• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Trans-
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• v.3
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• pp.55-86
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• 2017

This study begins with the necessity of the concept of reincarnation of film media and the inclusion of specific tendencies of contemporary films as post - cinema comes. Variable movements around recent films Challenging and experimental films show aesthetics that are difficult to approach with the analysis of classical mise en scene and montage. In this way, I review the dispositif proposed by Martin in films that are puzzling to criticize with the classical conceptual framework. This is because the concept of dispositive is a conceptual pile that extends more than a mise en scene and a montage. Dispositif films tend to be non-reproducible and non-narrative, but not all non-narrativef tendencies are dispositif films. Only the dispositif film is included in the flow. Dispositif movement has increased dramatically in the modern environment on which digital technology is based, but it is not a tendency to be found in any particular age. The movement has been detected in classical films, and the dispositif tendency has continued to exist in avant-garde films in the 1920s and some modernist films. First, for clear conceptualization of cinematic dispositif, this study examines the sources of dispositif debates that are being introduced into film theory today. In this process, the theory of Jean Louis Baudry, Michel Foucault, Agamben, Flusser, and Deleuze will help. The concept of dispositif was discussed by several scholars, including Baudry and Foucault, and today the notion of dispositif is defined across all these definitions. However, these various discussions are distinctly different from the cinematic dispositif or dispositif films that Martin advocates. Martin's proposed concept reminds us of the fundamentals of cinematic aesthetics that have distinguished between the mise-en-scene and the montage. And it will be able to reconsider those concepts and make it possible to view a thing a new light or create new films. The basic implications of dispositif are apparatus as devices, disposition and arrangement, the combination of heterogeneity. Thus, if you define a dispositif film in a word, it is a new 'constraint' consisting of rearrangement and arrangement of the heterogeneous elements that make up the conditions of the classical film. In order for something to become a new design, changes must be made in the arrangement and arrangement of the elements, forces, and forces that make up it. Naturally, the elements encompass both internal and external factors. These dispositif films have a variety of possibilities, such as reflection on the archival possibilities and the role of supervision, the reestablishment of active and creative audience, the reason for the film medium, and the ideological reflection. films can also 'network' quickly and easily with other media faster than any medium and create a new 'devised' aesthetic style. And the dispositif film that makes use of this will be a key keyword in reading the films that present the new trend of modern film. Because dispositif are so comprehensive and have a broad implication, there are certainly areas that are difficult to sophisticate. However this will have a positive effect on the future activation of dispositif studies end for end. Dispositif is difficult to elaborate the concept clearly, so it can be accessed from a wide range of dimensions and has theoretically infinite extensibility. At the beginning and end of the 21st century film, the concept of cinematic dispositif will become a decisive factor to dismantle old film aesthetics.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.6
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• pp.357-365
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• 2006

The investigations were conducted to find out the situation of fertilizer use and the contents of soil chemical components on summer vegetable crops at 791 farmers' upland fields located in the parts of Gangwon-do, Gyengsangbuk-do and Jeollabuk-do of alpine area. Major vegetable crops were potato, Chinese cabbage, radish, carrot, onion, and cabbage. From the location surroundings cultivated alpine vegetable crops, the orders were Gangwon-do>Gyeongsangbuk-do>Jeollabuk-do part in the sizes of a fie1d area and the height above sea level, and Jeollabuk-do>Gyeongsangbuk-do>Gangwon-do part in the slope degrees. The soil texture was of wide distribution on sandy loam soil for Gangwon-do(76%) and Jeollabuk-do part(64%), and 1oam(42%) and sandy loam soil(35%) for Gyeongsangbuk-do part. From the numbers of investigated fields, the distribution of slope degree was wider than those of height above sea level in relation to location surroundings. The upland soils of 785 fields cultivated vegetable crops were sampled at 0~15 cm of top soil before seeding or transplanting and analyzed. On an average, pH, organic matter, available phosphate and exchangeable potassium, calcium, magnesium of soil were 5.7, $27.6g\;kg^{-1}$, $765mg\;kg^{-1}$, $1.16cmol_c\;kg^{-1}$, $6.1cmol_c\;kg^{-1}$, and $1.6cmol_c\;kg^{-1}$, respectively. The average cation exchange capacity(CEC) of 120 sites in Gangwon-do part was $9.2cmol_c\;kg^{-1}$. The content of organic matter, exchangeable potassium, exchangeable calcium and exchangeable magnesium was higher, while that of available phosphate was lower with slope degrees. And the content of major chemical components in carrot soil was lower in comparison with other crop soils. The average levels of N, $P_2O_5$, $K_2O$, livestock manure and lime fertilizer of 785 Belds applied by farmers were 335, 198, 244, 12,680 and $1,750kg\;ha^{-1}$, respectively, for summer vegetable crops in alpine area. The average amounts of $N-P_2O_5-K_2O$ fertilizers applied by farmers in 785 Gelds of vegetable crops were higher 1.7~2.0-4.2~7.0-1.4~2.0 times on potato, 1.4~1.6-4.6~8.3-3.5~4.2 times on Chinese cabbage, and 1.2~1.3-4.2~7.2-3.0~3.61 times on radish than the rates of NPK fertilizers based on soil testing for each crop.

