• Journal of Environmental Health Sciences
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• v.43 no.4
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• pp.334-348
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• 2017

Objectives: We investigated the impacts of indoor plants on indoor air quality and occupational health, focusing on allergic rhinconjunctivitis and stress among employees in new office buildings. Methods: A total of 34 employees working at new public office buildings were enrolled as subjects (n=17, with indoor plants) and as a control (n=17) group. Before and after introducing indoor plants for three months, indoor air quality measurements including VOCs and aldehydes and questionnaires on sick building syndrome, AR symptoms (ARIA based), stress (DASS 42, KOSS, and SACL), and indoor characteristics were performed and statistically analysed. Results: Among the 34 enrolled subjects, 19 were included in the probable AR subject group (subjects with indoor plants, n=8, control n=11) and completed all questionnaires. Statistical analyses were done for total, AR subject groups, and controls. As a result, it was confirmed that major indoor air pollutants decreased after the introduction of indoor plants (p<0.5). Among major symptoms of allergic rhinoconjunctivitis, watery rhinorrhea, nasal stuffiness, and nasal itching indexes decreased (p<0.5, respectively). A decrease was noted in some areas of work-related stress indexes (mainly KOSS) among the subject group (total and AR) and a decrease of indoor environmental attractiveness among the control group (total and AR) (p<0.5, for all). Conclusions: Indoor plants may help reduce indoor air pollutants and decrease AR symptoms and work-related stress of employees in newly built office buildings. Various further follow-up studies on the mechanism of environmental, physical, and emotional influences and utilization of indoor plants in association with allergic diseases will be needed.

• The Journal of Engineering Geology
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• v.24 no.3
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• pp.431-440
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• 2014

Since the establishment of the Groundwater Act in Korea in 1993, the national policy on groundwater has focused on the preservation and management of groundwater, which should be used only as a subsidiary water resource. However, population growth, increased water demand, climate change, and the need for uniform water distribution have brought changes to groundwater policy, and have led to the prioritization of development projects such as groundwater dams and river bank filtration. Population growth, changes to the water environment, and increased water risks have all played a role in triggering rapid growth within the water industry; the size of the investment in water resources will also continue to increase worldwide. Until now, private wells and bottled mineral water have led the groundwater industry in South Korea. However, a new area of the groundwater industry, which includes the health and medical sciences, employs groundwater properties derived from regional geology, and is growing. This requires the advancement of groundwater research and technical development connected with ICT (Information and Communication Technology) and medical science, and that the public development of groundwater and its various applications is expanded through locating groundwater in the core of the water industry cluster.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.19-24
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• 2020

WHO declared a global pandemic on March 11th for Corona 19. However, there is a situation where you have to go to building for face-to-face education or seminars for economic and social activities. The first check method of COVID-19 infection is to measure body temperature, so the primary entrance and exit is blocked for near-field body temperature measurement. However, since it is troublesome to check directly, thermal camera is installed at the entrance of the building, and body temperature is measured indirectly using the infrared camera to control access. In case of middle and high schools, universities, and lifelong education center, we need a system that is possible to interoperate with attendance checks and automatically recognizes whether to wear masks and can authenticate students. We proposed the system that is to confirm whether to wear a mask with a camera that is embedded in a smart mirror, and that authenticates the user through voice recognition of the user who wants to enter the building by using voice recognition technology and determines whether to enter them or not. The proposed system can check attendance if it is linked with near-field temperature measurement and attendance check APP of student's smart phone.

• Journal of Distribution Science
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• v.15 no.6
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• pp.65-76
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• 2017

Purpose - The quality of customer service has been importantly considered as a way of retaining current customers. Recent development of service industry which based on Information & Communication Technology allows firms to utilize different employees for their businesses. Although it is regarded as important to consider emotional labor of employees working for customers in ICT service industry, little was known the role of emotional dissonance. Thus, current paper focused on emotional labor and tried to identify the factors which influence on employees' intrinsic motivation for married women working in call centers. This study highlighted the influence of the emotional dissonance on the employees' intrinsic motivation, and the moderating influences of work-family conflict on the relationship between emotional dissonance and intrinsic motivation. Research design, data, and methodology - The research samples were gathered from seven call centers of Korean financial institutions located in South Korea. The model of emotional dissonance was developed, which emphasizes the influence of emotional dissonance as a predictor on intrinsic motivation, and then the other model was also introduced to explain how employees' intrinsic motivation were aggravated by work-family conflict. To examine these research models, samples were collected from 468 married women working in call centers of Korean financial institutions located in Seoul. A total of 468 samples were used in the analysis after deleting data of missing value. SPSS 22.0 were utilized for data analysis. Results - The results of current study showed that emotional dissonance is negatively related to intrinsic motivation, and there are significant differences in work-family conflict. Those results generally support the proposed hypotheses. Conclusions - These results suggest that the relationship of intrinsic motivation of married women working in call center for customers' service were influenced by emotional dissonance, which outcomes were interacted not by face-to-face contact with their customers, but by emotional contacts. Managerially, these findings suggest the one who emphasize the quality of customer's service of call center need to introduce the programs for minimizing both of emotional dissonance and work-family conflict. These findings also suggest that the service quality via intrinsic motivation of married women working in call center is hard to be accomplished without considering the factors of emotional dissonance and work-family conflict.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.73-86
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• 2021

