• Journal of Intelligence and Information Systems
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• 제27권1호
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• Journal of Environmental Impact Assessment
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• 제30권2호
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• pp.105-116
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• 2021

In the city of high population density crowded with buildings, Urban Heat Island (UHI) is intensified, and the city is vulnerable to thermal comfort. The maintenance of vacant land in downtown is treated as a factor that undermines the residential environment, spoils the urban landscape, and decreases the economic vitality of the whole region. Therefore, this study compared the effects on microclimate in the surrounding area according to the development scenarios targeting the vacant land in Songhyeon-dong, Jongno-gu, Seoul. The status quo, green oriented, building oriented and green-building mediation scenarios were established and ENVI-met was used to compare and analyze the impact of changes in wind speed, air temperature and mean radiant temperature (MRT) within 1 km of the target and the target site. The result of inside and 1 km radius the targeted area showed that the seasonal average temperature decreased and the wind speed increased when the green oriented scenario was compared with the current state one. It was expected that the temperature lowered to -0.73 ℃ or increased to 1.5 ℃ in summer, and the wind speed was affected up to 210 meters depending on the scenario. And it was revealed that green area inside the site generally affects inside area, but the layout and size of the buildings affect either internal and external area. This study is expected to help as a decision-making support tool for developing Songhyeon-dong area and to be used to reflect the part related to microclimate on the future environmental effects evaluation system.

• Research in Plant Disease
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• 제28권3호
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• pp.113-121
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• 2022

Rice blast is a major plant disease that occurs worldwide and significantly reduces rice yields. Rice blast disease occurs periodically in Korea, causing significant socio-economic damage due to the unique status of rice as a major staple crop. A disease outbreak prediction system is required for preventing rice blast disease. Epidemiological investigations of disease outbreaks can aid in decision-making for plant disease management. Currently, plant disease prediction and epidemiological investigations are mainly based on quantitatively measurable, structured data such as crop growth and damage, weather, and other environmental factors. On the other hand, text data related to the occurrence of plant diseases are accumulated along with the structured data. However, epidemiological investigations using these unstructured data have not been conducted. The useful information extracted using unstructured data can be used for more effective plant disease management. This study analyzed news articles related to the rice blast disease through text mining to investigate the years and provinces where rice blast disease occurred most in Korea. Moreover, the average temperature, total precipitation, sunshine hours, and supplied rice varieties in the regions were also analyzed. Through these data, it was estimated that the primary causes of the nationwide outbreak in 2020 and the major outbreak in Jeonbuk region in 2021 were meteorological factors. These results obtained through text mining can be combined with deep learning technology to be used as a tool to investigate the epidemiology of rice blast disease in the future.

• Journal of agricultural medicine and community health
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• 제47권4호
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• pp.211-219
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• 2022

Objective: This study was conducted to investigate the awareness of work-related disease prevention of farmers. Method: As a research method, a qualitative focus group interview was conducted in 18 participants. Results: Prevention and management services for work-related diseases of farmers mostly are based on research from other fields and so are not highly effective because their content is not relevant to agricultural work. It has been suggested that such program designers be required to have some appropriate related knowledge, and that incentives and a certification system for participation in such education be established. To analyze work-related diseases of farmers, fields of prevention, diagnosis, treatment, and rehabilitation should be created. They demanded the designation of hospitals and the actualization of compensation for farmers' safety insurance. The work-related diseases to address were include musculoskeletal diseases, pesticide poisoning-related diseases (cardiovascular disease, respiratory disease), psychiatric diseases such as depression, and allergic diseases. However, this must have been the result of the harmful factors they felt during agricultural work. And for farmer patients diagnosed with work-related diseases, it was said to strengthen farmer safety insurance. Conclusion: In order to increase the safely and health effects of agricultural work, it is necessary to prevent and manage work-related diseases of farmers. Projects should be developed in consideration of cultural and economic barriers of farmers and the characteristics of the work.

• Journal of the Korean Institute of Landscape Architecture
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• 제50권1호
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• pp.1-19
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• 2022

