• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.3
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• pp.78-97
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• 2016

The objectives of this study are to examine the context of the establishment of Suhoe Gugok, Byeokgye Gugok Vally, and Nosan Palkyung, which have been established in Seojong-myeon of Yangpyeong-gun, by literature review and site investigations, and to determine the sceneries of Byeokgye scenic site as enjoyed and managed during the period of Hwaseo Lee, Hang-ro(華西 李恒老). The results of the study are as follows. First, Byeokgye Gugok Vally(黃蘗九曲) and Nosan Palkyung(蘆山八景), which have been established after the period of Hwaseo and theorized to have been established around key scenic areas associated with Hwaseo's activities, the analysis results showed that they were collecting sceneries of modern times. The extensive overlap between Byeokgye Gugok Vally and concentrated scenic elements of Suhoe Gugok(水回九曲), and the artificial configuration from the end point of Suhoe Gugok to the beginning point of Nosan Palkyung, reveal the pattern of space conflict and hegemony between Byeokgyes of Suip-ri and Nomun-ri. This is likely to be caused by the conflict between the historicity of the group that enjoyed Byeokgye prior to Hwaso's period and the strong territoriality of the space filled with the image of Hwaseo. Second, Byeokgye Gugok Vally was the secondary spatial system created by selecting the most scenic sites in Suip-ri while expanding the area of Nosan Palkyung. After establishment of Byeokgye Gugok Vally, the spatial identity of the entire Byeokgyecheon area was effectively established. This was a "Hwaseo-oriented" move, including the complete exclusion of the scenic sites from the pre-Hwaseo period such as Cheongseo Gujang and Suhoe Gugok's Letters Carved on the Rock. Consequently, the entire Byeokgyecheon area was reorganized into a cultural scenic site with Heoseo's influence. Third, Fifth, creations of Gugok(九曲) to determine the lineage of the Hwaseo School from Juja(朱子) to Yulgok(栗谷) to Uam(尤庵) to Hwaseo is likely to be an opportunity of birth and external motivation of the establishment of new Gugok Palkyung. In other words, Nosan Palkyung and Byeokgye Gugok Vally are likely to have been created as a reaction to the change of the center of the Hwaseo School to Okgyedong, and with strategic orientation based on the motivation and needs such as creation of the connecting space between Mui Gugok, Gosan Gugok, and Okgye Gugok, and the elevation of Hwaseo's status. Fourth, from the Hwaseo's Li-centric point of view, all revered sites in Beokwon(蘗園) that he managed existed as the spatial creative work to experience the existence of "li" through the objects in the landscape and the boundary of the spirit of emptiness of the aesthetic self. This clearly shows how Byeokgye Gugok Vally or Nosan Palkyung must be defined, and furthermore, appreciated and approached, prior to discussing it as the space associated with Hwaseo. Fifth, Nosan Palkyung was composed of cultural scenic landscapes of Gokjungkyung(曲中景) with eight scenic sites where Hwaseo gave his teachings and spend time around, in the Byeokgye of Nomun-ri area of Byeokgye Gugok Vally. The sceneries is, however, collected by depending on Hwaseo's Letters Carved on the Rock and poetry. Consequently, an inner exuberance of Nosan Palkyung is satisfied beside Byeokgye Gugok Vally, but its conceptual adequacy leaves room for questions.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.87-109
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• 2020

