• Korean Journal of Environment and Ecology
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• v.33 no.5
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• pp.506-514
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• 2019

Unlike natural ecosystems, the urban ecosystem proVides an interdependent enVironment in which wild organisms and urban people co-exist. Hornets (genus Vespa) appearing in urban green and parks haVe a positiVe effect on urban ecosystems, but they also cause ecosystem disserVices that cause physical and psychological discomforts to the urban people. Children's parks, for example, are Very popular among children and residents for easy accessibility, and hornets also use them as bases and habitats. HoweVer, there is still a lack of spatial analysis of habitats and appearance characteristics of hornets in children's parks. This study installed hornet traps in 27 children's parks in Cheonan from April to NoVember 2018 in consideration of the life cycle of hornets. We captured a total of fiVe Vespa species (Vespa crabro, V. analis, V. mandarinia, V. ducalis, and V. Velutina) for 32 weeks and analyzed the emergence of hornets in relation to the composition of seasonal characteristics, species characteristics, and enVironmental spatial information. We captured a total of 818 hornets during the study period. They included 290 V. analis (35.4%), 260 V. crabro (31.8%), 100 V. ducalis (12.1%), 87 V. mandaninia (10.6%), and 81 V. Velutina(9.9%). Most of the hornets showed a common feature that queen hornets were largely captured in May through June after they awake from hibernation, and the number of caught hornets decreased sharply beginning in mid-June, which was the cooperatiVe period. HoweVer, V. Velutina showed a seasonal specificity that more than 80% were captured beginning in the third week of October when other hornet species had already entered a decline phase. The analysis of the number of hornets caught in each spot in children's parks showed significant difference among the spots as 363 hornets (44.3%) were captured in top children's parks, and 35 hornets (4%) were captured in bottom children's parks. In particular, the mean NDVI (Normalized difference Vegetation index) of the top six children's parks was 0.79, and that of the bottom six children's parks was 0.38 (t=2.67*, *=p<0.05), indicating a significant difference. The frequency of capturing hornets was high when the ground around the children's parks was grass or bare land. This study is meaningful as a reference study that confirms the ecological characteristics of hornets appearing in green and parks in the city. We expect it to be a foundation for effectiVe urban green area management in the future.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.109-135
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• 2021

his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.49-58
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• 2019

There have been studies on the satisfaction, preference, and post occupancy evaluation of urban parks in order to reflect users' preferences and activities, suggesting directions for future park planning and management. Despite using questionnaires that are proven to be affective to get users' opinions directly, there haven been limitations in understanding the latest changes in park use through questionnaires. This study seeks to address the possibility of utilizing the thirdgeneration SNS data, Instagram and Google, to compare behavior patterns and trends in park activities. Instagram keywords and photos representing user's feelings with a specific park name were collected. We also examined reviews, peak time, and popular time zones regarding selected parks through Google. This study tries to analyze users' behaviors, emerging activities, and satisfaction using SNS data. The findings are as follows. People using park near residential areas tend to enjoy programs being operated in indoor facilities and to like to use picnic places. In an adjacent park of commercial areas, eating in the park and extended areas beyond the park boundaries is found to be one of the popular park activities. Programs using open spaces and indoor facilities were active as well. Han River Park as a detached park type offers a popular venue for excercises and scenery appreciation. We also identified companionship characteristics of different park types from texts and photos, and extracted keywords of feelings and reviews about parks posted in $3^{rd}$ generation SNS. SNS data can provide basis to grasp behavioral patterns and satisfaction factors, and changes of park activities in real time. SNS data also can be used to set future directions in park planning and management in accordance with new technologies and policies.

• The Korean Journal of Archival Studies
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• no.51
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• pp.89-132
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• 2017

The presidential records reflect the era of the times, and it has valuable evidence to support the administrative transparency and accountability of government operations. People's interest in the presidential records increased in response to its recent leak. The presidential archives were moved to Sejong in line with its desire to provide public-friendly services. This study will help users access the archives and utilize archiving information. The Ministry of Education introduced the free learning semester, which all middle schools have began conducting since 2016. The free learning semester provides an environment where education can be provided by external organizations. As middle school students are still unfamiliar with archives, the free learning semester provides a good environment for accessing archives and records. Although it serves as an opportunity to publicize archives, existing related studies are insufficient. This study aims to develop the free learning semester program using the presidential archives and records for middle school students during the free learning semester based on the analysis of the domestic and foreign archives education program. This study shows a development of the education program using presidential archives and records through literature research, domestic and foreign case analysis, and expert interview. First, through literature research, this research understood the definition of the free learning semester as well as its types. In addition, this research identified the four types of the free learning semester education program that can be linked to the presidential archives. Second, through website analysis and the information disclosure system, this research investigated domestic and foreign cases of the education program. A total of 46 education programs of institutions were analyzed, focusing on student-led education programs in the foreign archives as well as the education programs of the free learning semester in domestic libraries and archives. Third, based on these results, This study proposed four types of free learning semester education programs using the presidential archives and records, and provided concrete examples.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of agricultural medicine and community health
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• v.26 no.2
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• pp.15-41
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• 2001

