• Journal of Digital Convergence
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• v.19 no.11
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• pp.351-357
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• 2021

IoT devices, which are used in various ways in distributed environments, are becoming more important in data transmitted and received from IoT devices as fields of use such as medical, environment, transportation, bio, and public places are diversified. In this paper, as a method to ensure the reliability of IoT data, an autoencoder-based IoT-linked processing technique is proposed to classify and process numerous data by various important attributes. The proposed technique uses correlation indices for each IoT data so that IoT data is grouped and processed by blockchain by characteristics for IoT linkage processing based on autoencoder. The proposed technique expands and operates into a blockchain-based n-layer structure applied to the correlation index to ensure the reliability of IoT data. In addition, the proposed technique can not only select IoT data by applying weights to IoT collection data according to the correlation index of IoT data, but also reduce the cost of verifying the integrity of IoT data in real time. The proposed technique maintains the processing cost of IoT data so that IoT data can be expanded to an n-layer structure.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.222-235
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• 2022

The Government provides a financial assistance to stimulate firm R&D and innovation activities. Previous papers on the impact of public subsidies on firm R&D investments mainly had a focus on an individual policy tool regardless of potential impacts of other policy instruments. This study addresses this gap by examining the effects of policy mix regarding a subsidy and a tax credit. The empirical analyses from fixed effect model using Survey on Technology of SMEs 2015-2017 revealed valuable points. First, policy mix induces more R&D investment of SMEs, which in turn, shows a complementary relationship between two instruments. Second, even if impact of tax credit controlled, subsidy is positively associated with SMEs R&D investment. These findings justify policy mix interventions to promote SME R&D activity. Moreover, grants can be applied as a more useful policy tool for SMEs that are constrained by resources and capabilities.

• Design Convergence Study
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• v.15 no.2
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• pp.347-368
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• 2016

Recently, researches for the word of mouth(WOM) imply that consumers use WOM informations of products in their purchase process. This study suggests methods using opinion mining and visualization to understand consumers' opinion of each goods and each markets. For this study we conduct research that includes developing domain ontology based on reviews confined to "movie" category because people who want to have watching movie refer other's movie reviews recently, and it is analyzed by opinion mining and visualization. It has differences comparing other researches as conducting attribution classification of evaluation factors and comprising verbal dictionary about evaluation factors when we conduct ontology process for analyzing. We want to prove through the result if research method will be valid. Results derived from this study can be largely divided into three. First, This research explains methods of developing domain ontology using keyword extraction and topic modeling. Second, We visualize reviews of each movie to understand overall audiences' opinion about specific movies. Third, We find clusters that consist of products which evaluated similar assessments in accordance with the evaluation results for the product. Case study of this research largely shows three clusters containing 130 movies that are used according to audiences'opinion.

• The Korean Journal of Franchise Management
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• v.9 no.1
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• pp.27-39
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• 2018

Purpose - Recently, the mobile application field has been receiving astronomical attention from the past few years due to the growing number of mobile app downloads and withal due to the revenues being engendered. Especially delivery apps by mobile service market is experiencing rapid growth and competition is intensifying. Therefore, delivery apps' choice attributes has become important as a strategy for survival of franchise firms. Based on previous studies, this research proposed the theoretical framework about the structural relationships among customer satisfaction, trust and revisit intention on delivery apps' choice attributes. Research design, data, and methodology - This study examines the structural relationship between choice attributes of using the delivery app, satisfaction, trust, and revisit intention. More specifically, this study has been examined from the perspective of Generation Y who is enjoying electronic commerce and shopping with mobile phone. In this model, choice attributes of delivery app consists of three sub-dimensions such as service quality, system quality, interaction quality. So as to test the purposes of this study, research model and hypotheses were developed. After excluding 24 invalid respondent questionnaires, 201 valid questionnaires were coded and analyzed using frequency, confirmatory factor analysis, correlations analysis, and structural equation modeling with SPSS 21 and SmartPLS 3.0. Result - The results of the study are as follows. First, service quality and interaction quality had positive effects on satisfaction, and interaction quality had positive effects on trust, but system quality did not have a significant effect on both satisfaction and trust. Second, satisfaction had positive effects on both trust and revisit intention. Third, trust had positive effects on revisit intention. Conclusions - The implications of this study are following as: From the theoretical perspective, this study confirms the effect of delivery apps' choice attributes on satisfaction, trust, and revisit intention. In addition, it is significant that we examined the influence of choice attributes of delivery apps on their attitudes and behaviors of Generation Y familiar with mobile environment. Through this study, we hypothesized that the attributes of service quality and interaction quality of delivery apps have a significant effect on customer satisfaction, and this can be expected to provide meaningful implications for the development of franchise restaurant industry. To encourage continuous repurchase through customer satisfaction, franchise companies need to establish various strategic alliances with delivery app companies and new growth engines by providing diverse and high-quality services to customers in the smart age.

