• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.271-278
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• 2022

This study attempted to prepare basic data for international students with Vietnamese nationality in Korea to identify positive psychological capital, social support, social presence, and quality of life and to prepare support measures to improve their quality of life. Data collection is from May 1, 2021 to June 30, 2021, and was conducted through an online survey for anonymity and convenience. For data analysis, the IBM SPSS/25 statistical program was used, and the significance level for the results was measured as .05, and the reliability of each measurement tool was calculated. The results of this study are summarized as follows. First, the age of the subjects was '24 years old-27 years old', and women accounted for the majority. In the fourth grade, the fourth grade was the most, with "outgoing" personality, "sometimes" experiences of interpersonal conflict, "more than four years and less than five years" in the period of residence in Korea, and the level of Korean proficiency was "grade three." Second, the average quality of life of Vietnamese international students was 3.52 points (out of 5 points), positive psychological capital was 3.98 points (out of 6 points), social support was 2.96 points (out of 4 points), and social presence was 3.59 points (out of 5 points). Third, in the case of the quality of life of Vietnamese international students, there was a significant difference according to their personality, and as a result of post-verification, the quality of life of the 'extroverted' group was higher than the 'mixed' group. There was a significant difference according to interpersonal conflict), and as a result of post-examination, the "no conflict" group had a higher quality of life than the "conflict frequent" group. Fourth, the factors that most affected the subject's quality of life were social support, positive psychological capital, and personality (extroverted). The explanatory power of the model was 33.2%.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.489-495
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• 2023

Radiation shielding facilities are constructed in locations where diagnostic radiation generators are installed, with the aim of preventing exposure for patients and radiation workers. The purpose of this study is seek to compare and validate the trend of attenuation thickness of lead, the primary material in these radiation shielding facilities, at different maximum tube voltages by Monte Carlo simulations and measurement. We employed the Monte Carlo N-Particle 6 simulation code. Within this simulation, we set a lead shielding arrangement, where the distance between the source and the lead sheet was set at 100 cm and the field of view was set at 10 × 10 cm². Additionally, we varied the tube voltages to encompass 80, 100, 120, and 140 kVp. We calculated energy spectra for each respective tube voltage and applied them in the simulations. Lead thicknesses corresponding to attenuation rates of 50, 70, 90, and 95% were determined for tube voltages of 80, 100, 120, and 140 kVp. For 80 kVp, the calculated thicknesses for these attenuation rates were 0.03, 0.08, 0.21, and 0.33 mm, respectively. For 100 kVp, the values were 0.05, 0.12, 0.30, and 0.50 mm. Similarly, for 120 kVp, they were 0.06, 0.14, 0.38, and 0.56 mm. Lastly, at 140 kVp, the corresponding thicknesses were 0.08, 0.16, 0.42, and 0.61 mm. Measurements were conducted to validate the calculated lead thicknesses. The radiation generator employed was the GE Healthcare Discovery XR 656, and the dosimeter used was the IBA MagicMax. The experimental results showed that at 80 kVp, the attenuation rates for different thicknesses were 43.56, 70.33, 89.85, and 93.05%, respectively. Similarly, at 100 kVp, the rates were 52.49, 72.26, 86.31, and 92.17%. For 120 kVp, the attenuation rates were 48.26, 71.18, 87.30, and 91.56%. Lastly, at 140 kVp, they were measured 50.45, 68.75, 89.95, and 91.65%. Upon comparing the simulation and experimental results, it was confirmed that the differences between the two values were within an average of approximately 3%. These research findings serve to validate the reliability of Monte Carlo simulations and could be employed as fundamental data for future radiation shielding facility construction.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.201-214
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• 2022

