• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.89-100
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• 2018

The precedent study stage of defense programs is a project stage that is conducted to support the determination of an efficient acquisition method of the weapon system determined by the requirement. In this study, the FTA/FMEA technique was used in the safety analysis process to identify elements to be conducted in the precedent study stage and a methodology for deriving the key review elements through conceptualization and tailoring was suggested. To supplement the key elements derived from the existing research, it is necessary to analyze various events that may arise from key elements. To accomplish this, the HAZOP technique for safety analysis in other industrial fields was used to supplement the results of kdy element derivation. We analyzed and modeled the execution procedure by establishing input/output information and association with the key elements of the precedent study stage derived by linking HAZOP/FTA/FMEA techniques. In addition, performance maturity was evaluated for performance of precedent study, and a risk-based response manual was generated based on inter-working information with key elements with low maturity. Based on the results of this study, it is possible to meet the performance, cost, and schedule of the project implementation through application of the key elements and procedures and the risk management response manual in the precedent study stage of the defense program.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.457-467
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• 2010

The purpose of this study is to develop New-IT internship and to search for the way to reduce quality mismatch and unemployment ratio and to ultimately enhance its effectiveness of university-industry collaboration(UIC) in the field of information technology in Korea. To achieve the goal of this study, we tried to come up with more job creation than educational UIC. The survey(based on CIPP model) based on the reaction of companies and interns participating in IT internship program promoted by MKE(Ministry of Knowledge and Economy) shows that intern experience helped them to get jobs and longer intern period gave them to find job more easily. This program is designed to focus on intern matching between students' major and their intern jobs, and requires new employees' level of job quality. They(56%) preferred to hire local college students majoring in special technology area related to regional innovation industry cluster. It also found that intern companies(87%) wanted to participate in this program again and hired intern students(61%) as showing the possible connection of internship and employment. IT Internship program affected students(68.3%) good images about small and medium enterprises(SME) after finishing internship.

• Journal of the Mineralogical Society of Korea
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• v.23 no.4
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• pp.377-393
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• 2010

Pumiceous tuffs occurring in the Beomgockri Group are examined applied-mineralogical characteristics and their controling factors to evaluate their potentials as the adsorption-functional mineral resources. The pumiceous tuffs are diagenetically altered to low-grade zeolitcs and bentonites in the Janggi area. Compositional specialty due to the presence of pumice fragments induces the altered tuffs to exhibit the characteristic adsorption property combined with cation exchange capacity, specific surface area, and acidic pH. Unusual lower pH in the adsorption-functional mineral substances is turned out to be originated from the presence of H-smectite having $H^+$ in the interlayer site of the sheet structure. On account of disordered crystallinity resulting from the exchanged $H^+$ in the interlayer site, the smectite commonly forms crenulated edges in the planar crystal form and exhibits characteristic X-ray diffraction patterns showing comparatively lower intensities of basal spacings including (001) peak than conventional Ca-smectite. Based on the interpretation of paragenetic relations and precursor of the H-smectite, a genetic model of the peculiar clay mineral was proposed. The smectite formation may be facilitated resulting from the precipitation of opal-CT at decreasing pH condition caused by the release of H+ during diagenetic alteration of pumice fragments. Because of the acidic smectite, the low-grade mineral resources from the Beomgockri Group may be applicable to the adsorption industry as the raw materials of acid clays and bed-soil.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.215-228
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• 2021

The purpose of this qualitative study was to identify cultural factors among older Americans that could influence them to accept new welfare technologies. This study also explored how social and cultural-based plans could increase the acceptability of welfare technologies for improving the quality of life of older adults in the future. In-depth interviews were conducted with ten professionals who work with older adults. The collected interview data were subsequently analyzed using a two-cycle open coding process. The data analysis generated 29 codes that were organized into 7 primary codes, or categories, and 22 secondary codes nested within the primary codes. Several themes were identified: individualism, family-oriented culture, pragmatism, low-context culture, privacy, fun-seeking culture, and a less hierarchical culture. These findings will inform the development of a future survey to examine the relationship between older adults' intentions when using technology and socio-cultural factors in community settings. In order to explore the different impact levels of the cultural factors found in this study, the future study will need to include measures for identifying socio-cultural variations among individuals in one country or across countries.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.135-155
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• 2022

