• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.214-223
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• 2019

This study is a meta-analysis based on results of empirical studies related to work-life balance(WLB), and the relationships between WLB and other variables. In order to achieve this objective, articles published in domestic journals prior to December 2018 were collected. Data was collected using an online database provided by the Korea Educational and Scientific Information Service, and a total of 27 studies and 126 sub data were coded. Data was analyzed using CMA (comprehensive meta-analysis) 3.0 program. Results of this study are as follows. First, the overall mean effect size of WLB was 0.365, indicating a small effect size. Second, the effect sizes of dependent variables influenced by WLB included immersion, innovation, and performance in order. Third, the effect size of organizational focus variables was more than twice as big as that of individual focus variables. Fourth, the negative theoretical background dependent variables of WLB, such as sacrifice, job stress, and turnover showed -0.254 effect size, and the positive theoretical background dependent variables, such as job satisfaction and emotional commitment have mid-size effect (0.576). Fifth, the effect size of independent variables were in the order of work-development balance, work-home balance, and work-leisure balance.

• Tribology and Lubricants
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• v.34 no.6
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• pp.241-246
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• 2018

This research addresses the improvement of tribo-systems, specifically regarding the reduction of friction and wear through tribo-coupling between tetrahedral amorphous carbon (ta-C) with different types of counterpart materials, namely bearing steel (SUJ2), tungsten carbide (WC), stainless steel (SUS304), and alumina ($Al_2O_3$). A second variable in this project is the utilization of different values of duct bias voltage in the deposition of the ta-C coating - 0, 5, 10, 15, and 20 V. The results of this research are expected to determine the optimum duct bias and best counter materials associated with ta-C to produce the lowest friction and wear. Results obtained reveal that the tribo-couple between the ta-C coating and SUJ2 balls produces the lowest friction coefficient and wear rate. In terms of duct bias changes, deposition using 5 V produces the most optimum tribological behavior with lowest friction and wear on the tribo-system. In contrast, the tribo-couple between ta-C with a WC ball causes penetration through the coating surface layer and hence high surface delamination. This study demonstrates that the most effective ta-C coating duct bias is 5 V associated with SUJ2 counter material to produce the lowest friction and wear.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.4
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• pp.445-451
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• 2008

The purpose of this study was carried out to examine the effects of onion kimchi extract supplementation on blood glucose level and serum lipid components in streptozotocin (STZ)-induced diabetic rats for 4 weeks. STZ was administered as a single dose (50 mg/kg BW) to induce diabetes, and the diabetic rats were divided into eight groups (normal, diabetic control, and six treatment groups). The dose of onion kimchi extract 100 (OK-100), 200 (OK-200), and 400 (OK-400) mg/kg/day or quercetin as a main compound of onion 5 (Q-5), 10 (Q-10), and 20 (Q-20) mg/kg/day were orally administered daily to STZ-induced diabetic rats for 4 weeks after STZ injection. The diabetic control rats (465.6 mg/dL) showed significantly higher blood glucose level than the normal rats (76.3 mg/dL) after 4 weeks, but was significantly reduced with onion kimchi extract and quercetin supplementation (p<0.001). Changes in body weight, kidney weight and urine volume were not significantly different in diabetic control rats, and in onion kimchi extract and quercetin treated rats. The serum total cholesterol levels of control were significantly decreased in onion kimchi extract and quercetin supplementation groups, respectively (p<0.001). The blood urea nitrogen level and urinary protein excretion in diabetic rats were not significant different among the groups. These results suggest that onion kimchi extract supplementation in STZ-induced diabetic rats may be a very important factor for the reduction of blood glucose and serum cholesterol profiles.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.157-175
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• 2022

At this moment, a paradigm shift is taking place across all sectors of society for the transition movements to the digital economy. Various movements are taking place in the global agricultural industry to achieve innovative growth using big data which is a key resource of the 4th industrial revolution. Although the government is making various attempts to promote the use of big data, the movement of the agricultural industry as a key player in the use of big data, is still insufficient. Therefore, in this study, effects of performance expectations, effort expectations, social impact, facilitation conditions, based on the Unified Theory of Acceptance and Use of Technology(UTAUT), and innovation tendencies on the acceptance intention of big data were analyzed using the economic and practical benefits that can be obtained from the use of big data for agricultural-related companies as moderating variables. 333 questionnaires collected from agricultural-related companies were used for empirical analysis. The analysis results using SPSS v22.0 and Process macro v3.4 were found to have a significant positive (+) effect on the intention to accept big data by effort expectations, social impact, facilitation conditions, and innovation tendencies. However, it was found that the effect of performance expectations on acceptance intention was insignificant, with social impact having the greatest influence on acceptance intention and innovation tendency the least. Moderating effects of economic benefit and practical benefit between effort expectation and acceptance intention, moderating effect of practical benefit between social impact and acceptance intention, and moderating effect of economic benefit and practical benefit between facilitation condition and acceptance intention were found to be significant. On the other hand, it was found that economic benefits and practical benefits did not moderate the magnitude of the influence of performance expectations and innovation tendency on acceptance intention. These results suggest the following implications. First, in order to promote the use of big data by companies, the government needs to establish a policy to support the use of big data tailored to companies. Significant results can only be achieved when corporate members form a correct understanding and consensus on the use of big data. Second, it is necessary to establish and implement a platform specialized for agricultural data which can support standardized linkage between diverse agricultural big data, and support for a unified path for data access. Building such a platform will be able to advance the industry by forming an independent cooperative relationship between companies. Finally, the limitations of this study and follow-up tasks are presented.