This study theoretically and empirically examined whether organizational communication mediates the effect of organizational learning culture perceived by members in the organization on task performance and contextual performance. Organizational learning culture is defined as a culture that is good at creating, acquiring, transferring, and modifying behavior to reflect new knowledge and insights. The hypothesis of this study is that the perceived organizational learning culture can increase performance through organizational communication between members. In particular, we measured communication within the organization into three types: upward, horizontal, and downward. These communications were set as mediating variables. In empirical studies, independent variables were perceived organizational learning culture, mediation variables were upward, horizontal and downward communication, and dependent variables were task performance and contextual performance. Hypothesis 1 is that the organizational learning culture will have a positive effect on employees' tasks and contextual performance. Hypothesis 2 is about the mediating effect of communication on the relationship between Hypothesis 1. In the empirical study, after verifying the validity and reliability of the research variables, correlation analysis and hypothesis verification were conducted. Hypothesis 1 was verified through regression analysis, and all detailed hypotheses were supported. To verify Hypothesis 2, we conducted a bootstrap test using process macro to separate the total, direct, and indirect effects and examine the significance of the indirect effects. As a result, Hypothesis 2 was partially supported. Downward communication mediated organizational learning culture and task and contextual performance, and horizontal communication mediated organizational learning culture and contextual performance. The mediating effect of upward communication was not significant. The results of this study contributed to the suggestion of implications, research limitations, and research directions. Organizational learning culture is the direction and intention of the organization to achieve its goals through the learning and growth of its members. By strengthening internal motivation, organizational members can take voluntary desirable actions that help groups and organizations as well as essential tasks given. since this relationship appears as a medium of downward communication, organizations can strengthen the relationship between organizational learning culture and performance through leadership education.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.61-71
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• 2024

In this study, we analyzed chlorogenic acid indicator components in preparation for the additional listing of green coffee bean extract in the Health Functional Food Code and optimized caffeine for simultaneous analysis. We extracted chlorogenic acid and caffeine using 30% methanol, phosphoric acid solution, and acetonitrile-containing phosphoric acid and analyzed them at 330 and 280 nm, respectively, using liquid chromatography. Our analysis validation results yielded a correlation coefficient (R²) revealing a significance level of at least 0.999 within the linear quantitative range. The chlorogenic acid and caffeine detection and quantification limits were 0.5 and 0.2 ㎍/mL and 1.4, and 0.4 ㎍/mL, respectively. We confirmed that the precision and accuracy results were suitable using the AOAC validation guidelines. Finally, we developed a simultaneous chlorogenic acid and caffeine analysis approach. In addition, we confirmed that our analysis approach could simultaneously quantify chlorogenic acid and caffeine by examining the applicability of each formulation through prototypes and distribution products. In conclusion, the results of this study demonstrated that the standardized analysis would expectably increase chlorogenic acidcontaining health functional food quality control reliability.

• Journal of Distribution Research
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• v.16 no.4
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• pp.1-27
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• 2011

Introduction: Sales of private labels(PU have been growing m recent years. Globally, PLs have already achieved 20% share, although between 25 and 50% share in most of the European markets(AC. Nielson, 2005). These products are aimed to have comparable quality and prices as national brand(NB) products and have been continuously eroding manufacturer's national brand market share. Stores have also started introducing premium PLs that are of higher-quality and more reasonably priced compared to NBs. Worldwide, many retailers already have a multiple-tier private label architecture. Consumers as a consequence are now able to have a more diverse brand choice in store than ever before. Since premium PLs are priced higher than regular PLs and even, in some cases, above NBs, stores can expect to generate higher profits. Brand extensions and private label have been extensively studied in the marketing field. However, less attention has been paid to the private label extension. Therefore, this research focuses on private label extension using the Multi-Attribute Attitude Model(Fishbein and Ajzen, 1975). Especially there are few studies that consider the hierarchical effect of the PL's two parent brands: store brand and the original PL. We assume that the attitude toward each of the two parent brands affects the attitude towards the extended PL. The influence from each parent brand toward extended PL will vary according to the perceived fit between each parent brand and the extended PL. This research focuses on how these two parent brands act as reference points to one another in the consumers' choice consideration. Specifically we seek to understand how store image and attitude towards original PL affect consumer perceptions of extended premium PL. How consumers perceive extended premium PLs could provide strategic suggestions for retailer managers with specific suggestions on whether it is more effective: to position extended premium PL similarly or dissimilarly to original PL especially on the quality dimension and congruency with store image. There is an extensive body of research on branding and brand extensions (e.g. Aaker and Keller, 1990) and more recently on PLs(e.g. Kumar and Steenkamp, 2007). However there are no studies to date that look at the upgrading and influence of original PLs and attitude towards store on the premium PL extension. This research wishes to make a contribution to this gap using the perceived fit difference between parent brands and extended premium PL as the context. In order to meet the above objectives, we investigate which factors heighten consumers' positive attitude toward premium PL extension. Research Model and Hypotheses: When considering the attitude towards the premium PL extension, we expect four factors to have an influence: attitude towards store; attitude towards original PL; perceived congruity between the store image and the premium PL; perceived similarity between the original PL and the premium PL. We expect that all these factors have an influence on consumer attitude towards premium PL extension. Figure 1 gives the research model and hypotheses. Method: Data were collected by an intercept survey conducted on consumers at discount stores. 403 survey responses were attained (total 59.8% female, across all age ranges). Respondents were asked to respond to a series of Questions measured on 7 point likert-type scales. The survey consisted of Questions that measured: the trust towards store and the original PL; the satisfaction towards store and the original PL; the attitudes towards store, the original PL, and the extended premium PL; the perceived similarity of the original PL and the extended premium PL; the perceived congruity between the store image and the extended premium PL. Product images with specific explanations of the features of premium PL, regular PL and NB we reused as the stimuli for the Question response. We developed scales to measure the research constructs. Cronbach's alphaw as measured each construct with the reliability for all constructs exceeding the .70 standard(Nunnally, 1978). Results: To test the hypotheses, path analysis was conducted using LISREL 8.30. The path analysis for verification of the model produced satisfactory results. The validity index shows acceptable results(${\chi}^2=427.00$(P=0.00), GFI= .90, AGFI= .87, NFI= .91, RMSEA= .062, RMR= .047). With the increasing retailer use of premium PLBs, the intention of this research was to examine how consumers use original PL and store image as reference points as to the attitude towards premium PL extension. Results(see table 1 & 2) show that the attitude of each parent brand (attitudes toward store and original pL) influences the attitude towards extended PL and their perceived fit moderates these influences. Attitude toward the extended PL was influenced by the relative level of perceived fit. Discussion of results and future direction: These results suggest that the future strategy for the PL extension needs to consider that positive parent brand attitude is more strongly associated with the attitude toward PL extensions. Specifically, to improve attitude towards PL extension, building and maintaining positive attitude towards original PL is necessary. Positioning premium PL congruently to store image is also important for positive attitude. In order to improve this research, the following alternatives should also be considered. To improve the research model's predictive power, more diverse products should be included in study. Other attributes of product should also be included such as design, brand name since we only considered trust and satisfaction as factors to build consumer attitudes.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Food Hygiene and Safety
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• v.37 no.2
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• pp.39-45
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• 2022