This study analyzes patent trends by deriving sub-technical fields of Organic Light-Emitting Diodes (OLEDs) industry, and analyzing technology value, originality, and diversity for each sub-technical field. To collect patent data, a set of international patent classification(IPC) codes related to OLED technology was defined, and OLED-related patents applied from 2005 to 2017 were collected using a set of IPC codes. Then, a large number of collected patent documents were classified into 12 major technologies using the Latent Dirichlet Allocation(LDA) topic model and trends for each technology were investigated. Patents related to touch sensor, module, image processing, and circuit driving showed an increasing trend, but virtual reality and user interface recently decreased, and thin film transistor, fingerprint recognition, and optical film showed a continuous trend. To compare the technological value, the number of forward citations, originality, and diversity of patents included in each technology group were investigated. From the results, image processing, user interface(UI) and user experience(UX), module, and adhesive technology with high number of forward citations, originality and diversity showed relatively high technological value. The results provide useful information in the process of establishing a company's technology strategy.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.285-292
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• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• Journal of Korean Society of Forest Science
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• v.113 no.2
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• pp.170-186
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• 2024

This study was conducted to identify the usage characteristics of the DMZ Punchbowl Trail (DPT) as a national forest trail (NFT) and to estimate its ripple effects on the local economy. The objective of this study is to provide policy implications for sustainable operational management. Out of the 500 questionnaires distributed, 215 respondents provided their complete travel itineraries and expenditures. The respondents, mainly aged 50 and above and residing in the Seoul Metropolitan Area, spend 3.5 hours of travel time to the DPT. Together with their families, the respondents typically spend approximately 4 hours for leisurely activities, primarily appreciation of scenic views and relaxation by visiting the "O-yubatgil." Furthermore, they extend their travels to other parts of Gangwon Province, where the DPT is situated. Within Gangwon Province, Yanggu County is the most visited destination. The respondents reported a notably higher average expenditure per visitor compared with the typical local walking tourists. Estimates show that the DPT generates an annual average of KRW 2.1 billion in direct expenditure (based on an average of 10,000 visitors for over five years), KRW 2.8 billion in production, and KRW 1.3 billion in added value, and it has created 40 jobs in Gangwon Province. The results of this study lies in empirically determining the specific economic scale and ripple effects of DPT as an NFT in the major sector, which occupies a significant portion of the Gangwon Province's local economy. The results will be instrumental in validating NFT policies and informing policy making for sustainable forest utilization.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.603-614
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• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.41-61
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• 2023

The universal form of life in the era of the 4th industrial revolution can probably be summarized as the keyword "non-face-to-face". In particular, in terms of consumption activities, face-to-face contact is gradually changing to a system that minimizes, and offline stores are rapidly changing to non-contact services through kiosks and robots. The social structure is also changing with the passage of time, and most fundamentally, our dietary consumption patterns are changing. In particular, the increase in single-person households and the aging population are having a great impact on changes in the food service industry, which is closely related to dietary life. The HMR (Home Meal Replacement) market has grown significantly as the labor of cooking at home has decreased and the use of substitute foods has increased. As the size of the market has grown, the types of businesses that provide products have also diversified. The development of technology, non-face-to-face culture, and corporate management efficiency are intertwined, and unmanned stores are spreading recently. In this study, service quality attributes of HMR unmanned stores, where competition is gradually intensifying, are classified, and service quality classification using the Kano model and Timko's customer satisfaction coefficient are calculated to provide implications for service management based on customer satisfaction. As a result of the analysis, 'products with short cooking time' and 'variety of products (menu)' were classified as attractive qualities, and 'cleanliness inside/outside of the store' and 'products at reasonable prices' were classified as unified quality. In addition, 'convenience of self-checkout process' was classified as a natural quality, and 'convenience of in-store passage' was classified as an indifferent quality. Furthermore, when the service factor was satisfied within the HMR unmanned store, the factor with the highest satisfaction coefficient was 'product (menu) variety', and the factor with the highest dissatisfaction factor was 'convenience of self-checkout process'. Through the results of this study, it is intended to derive priorities in service quality management of HMR unmanned stores and provide strategic implications for related businesses.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.