Currently, thanks to the major stride made in developing wired and wireless communication technology, a variety of IT services are available on land. This trend is leading to an increasing demand for IT services to vessels on the water as well. And it is expected that the request for various IT services such as two-way digital data transmission, Web, APP, etc. is on the rise to the extent that they are available on land. However, while a high-speed information communication network is easily accessible on land because it is based upon a fixed infrastructure like an AP and a base station, it is not the case on the water. As a result, a radio communication network-based voice communication service is usually used at sea. To solve this problem, an additional frequency for digital data exchange was allocated, and a ship ad-hoc network (SANET) was proposed that can be utilized by using this frequency. Instead of satellite communication that costs a lot in installation and usage, SANET was developed to provide various IT services to ships based on IP in the sea. Connectivity between land base stations and ships is important in the SANET. To have this connection, a ship must be a member of the network with its IP address assigned. This paper proposes a SANET-CC protocol that allows ships to be assigned their own IP address. SANET-CC propagates several non-overlapping IP addresses through the entire network from land base stations to ships in the form of the tree. Ships allocate their own IP addresses through the exchange of simple requests and response messages with land base stations or M-ships that can allocate IP addresses. Therefore, SANET-CC can eliminate the IP collision prevention (Duplicate Address Detection) process and the process of network separation or integration caused by the movement of the ship. Various simulations were performed to verify the applicability of this protocol to SANET. The outcome of such simulations shows us the following. First, using SANET-CC, about 91% of the ships in the network were able to receive IP addresses under any circumstances. It is 6% higher than the existing studies. And it suggests that if variables are adjusted to each port's environment, it may show further improved results. Second, this work shows us that it takes all vessels an average of 10 seconds to receive IP addresses regardless of conditions. It represents a 50% decrease in time compared to the average of 20 seconds in the previous study. Also Besides, taking it into account that when existing studies were on 50 to 200 vessels, this study on 100 to 400 vessels, the efficiency can be much higher. Third, existing studies have not been able to derive optimal values according to variables. This is because it does not have a consistent pattern depending on the variable. This means that optimal variables values cannot be set for each port under diverse environments. This paper, however, shows us that the result values from the variables exhibit a consistent pattern. This is significant in that it can be applied to each port by adjusting the variable values. It was also confirmed that regardless of the number of ships, the IP allocation ratio was the most efficient at about 96 percent if the waiting time after the IP request was 75ms, and that the tree structure could maintain a stable network configuration when the number of IPs was over 30000. Fourth, this study can be used to design a network for supporting intelligent maritime control systems and services offshore, instead of satellite communication. And if LTE-M is set up, it is possible to use it for various intelligent services.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Korean Journal of Community Nutrition
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• v.26 no.3
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• pp.188-199
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• 2021

Objectives: The purpose was to identify the ingredients that are usually surveyed for assessing real prices and to present the demand for such surveys by nutrition teachers and dietitians for ingredients used by school foodservice. Methods: A survey was conducted online from December 2019 to January 2020. The survey questionnaire was distributed to 1,158 nutrition teachers and dietitians from elementary, middle, and high schools nationwide, and 439 (37.9% return rate) of the 1,158 were collected and used for data analysis. Results: The ingredients which were investigated for price realities directly by schools were industrial products in 228 schools (51.8%), fruits in 169 schools (38.4%), and specialty crops in 166 schools (37.7%). Moreover, nutrition teachers and dietitians in elementary, middle, and high schools searched in different ways for the real prices of ingredients. In elementary schools, there was a high demand for price information about grains, vegetables or root and tuber crops, special crops, fruits, eggs, fishes, and organic and locally grown ingredients by the School Foodservice Support Centers. Real price information about meats, industrial products, and pickled processed products were sought from the external specialized institutions. In addition, nutrition teachers and dietitians in middle and high schools wanted to obtain prices of all of the ingredients from the Offices of Education or the District Office of Education. Conclusions: Schools want to efficiently use the time or money spent on research for the real prices of ingredients through reputable organizations or to co-work with other nutrition teachers and dietitians. The results of this study will be useful in understanding the current status of the surveys carried out to determine the real price information for ingredients used by the school foodservice.

• Atmosphere
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• v.33 no.2
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• pp.275-295
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• 2023

In Korea, there are four institutions related to atmospheric science: the university's atmospheric science-related department, the Korea Meteorological Administration (KMA), the ROK Air Force Weather Group, and the Meteorological Industry Association. These four institutions have developed while maintaining a deep cooperative relationship with the Korea Meteorological Society (KMS) for the past 60 years. At the university, 6,986 bachelors, 1,595 masters, and 505 doctors, who are experts in meteorology and climate, have been accredited by 2022 at 7 universities related to atmospheric science. The KMA is carrying out national meteorological tasks to protect people's lives and property and foster the meteorological industry. The ROK Air Force Weather Group is in charge of military meteorological work, and is building an artificial intelligence and space weather support system through cooperation with universities, the KMA, and the KMS. Although the Meteorological Industry Association has a short history, its members, sales, and the number of employees are steadily increasing. The KMS greatly contributed to raising the national meteorological service to the level of advanced countries by supporting the development of universities, the KMA, the Air Force Meteorological Agency, and the Meteorological Industry Association.