This study aimed to investigate the validation and modify the analytical method to determine quercetin-3-𝑜-gentiobioside and isoquercitrin in Abelmoschus esculentus L. Moench for the standardization of ingredients in development of functional health products. The analytical method was validated based on the ICH (International Conference for Harmonization) guidelines to verify the reliability and validity there of on the specificity, linearity, accuracy, precision, detection limit and quantification limit. For the HPLC analysis method, the peak retention time of the index component of the standard solution and the peak retention time of the index component of A. esculentus L. Moench powder sample were consistent with the spectra thereof, confirming the specificity. The calibration curves of quercetin-3-𝑜-gentiobioside and isoquercitrin showed a linearity with a near-one correlation coefficient (0.9999 and 0.9999), indicating the high suitability thereof for the analysis. A. esculentus L. Moench powder sample of a known concentration were prepared with low, medium, and high concentrations of standard substances and were calculated for the precision and accuracy. The precision of quercetin-3-𝑜-gentiobioside and isoquercitrin was confirmed for intra-day and daily. As a result, the intra-day precision was found to be 0.50-1.48% and 0.77-2.87%, and the daily precision to be 0.07-3.37% and 0.58-1.37%, implying an excellent precision at level below 5%. As a result of accuracy measurement, the intra-day accuracy of quercetin-3-𝑜-gentiobioside and isoquercitrin was found to be 104.87-109.64% and the daily accuracy thereof was found to be 106.85-109.06%, reflecting high level of accuracy. The detection limits of quercetin-3-𝑜-gentiobioside and isoquercitrin were 0.24 ㎍/mL and 0.16 ㎍/mL, respectively, whereas the quantitation limits were 0.71 ㎍/mL and 0.49 ㎍/mL, confirming that detection was valid at the low concentrations as well. From the analysis, the established analytical method was proven to be excellent with high level of results from the verification on the specificity, linearity, precision, accuracy, detection limit and quantitation limit thereof. In addition, as a result of analyzing the content of A. esculentus L. Moench powder samples using a validated analytical method, quercetin-3-𝑜-gentiobioside was analyzed to contain 1.49±0.01 mg/dry weight g, while isoquercitrin contained 1.39±0.01 mg/dry weight g. The study was conducted to verify that the simultaneous analysis on quercetin-3-𝑜-gentiobioside and isoquercitrin, the indicators of A. esculentus L. Moench, is a scientifically reliable and suitable analytical method.