• Journal of Animal Environmental Science
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• v.17 no.1
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• pp.39-48
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• 2011

The present survey was conducted to provide basic information on automatic milking system (AMS) in relation to purchase motive, milk yield and quality, customer satisfaction, difficulties of operation and customer suggestions, etc. Purchase motives of AMS were insufficient labor (44%), planning of dairy experience farm (25%), better performance of high yield cows (19%) and others (6%), respectively. Average cow performance after using AMS was 30.9l/d for milk yield, 3.9% for milk fat, 9,100/ml for bacterial counts. Sixty-eight percentage of respondents were very positive in response to AMS use for their successors but 18% were negative. The AMS operators were owner (44%), successor (44%), wife (6%) and company worker (6%), respectively. The most difficulty (31%) in using AMS was operating the system and complicated program manual. The rate of response to system error and breakdown was 25%. The reasons for culling cow after using AMS were mastitis (28%), reproduction failure (19%), incorrect teat placement (12%), metabolic disease (7%) and others (14%), respectively. Fifty-six percentages of the respondents made AMS maintenance contract and 44% did not. Average annual cost of the maintenance contract was 6,580,000 won. Average score for AMS satisfaction measurement (1 to 5 range) was 3.2 with decrease of labor cost 3.7, company A/S 3.6, increase of milk yield 3.2 and decrease of somatic cell count 2.8, respectively. Suggestions for the higher efficiency in using AMS were selecting cows with correct udder shape and teat placement, proper environment, capital and land, and attitude for continuous observation. Systematic consulting was highly required for AMS companies followed by low cost for AMS setup and systematization of A/S.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.181-199
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• 2010

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user's preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user's psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn't like the recommendation list received from them. That is, this step updates each user's neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user's interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illnesses and to improve user's lifestyle through providing context aware and personalized food and exercise services with the help of similar users'experience and knowledge. We expect that the user of this system can improve their life habit with the help of handheld mobile smart phone, because it uses autonomous collaboration to arouse interest in healthcare.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.477-490
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• 2022

Serious Accident Punishment Act(SAPA) went into effect as of Jan. 27, 2022. The subject of study was the worker of the nuclear medicine department and the investigation was aimed at identifying the present situation of their understanding on the issue in the here and now, which can be utilized as basic research for further study. The survey was conducted on 51 people of the worker in the nuclear medicine department. The general factors were classified by their gender, the scale of the hospitals, the period of career, and the detailed occupational categories. The conclusion was drawn, including 1 missing data in gender and 2 in the type of occupation. The targeted hospitals were tertiary hospital, university hospital, and general hospital which have nuclear medicine department in. The period of subjects' career was categorized by less than 3 years, 3 to 5 years, 5 to 10 years, and more than 10 years. The specific occupation was classified by in-vivo radiological technologist, radiation safety manager and others. The amount of pressure that the job entails was highest in the category of general hospital, the period of 3 to 5 years of job experience, and radiation safety manager each. The system of the code was well constructed in the category of general hospital, the period of less than 3-year career, and radiation safety manager, as they responded. The blood transmissible disease had the largest number of outbreak of accidents related to the serious industrial accident. In addition, the radiopharmaceutical dosing error had the highest number of outbreak of accidents related to the serious civil accident. Therefore, we need to improve SAPA, facility inspection, security of budget, security of professional manpower. It will help the stable use of radiation and ensure patient safety.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.4
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• pp.392-401
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• 2023