Since medical clients and the community they live in are expected to be center of future public health and medical care system, new service programs must be developed with patients focused on in line with widening public access of information and social participation. Patients- focused service shall mean the area- oriented provision of public health service. In this study, health officers working at public health centers, public health sub- centers and medical offices in Jeonbuk- do area were taken for population in order to investigate their attitudes toward and knowledge about the service area assigning system under the public health programs. Findings from the survey to 260 health officers, divided by general category, are as follows : Government officers at public health organizations appeared to have high grade of understanding to the service area assigning system and also great appreciation for the necessity of it. Regarding the timing for the system to be introduced, they support the gradual implementation and, as for the type of service to be provided, they preferred home nursing and treatment of chronic diseases. Highly positive responses were centered on the health classes under the health promotion projects, and as far as health projects for the old are concerned, services for home nursing, for the disabled and for home- alone people are favored most. On the other hand, budgeting, manpower and reorganization are rated as prerequisite to establishment of the service area assigning system. From the viewpoint of system side, the improvement of working conditions is rendered as most urgent, while the information system for establishing the service area assigning system is conceived far from satisfactory. Proper assignment of specialists was noted as mostly important to establish the delivery system for medical service through the service area assigning system by team. As merits of the service area assigning system, it is pointed out that, through the system, health clients can better be managed and the nursing quality will be improved thank to the enhanced specialization. It is also perceived that the district health service is not well prepared to respond to the increased and diversified needs of community people and, furthermore, service programs of health centers have not been fully developed. The most serious problem standing in the way to expansion of health projects is, it is noted, uniformity (formality) of the project. Based on the results of the survey which suggest time has ripen to introduce the service area assigning system, following strategies are proposed to anchor down the system as soon as possible: First, we should introduce the system gradually, starting from the area selected, and in consideration of area specialities, refraining from the hitherto stereotyped way of providing health service. Second, we should seek to properly assign the specialists and improve the working conditions of the assigned officers by securing sufficient budget, since it is a most urgent step to lay foundation for the service area assigning system. Third, best service program should be developed to meet the satisfaction of community people by responding to their needs and solidifying the management of medical clients. Fourth, wide scope of study should further be conducted in order to help this system take roots in the central living of community residents since pilot project on the experimental base attended by specialists only can not win popularity among the masses.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.159-175
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• 2018

The purpose of this study was to identify the factors that could overcome the crisis and adversity of the nursing care provider through understanding the effect of job adaptation on the turnover intention of the nursing care provider and to contribute to the various problems of the nursing care provider in the long term. In order to confirm this as an empirical research task, risk factors and protection factors, general characteristics of the survey subjects, job adaptation and turnover intention were selected, and the risk factors and protective factors of caregivers' As a mediator. So Seoul. The results of the questionnaire survey were as follows: 291 caregivers in the elderly medical welfare facilities in Gyeonggi area. First, as the relationship between the risk factors and protective factors of occupational caregivers and occupational adaptation were more severe, the higher the maladjustment of the workplace culture, the more the job satisfaction and organizational commitment were adversely affected. The emotional support, The higher the information support, the more satisfied and satisfied the job. Second, the relationship between the risk factors of the caregiver and the protective factors and the turnover intention, the higher the conflict of caregivers, the more unstable the workplace, the more difficult it is to adapt to work culture, Respectively. Finally, as a result of verifying the mediating effect of occupational adaptation on the relationship between risk factors and protective factors and turnover intention of caregivers, job satisfaction, which is a sub-factor of job adaptation, It is shown that they play mediating roles only in the relationship between stress and turnover intention, and do not play a mediating role in the relationship between protective factor self - efficacy and social support and turnover intention. In other words, if caregivers feel satisfaction about their job, they can be less stressed on their jobs, improve their self-efficacy, and have a positive attitude toward social support. Also, it was found that the more the caregiver 's immersion into the organization, the less job stress and turnover intention decreased, but the self - efficacy and social support perception were not influenced. Based on this, the director of the facility should strive to stabilize the operation of the facility and provide high-quality services by seeking ways to improve conflict resolution and adaptation to the workplace culture so that nursing care workers can adapt to their work. And it is required to develop active management strategies and institutional support for improving job satisfaction and organizational commitment of caregivers.