• The Korean Journal of Franchise Management
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• v.9 no.4
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• pp.7-19
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• 2018

Purpose - Recently, global management in Korea franchise industry is becoming an important keyword. As an important branch market, Chinese market plays a major role not only by making experience of the competitiveness among global brands which offers a foothold to become a top global brand, but also by actualizing an economies of scale in production, sales, etc. Therefore, it is necessary to identify key successful factor influencing customer evaluation and responses of Korean franchise restaurant targeting Chinese consumers in China context. The purpose of this study is to examine the effects for Korean franchise restaurant selection attributes on perceived value, customer satisfaction and customer loyalty in Chinese context with SmartPLS 3 and Artifical Neural Network(ANN). Research design, data, and methodology - For these purposes, the authors developed several hypotheses. A questionnaire survey was conducted on the panel of online survey companies for Chinese consumers who have visited Korean franchise restaurants. A total of 404 data were analyzed using structural equation modeling(SEM) and artifical neural network(ANN) with SPSS 22.0 and SmartPLS 3.0. Result - The findings of this study are as follows: First, the alternative model findings show that facilities & atmosphere, employee service, and menu influenced on utilitarian value, customer satisfaction, and customer loyalty directly. Second, employee service influenced on customer satisfaction. Third, menu influenced on hedonic value. Fourth, brand reputation influenced on utilitarian value. Fifth, hedonic value increase customer satisfaction and customer loyalty. Sixth, hedonic value increase customer loyalty. Seventh, customer increase customer loyalty. And, the ANN analysis shows that utilitarian value is the first most important factor influencing customer satisfaction, followed by hedonic value, facilities & atmosphere, menu and employee service. However, the ANN analysis shows that customer satisfaction is the first most important factor influencing customer loyalty, followed by utilitarian value, hedonic value, brand reputation, menu, and employee service. Conclusions - This study provides practical implications for enhancing customer satisfaction and customer loyalty by applying the ANN technique that complements the limitations of the linear structural relationship analysis using the proposed model and the alternative model. In other words, the SEM-ANN model provides guidelines on how Korean franchise restaurants should formulate facilities & atmosphere, employee service, and menu mix strategies in China. In addition, ANN 's analysis shows that restaurant brand reputation plays a pivotal role in increasing customer loyalty. The fact suggests that Korean franchise companies should establish their domestic brand reputation prior to their entry into overseas markets such as China.