The Geumgang Pine (Pinus densiflora) Forest in Sogwang-ri, Uljin has traditionally been a pine tree protection area (prohibited forest) for timber production purposes, and is now designated and managed as a protected area for forest genetic resource conservation by the Korea Forest Service. This study, we analyzed topographical characteristics, existing vegetation, tree age, and plant community structure, and proposed a sustainable management method for the Geumgang Pine (Pinus densiflora) Forest in Sogwang-ri, Uljin for timber havesting purposes. The topographical characteristics of the target area were 36.7% ridges and 38.7% valleys; the ratio of ridges to valleys was similar, and the slopes formed 24.7% of the total area. The types of pine forest communities are divided into six types based on the progress of pine forest renewal, the competition with other species such as deciduous broadleaf trees, and the formation of layered structures. It has been confirmed that the age of the large-diameter pine trees (40~60cm in diameter) is approximately 60~70 years, which is relatively low. As a result of the analysis of the relative importance percentage and layered structure, differences depended on the progress of the pine forest renewal project, and not only the maintenance of the pine forest, but also the creation of a secondary growth forest, the density adjustment of pine trees, and the active management of competitive trees. The average basal area by the community was 12,642.1~25,424.4cm² for the tree layer and 1.8~1,956.5cm² for the low tree layer based on a quadrat of 400m². The difference in the basal area appeared to depend on the size and number of trees forming the tree layer and the degree of pine forest renewal (the degree of time elapsed after thinning pine trees). The average number of species that appeared in each community was 8.7-20.3; there were many species located in valleys, and the type competes with deciduous broadleaf trees due to the lack of management. The diversity of species ranged from 0.6915-1.0942, and was evaluated as low compared to pine communities in central temperate zones. In this paper, we determined the management goals of Geumgang Pine (Pinus densiflora) Forest in Sogwang-ri, Uljin to produce timber with high economic value, and suggested efficient vegetation management for continuous afforestation, the establishment of a timber production system, and improvement of wood production as a management direction.

• Journal of Intelligence and Information Systems
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• 제28권3호
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• pp.237-258
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• 2022

As the global market environment changes, the domestic ICT industry has a growing influence on the world economy. This industry is regarded as an important driving force in the national economy from a technological and social point of view. In particular, small and medium-sized enterprises (SMEs) in the ICT industry are regarded as essential actors of domestic economic development in terms of company diversity, technology development and job creation. However, since it is small compared to large-sized enterprises, it is difficult for SMEs to survive with a differentiated strategy in an incomplete and rapidly changing environment. Therefore, SMEs must make a lot of efforts to improve their own capabilities, and the government needs to provide the desirable help suitable for corporate internal resources so that they can continue to be competitive. This study classifies the types of ICT SMEs participating in government support programs, and analyzes the relationship between resources and performance creation of each type. The data from the "ICT Small and Medium Enterprises Survey" conducted annually by the Ministry of Science and ICT was used. In the first stage, ICT SMEs were clustered based on common factors according to their experiences with government support programs. Three clusters were meaningfully classified, and each cluster was named "active participation type," "initial support type," and "soloist type." As a second step, this study compared the characteristics of each cluster through profiling analysis for each cluster. The third step carried out in this study was to find out the mechanism of R&D performance creation for each cluster through regression analysis. Different factors affected performance creation for each cluster, and the magnitude of the influence was also different. Specifically, for "active participation type", "current manpower", "technology competitiveness", and "R&D investment in the previous year" were found to be important factors in creating R&D performance. "Initial support type" was identified as "whether or not a dedicated R&D organization exists", "R&D investment amount in the previous year", "Ratio of sales to large companies", and "Ratio of vendors supplied to large companies" contributed to the performance. Lastly, in the case of "soloist type", "current workforce" and "future recruitment plan", "technological competitiveness", "R&D investment", "large company sales ratio", and "overseas sales ratio" showed a significant relationship with the performance. This study has practical implications of showing what strategy should be established when supporting SMEs in the future according to the government's participation in the startup program and providing a guide on what kind of support should be provided.

• Journal of Intelligence and Information Systems
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• 제28권4호
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• 제40권3호
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• pp.1-9
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• 2022

This study compared and reviewed the recognition determinants by applying the OECMs criteria, focusing on old big trees, plant of natural monument that are natural heritage under the national heritage system of the Cultural Heritage Administration, and the results are as follows. First, among the protected areas designated and managed by government agencies according to each protection purpose, it is necessary to actively introduce new conservation measures, OECMs, to fulfill the Biodiversity strategy for 2030 while the land area is already saturated. Second, the OECMs are geographically defined areas(CBD, 2018), not currently recognized as a protected areas, governed and managed in a way that achieves positived sustained and effective contribution to in situ conservation of biodiversity. Since the selection of term, the scope of application criteria, and the context of interpretation are inevitably different, it is necessary to separately legislate and establish related laws of the OECMs suitable for each country's situation. Third, as a result of reviewing the OECMs criteria for plant of natural monument, the final 58 potential resources were recognized. Important elements among the OECMs criteria are that buffer zones should be spaced apart from designated zones to secure a certain area, and that economic activities through commercial production should not occur and meet biodiversity standards. Among the potential candidates, 23 areas were analyzed to be geographically isolated and independent, such as Forest of Oriental Arborvitae in Do-dong, Daegu, and forest types such as Carstor Aralia of Gungchon-ri, Samcheok and Forest of Common Camellias in Maryang-ri, Seocheon. As a result of reviewing the application of OECMs criteria for plant of natural monument, it was confirmed that the functions as a traditional uses were specialized among the values of biodiversity, and ecosystem services and cultural and spiritual values were inherited through Korea's unique culture of old big trees and Dangsan ritual. In terms of biodiversity criteria, it can be used as an important factor in connecting human and natural ecosystem networks without the discovery of new species.

• Journal of Intelligence and Information Systems
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• 제29권1호
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Journal of Intelligence and Information Systems
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• 제29권3호
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.