• Informatization Policy
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• v.25 no.3
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• pp.3-28
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• 2018

The purpose of this study is to analyze the research trends and present future research directions in the field of Cyber Physical System (CPS), a key element in the 4th Industrial Revolution, Industry 4.0, and Smart Manufacturing that are currently promoted as important innovation agenda both at home and abroad. In this study, (1) the concepts of industry 4.0, smart manufacturing and CPS are summarized; (2) analysis criteria of these fields are established; and 3) analysis results are presented and future research direction is proposed. 74 overseas and 8 domestic literature on manufacturing CPS from 2013 to 2017 are identified through 'Google Scholar Search'. Major results of the analysis are summarized as follows: (1) research on a common methodology and framework for the manufacturing CPS needs to be done based on the analysis of the existing methodologies and frameworks of various perspectives; (2) in order to improve the maturity of the manufacturing CPS, it is necessary to study actual deployment and operations of CPS, including the existing systems; (3) it is necessary to study the diagnostic methodology that can evaluate manufacturing CPS and suggest improvement strategy; and (4) as for the detailed model and tool, it is necessary to reinforce research on SCM production planning and human-machine collaboration while considering the characteristics of CPS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2290-2299
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• 2009

This study aims to analyze the importances of various SCM adoption factors suggested in precedent researches with AHP. SCM adoption factors were categorized by four types: organization factor, transaction factor, relation factor, and information factor. Each factor has sub-factors. Organization factor has five sub-factors: adoption strategy, support of CEO, maturity of information technology, development of assessment system, and innovation leadership. Transaction factor has three sub-factors: transaction period, delivery/quality, and shared goal. Relation factor has five sub-factors: trust, collaboration, inter-dependence, conflict, and immersion. Information factor has three sub-factors: information quality, information share, and information exchange. There are sixteen sub-factors altogether. Analyzing the importances of SCM adoption factors with AHP, the importance of organization factor(.387) ranked the highest. Relation factor(.291), information factor(.167), and transaction factor(.155) followed. Putting the analysis results of primary hierarchy factors and secondary hierarchy factors together, support of CEO(.169) ranked the highest and trust(.124), adoption strateg (.089), share goal(.081), information exchange(.069), collaboration(.064), and information share (.057) followed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.110-124
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• 2019

In recent years, over-supply of hospitals has made hospital management more difficult and hospitals have introduced innovative hospital management to enhance customer satisfaction. The purpose of this study is to investigate the effect of innovativeness of hospitals on customer satisfaction by using mediating effects of hospital capacity and location strategy. The subjects of this study were selected from 120 patients and caregivers who were hospitalized in Seoul and Metropolitan area hospitals and conducted questionnaire and statistical analysis. The results of this study are as follows: Firstly, this paper shows hospital Innovativeness does not significantly affect customer satisfaction. Unlike private companies, it is urgent to develop innovativeness measuring tools that are unique to hospitals differentiated from those of general companies. Secondly, although the impact of Innovativeness on hospital internal competency and location strategy was similar, location strategy(${\beta}=0.357$) had a greater impact on customer satisfaction than internal competency(${\beta}=0.283$). This suggests that the medical institution should take precedence over the detailed preparations based on its location marketability, traffic infrastructure, building sales and medical concentration from the time of its opening. Thirdly, this paper confirms through empirical analysis that the relationship between hospital Innovativeness and customer satisfaction is completely mediated by internal capacity and location strategy. The hospital's Innovativeness is affecting customer satisfaction by enhancing the hospital's internal competencies and inducing an active attitude toward establishing a location strategy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.1832-1843
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• 2015

Recently, since the "Capital Market Consolidation Act" has been effective in 2009, the competition among the securities companies in Korean security market has been fierce. Thus, securities business lately are needed by the market environmental requirements rapidly changed for various strategies. The purpose of this study was to identify the effect of corporate attribute, employee attribute, financial product attribute securities on customer satisfaction, trust with the firm, and usage intention. As the subjects I selected the customer of securities in Seoul in 2014 and conducted survey with questionnaires. Among total 400, I chose 378 as the valid sample by convenience sampling. For the data process, I used SPSS 20.0 I verified the perspective hypotheses after testing reliability and validity of fit by the data process. The results are as following. First, it was shown that the sub-factors of corporate attribute, employee attribute, financial product attribute in securities as ethics, innovation, size, kind, professionalism, ethics, profitability and diversity had significant effect on usage intention. Second, the study confirms that reliability and satisfaction influences customer loyalty as moderate variable. The industrial and academic significance of this study is that it may serve as a useful base date to understand customer behavior and draw new strategies in a financial management environment.

• Informatization Policy
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• v.30 no.4
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• pp.40-61
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• 2023

The adoption of smart factories and smart manufacturing as strategies to enhance competitiveness and stimulate growth in the manufacturing sector is vital for a country's future competitiveness and industrial transformation. The government has consistently pursued smart manufacturing innovation policies starting with the Manufacturing Innovation 3.0 strategy in the Ministry of Industry. This study aims to identify policy areas for smart factories and smart manufacturing based on technical standards. Analyzing policy areas at the current stage where the establishment and support of domestic standards aligning with international technical standards are required is crucial. By prioritizing smart manufacturing process areas within the industry, policymakers can make well-informed decisions to advance smart manufacturing without blindly following international standardization in already well-established areas. To achieve this, the study utilizes a hierarchical analysis method including expert interviews and importance-performance analysis for the five major process areas. The findings underscore the importance of proactive participation in standardization for emerging technologies, such as data and security, instead of solely focusing on areas with extensive international standardization. Additionally, policymakers need to consider carbon emissions, energy costs, and global environmental challenges to address international trends in export and digital trade effectively.