• Journal of Leisure Studies
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• v.9 no.2
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• pp.61-83
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• 2011

The purpose of this study was to find out whether or not the disabled students' assistance could be interpreted as a serious leisure. t is academically worth because this study reviewed if college students voluntarism could be considered a leisure and provided fundamental information about the interaction between the volunteered and the disabled. Voluntarism in the college level, in general, is spontaneous. The volunteered are expected to experience a psychological well-being, and their participation in voluntary activity helps them have a chance to develop socially, emotionally, and psychologically. In addition, as Stebbins(2001) claims, a serious leisure could be kept with one's enjoyment and play a role of leisure. Therefore, this study interviewed 6 college students who played a role of a spontaneous assistant. As results, 103 conceptual terms and 13 sub-categories and 5 higher categories were come out. The volunteered had stress at the early stage and experienced a state of being identified with the disabled. They, however, had an opportunity to foster expertise for volunteering, which contributes to their increased enjoyment and consistent volunteering thereafter. Some, even quitted in the middle, showed effort to find an appropriate substitute. It was revealed through this study that college students' assistance to the disabled has attributes for a serious leisure.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of Science Education
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• v.43 no.1
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• pp.157-172
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• 2019

The purpose of this study is to analyze the influence of number of targets on common knowledge generation and brain activity during the common life science discovery task performance. In this study, 35 preliminary life science teachers participated. This study was intentionally made a block designed for EEG recording. EEGs were collected while subjects were performing common discovery tasks. The sLORETA method and the relative power spectrum analysis method were used to analyze the brain activity difference and the role of activated cortical and subcortical regions according to the degree of difficulty of common discovery task. As a result of the study, in the case of the Theta wave, the activity of the Theta wave was significantly decreased in the frontal lobe and increased in the occipital lobe when the difficult difficulty task was compared with the easy difficulty task. In the case of Alpha wave, the activity of Alpha decreased significantly in the frontal lobe when performing difficult task with difficulty. Beta wave activity decreased significantly in the frontal lobe, parietal lobe, and occipital lobe when performing difficult task. Finally, in the case of Gamma wave, activity of Gamma wave decreased in the frontal lobe and activity increased in the parietal lobe and temporal lobe when performing the difficult difficulty task compared to the task of easy difficulty. The level of difficulty of the commonality discovery task is determined by the cingulate gyrus, the cuneus, the lingual gyrus, the posterior cingulate, the precuneus, and the sub-gyral where it was shown to have an impact. Therefore, the difficulty of the commonality discovery task is the process of integrating the visual information extracted from the image and the location information, comparing the attributes of the objects, selecting the necessary information, visual work memory process of the selected information. It can be said to affect the process of perception.

• Journal of Chest Surgery
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• v.39 no.5 s.262
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• pp.359-365
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• 2006

Background: Arterial grafts have been used to achieve better long-term results for coronary revascularization. Bilateral internal thoracic artery (ITA) grafts have a better results, but it may be not used in some situations such as diabetes and chronic obstructive pulmonary disease (COPD). We evaluated the clinical and angiographic results of composite left internal thoracic artery-radial artery (LITA-RA) Y graft. Material and Method: Between April 2002 and September 2004, 119 patients were enrolled in composite Y graft for coronary bypass surgery. The mean age was $62.6{\pm}8.8$ years old and female was 34.5%. Preoperative cardiac risk factors were as follows: hypertension 43.7%, diabetes 33.6%, smoker 41.2%, and hyperlipidemia 22.7%, There were emergency operation (14), cardiogenic shock (6), left ventricle ejection fraction (LVEF) less than 40% (17), and 17 cases of left main disease. Coronary angiography was done in 35 patients before the hospital discharge. Result: The number of distal anastomoses was $3.1{\pm}0.91$ and three patients (2.52%) died during hospital stay. The off-pump coronary artery bypass (OPCAB) was applied to 79 patients (66.4%). The LITA was anastomosed to left anterior descending system except three cases which was to lateral wall. The radial Y grafts were anastomosed to diagonal branches (4), ramus intermedius (21), obtuse marginal branches (109), posterolateral branches (12), and posterior descending coronary artery (8). Postoperative coronary angiography in 35 patients showed excellent patency rates (LITA 100%, and RA 88.5%; 3 RA grafts which anastomosed to coronary arteries <70% stenosed showed string sign with competitive flow). Conclusion: The LITA-RA Y composite graft provided good early clinical and angiographic results in multivessel coronary revascularization. But it should be cautiously used in selected patients.