Corn (Zea mays L.) is one of the world's most important crops, along with wheat and rice, with a global corn production expected to reach 1,154.5 million tons in 2023. Considering this grain production, The generation of corn cob is expected to reach approximately 207.8 million tons in 2023. However, as an agricultural by-product, corn cobs are often considered waste and remain underutilized. Phytosterols, which are abundant in vegetable oils such as corn oil, provide a number of health benefits, including liver health, cholesterol reduction, and protection against chronic diseases such as diabetes. In this study, we investigated the potential of Kwangpyeongok ears, which are commonly used as grain and silage corn in Korea. We also examined the variation in phytosterol content with the maturity of corn ears to identify the optimal time for utilization. At the beginning of physiological maturity, corn cobs had 113.3 mg/100g DW of total phytosterols, which was highest phytosterol abundance during the growth stage. Corn kernels also had the highest phytosterol content at the beginning of physiological maturity. While previous studies on corn bioactive compounds have mainly focused on the kernels, the results of this study highlight that cobs are an excellent source of these compounds. Furthermore, phytosterol levels were influenced by genetic factors and developmental stages, suggesting the to increase the use of cobs as a source of bioactive compounds.

• Horticultural Science & Technology
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• v.32 no.5
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• pp.645-654
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• 2014

This study was conducted to investigate the effect of 1-methylcyclopropene (1-MCP, $1.0{\mu}L{\cdot}L^{-1}$), a known ethylene action inhibitor, on fruit quality and incidence of physiological disorders during a simulated marketing period at $25^{\circ}C$ for 20 days in early-season Asian pear (Pyrus pyrifolia Nakai) 'Hanareum' that had been treated with 0, 0.5, 1.2 or 2.4% $GA_{4+7}$. Weight loss of stored fruits increased with $GA_{4+7}$ concentration, and the 1-MCP treatment slightly reduced the weight loss rates during the marketing period. Flesh firmness decreased abruptly in all 1-MCP-untreated fruits as the storage period extended to 10 d, whereas the firmness of 1-MCP-treated fruits remained high (> 30 N) during 15 days shelf-life. The effect of 1-MCP was significantly reduced when fruits were subjected to increased GA concentration. Higher soluble solids content and acidity during extended shelf-life were also apparent in 1-MCP-treated 'Hanareum' pears. The L-values (lightness) and hue angles of 1-MCP treated samples were higher than those of controls during 20 days shelf-life, but the a-value (redness) was lower in 1-MCP treated fruits. 1-MCP treatment did not decrease the level of ethylene evolution regardless of $GA_{4+7}$ concentration during shelf-life in early-season Asian pear 'Hanareum'. By contrast, 1-MCP treatment decreased the respiration rate significantly during shelf-life. The efficacy of 1-MCP was greatest in the GA-untreated fruit and was reduced as the $GA_{4+7}$ concentration increased. 1-MCP treatment influenced the severity of physiological disorders including core browning and mealiness: 1-MCP treatment completely blocked the incidence of core browning of during 15 days shelf-life, and reduced the severity of mealiness during 20 days shelf-life regardless of $GA_{4+7}$ concentration. Based on our results, we conclude that the use of $1{\mu}L{\cdot}L^{-1}$ 1-MCP can be of great benefit for maintaining quality and preventing physiological disorders in early-season pear cultivar 'Hanareum' pear, whereas its efficacy decreases with the concentration of $GA_{4+7}$ whereas its efficacy gradually decreases when the concentration of $GA_{4+7}$ paste increased.