather than text that is commonly used today, and that various classical mystery novel writers were not compared. Subsequent research will attempt to increase the diversity of interpretations by taking into account a wider variety of grammatical systems and stylistic structures and will also be applied to the current frequently used online text analysis to assess the potential for interpretation. It is expected that this will enable the interpretation and definition of the specific structure of the style and that various usability can be considered.

• Korean Journal of Food Science and Technology
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• v.51 no.4
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• pp.316-323
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• 2019

It is difficult to analyze pymetrozine in citrus fruits using the hydromatrix method because of its low efficiency of purification and overlap of matrix and pymetrozine peaks. Liquid-liquid extraction can analyze pymetrozine in citrus fruits using dichloromethane. Since low pH interferes with the extraction of pymetrozine, the extracts of citrus fruits were maintained over pH 7.0 by adding borax buffer and 1 N NaOH in the improved method. According to the improved method, citrus fruits (such as lemon, lime, orange, tangerine, and grapefruit) were extracted and purified for HPLC-photo diode array analysis. The results of validation were as follows: $4.360{\mu}g/kg$ of limit of detection, $14.533{\mu}g/kg$ of limit of quantitation, and 0.007 mg/kg of method quantitative limit. Citrus fruits spiked with pymetrozine showed a recovery range from 71.8 to 83.7% and a coefficient of variation below 6%. Thus, the improved method can efficiently analyze pymetrozine in citrus fruits.

• Korean Journal of Environmental Agriculture
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• v.41 no.2
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• pp.82-94
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• 2022

BACKGROUND: Spiropidion and its metabolite are tetramic acid insecticide and require the establishment of an official analysis method for the safety management because they are newly registered in Korea. Therefore, this study was to determine the analysis method of residual spiropidion and its metabolite for the five representative agricultural products. METHODS AND RESULTS: Three QuEChERS methods (original, AOAC, and EN method) were applied to optimize the extraction method, and the EN method was finally selected by comparing the recovery test and matrix effect results. Various adsorbent agents were applied to establish the clean up method. As a result, the recovery of spiropidion was reduced when using the dispersive-SPE method with MgSO₄, primary secondary amine (PSA), graphitized carbon black (GCB) and octadecyl (C₁₈) in soybean. Color interference was minimized by selecting the case including GCB and C18 in addition to MgSO₄. This method was established as the final analysis method. LC-MS/MS was used for the analysis by considering the selectivity and sensitivity of the target pesticide and the analysis was performed in MRM mode. The results of the recovery test using the established analysis method and inter laboratory validation showed a valid range of 79.4-108.4%, with relative standard deviation and coefficient of variation were less than 7.2% and 14.4%, respectively. CONCLUSION(S): Spiropidion and its metabolite could be analyzed with a modified QuEChERS method, and the established method would be widely available to ensure the safety of residual insecticides in Korea.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

• Elastomers and Composites
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• v.22 no.3
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• pp.204-212
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• 1987

The purpose of this study is to investigate the reinforced effect between MGF treated silane coupling agents and rubber matrix under the configuration chemical bonds, also the effect of triazine thiol compounds. For this study, vulcanizates were prepared with fifteen different compounding formulas. Their vulcanization characteristics, physical properties were examined by means of the ODR(Oscillating Dist Rheometer), the tensile tester, the benzene swelling test. The results of this study obtained are as follows: 1. In the ODR test, the MA vulcanizate was the fastest one in terms of having reached to optimum cure time($t_{90}$) and, with the same formula, when MGF vulcanizates, the shortest optimum cure times has appeared. 2. The SA, SC vulcanizates were the best the other in the physical properties such as 100%modulus, 200%modulus, 300%modulus, tensile strength. The SB vulcanizate, with higher density of crosslinking than other vulcanizates. The vulcanizates, which were filled with MGF treated with silane coupling agents we were the higher density of crosslinking than vulcanizates filled with MGF only. 3. In aging properties, the silica vulcanizates appeared to be better than the other vulcanizates. The aging Properties of treated MGF vulcanizates were similar to the silica vulcanizates. The(CR+APS+silica) and(CR+APS+MCF) were easily crosslinked by exposure to the air, and the physical properties have been improved.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.35-48
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• 2010