The primary purpose of this paper is to explore the advanced models that predict entrepreneurial innovativeness most accurately. For the first time in the field of entrepreneurship research, it presents a model that predicts entrepreneurial innovativeness based on machine learning corresponding to data scientific approaches. It uses 22,099 the Global Entrepreneurship Monitor (GEM) data from 62 countries to build predictive models. Based on the data set consisting of 27 explanatory variables, it builds predictive models that are traditional statistical methods such as multiple regression analysis and machine learning models such as regression tree, random forest, XG boost, and artificial neural networks. Then, it compares the performance of each model. It uses indicators such as root mean square error (RMSE), mean analysis error (MAE) and correlation to evaluate the performance of the model. The analysis of result is that all five machine learning models perform better than traditional methods, while the best predictive performance model was XG boost. In predicting it through XG boost, the variables with high contribution are entrepreneurial opportunities and cross-term variables of market expansion, which indicates that the type of entrepreneur who wants to acquire opportunities in new markets exhibits high innovativeness.

• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.67-78
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• 2022

Interest in bio-signal monitoring of wearable devices is increasing significantly as the next generation needs to develop new devices to dominate the global market of the information and communication technology industry. Accordingly, this research developed a resistive textile strain sensor through a wetting process in a single-wall carbon nanotube dispersion solution using an E-Band with low hysteresis. To measure the resistance signal in the E-Band to which electrical conductivity is applied, a universal material tester, an Arduino, and LCR meters that are microcontroller units were used to measure the resistance change according to the tensile change. To effectively handle various noises generated due to the characteristics of the fabric textile strain sensor, the filter performance of the sensor was evaluated using the moving average filter, Savitsky-Golay filter, and intermediate filters of signal processing. As a result, the reliability of the filtering result of the moving average filter was at least 89.82% with a maximum of 97.87%, and moving average filtering was suitable as the noise filtering method of the textile strain sensor.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.73-82
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• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• Management & Information Systems Review
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• v.38 no.1
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• pp.23-41
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• 2019

Smart Factory is different from existing factory automation in that it aims to produce personalized products with minimum time and cost through ICT. However, previous researches, not from consumers but from product suppliers, have focused on technology trends and technology application methods. In order for Smart Factory to be successful, it must go beyond supplier-focus to meet the needs of consumers. In this study, we surveyed the purchase intention of the personalized product manufactured by smart factory. Influencing factors of purchase intention were drawn as consumers' need for uniqueness, innovativeness, need for touch, and privacy concern, based on previous research. As results of data analysis, it was confirmed that respondents were willing to purchase personalized products, and that consumers' need for uniqueness, innovativeness, and need for touch had a significant impact on purchase intention of personalized products. Our findings can be summarized as follows. First, Consumers' need for uniqueness was found to have positive effects(${\beta}=0.168$) on purchase intention of personalized products. The desire to differentiate themselves from others will be reflected in their personalized products. Therefore, consumers with a higher desire for uniqueness tend to be more willing to purchase personalized products. Second, consumer innovativeness was found to have positive effects(${\beta}=0.233$) on purchase intention of personalized products. Personalized shoes suggested in this study is a new type of personalized product that is manufactured by the latest information and communication technologies such as multi-function robots and 3D printing. Therefore, consumers seeking innovative new experiences are more willing to purchase personalized products. Third, need for touch was found to have positive effects(${\beta}=0.299$) on purchase intention of personalized products. In a smart factory environment, prosuming participation is given to consumers. If consumers participate in the product development process and reflect their requirements on the product, they are expected to increase their purchase intention by virtually satisfying the need for touch. Fourth, privacy concern was found to have no significantly related to purchase intention of personalized products. This is interpreted as a willingness to tolerate the risk of exposing personal information such as home address, telephone number, body size, and preference for consumers who feel highly useful in personalized products.

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

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

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.251-259
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• 2018

BACKGROUND: Various culture media have been used for hydroponic cultures of horticultural plants under the smart greenhouses with natural and artificial light types. Management of the culture medium for the control of medium amounts and/or necessary components absorbed by plants during the cultivation period is performed with ICT (Information and Communication Technology) and/or IoT (Internet of Things) in a smart farm system. This study was conducted to develop the cloud-based data analysis system for effective management of culture medium applying to hydroponic culture and plant growth in smart greenhouses. METHODS AND RESULTS: Conventional inorganic Yamazaki and organic media derived from agricultural byproducts such as a immature fruit, leaf, or stem were used for hydroponic culture media. Component changes of the solutions according to the growth stage were monitored and plant growth was observed. Red and green lettuce seedlings (Lactuca sativa L.) which developed 2~3 true leaves were considered as plant materials. The seedlings were hydroponically grown in the smart greenhouse with fluorescent and light-emitting diodes (LEDs) lights of $150{\mu}mol/m^2/s$ light intensity for 35 days. Growth data of the seedlings were classified and stored to develop the relational database in the virtual machine which was generated from an open stack cloud system on the base of growth parameter. Relation of the plant growth and nutrient absorption pattern of 9 inorganic components inside the media during the cultivation period was investigated. The stored data associated with component changes and growth parameters were visualized on the web through the web framework and Node JS. CONCLUSION: Time-series changes of inorganic components in the culture media were observed. The increases of the unfolded leaves or fresh weight of the seedlings were mainly dependent on the macroelements such as a $NO_3-N$, and affected by the different inorganic and organic media. Though the data analysis system was developed, actual measurement data were offered by using the user smart device, and analysis and comparison of the data were visualized graphically in time series based on the cloud database. Agricultural management in data visualization and/or plant growth can be implemented by the data analysis system under whole agricultural sites regardless of various culture environmental changes.