Today, the fashion market challenged by a maturing retail market needs a new paradigm in the "evolution of brand" to improve their comparative advantages. An important issue in fashion marketing is lifestyle brand extension with a specific aim to meet consumers' specific needs for their changing lifestyle. For fashion brand extensions into lifestyle product categories, Gen Y and Baby Boomer are emerging as "prospects"-Baby Boomers who are renovating their lifestyle, and generation Y experiencing changes in their life stage-with demands for buying new products. Therefore, it is imperative that apparel companies pay special attention to the consumer cohort for brand extension to create and manage their brand equity in a new product category. The purposes of this study are to (a) evaluate brand equity between parent and extension brands; (b) identify consumers' perceived marketing elements for brand extension; and (c) estimate a structural equation model for examining causative relationship between marketing elements and brand equity for brand extensions in lifestyle product category including home fashion items for the selected two groups (e.g., Gen Y, and Baby boomer). For theoretical frameworks, this study focused on the traditional marketing 4P's mix to identify what marketing element is more importantly related to brand extension equity for this study. It is assumed that comparable marketing capability can be critical to establish "brand extension equity", leads to successfully entering the new categories. Drawing from the relevant literature, this study developed research hypotheses incorporating brand equity factors and marketing elements by focusing on the selected consumers (e.g., Gen Y, Baby Boomer). In the context of brand extension in the lifestyle products, constructs of brand equity consist of brand awareness/association, brand perceptions (e.g., perceived quality, emotional value) and brand resonance adapted from CBBE factors (Keller, 2001). It is postulated that the marketing elements create brand extension equity in terms of brand awareness/association, brand perceptions by the brand extension into lifestyle products, which in turn influence brand resonance. For data collection, the sample was comprised of Korean female consumers in Gen Y and Baby Boomer consumer categories who have a high demand for lifestyle products due to changing their lifecycles. A total of 651 usable questionnaires were obtained from female consumers of Gen Y (n=326) and Baby Boomer (n=325) in South Korea. Structural and measurement models using a correlation matrix was estimated using LISREL 8.8. Findings indicated that perceived marketing elements for brand extension consisted of three factors: price/store image, product, and advertising. In the model of Gen Y consumers, price/store image had a positive effect on brand equity factors (e.g., brand awareness/association, perceived quality), while product had positive effect on emotional value in the brand extensions; and the brand awareness/association was likely to increase the perceived quality and emotional value, leading to brand resonance for brand extensions in the lifestyle products. In the model of Baby Boomer consumers, price/store image had a positive effect on perceived quality, which created brand resonance of brand extension; and product had a positive effect on perceived quality and emotional value, which leads to brand resonance for brand extension in the lifestyle products. However, advertising was negatively related to brand equity for both groups. This study provides an insight for fashion marketers in developing a successful brand extension strategy, leading to a sustainable competitive advantage. This study complements and extends prior works in the brand extension through critical factors of marketing efforts that affect brand extension success. Findings support a synergy effect on leveraging of fashion brand extensions (Aaker and Keller, 1990; Tauber, 1988; Shine et al., 2007; Pitta and Katsanis, 1995) in conjunction with marketing actions for entering into the new product category. Thus, it is recommended that marketers targeting both Gen Y and Baby Boomer can reduce marketing cost for entering the new product category (e.g., home furnishings) by standardized marketing efforts; fashion marketers can (a) offer extension lines with premium ranges of price; (b) place an emphasis on upscale features of store image positioning by a retail channel (e.g., specialty department store) in Korea, and (c) combine apparel with lifestyle product assortments including innovative style and designer’s limited editions. With respect to brand equity, a key to successful brand extension is consumers’ brand awareness or association that ensures brand identity with new product category. It is imperative for marketers to have knowledge of what contributes to more concrete associations in a market entry into new product categories. For fashion brands, a second key of brand extension can be a "luxury" lifestyle approach into new product categories, in that higher price or store image had impact on perceived quality that established brand resonance. More importantly, this study increases the theoretical understanding of brand extension and suggests directions for marketers as they establish marketing program at Gen Y and Baby Boomers.