• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.5
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• pp.209-217
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• 2015

Development of food industry and increased city life induced enlarged generation of food waste which is currently reused in a wide range of way. In this study, treatment of food waste generated from the public agricultural product wholesale markets (they are categorized as business places which imposes the duty for reducing the food waste discarded) in Korea was investigated, and subsequently, a scheme for improving the food waste recycling business was suggested. The food waste treatment plants are mainly located in Kyeongki-do at 39.5% of total plants in Korea and the other provinces have less than 10% of the total numbers, among which public treatment plant was 38.0% and private plant was 62.0%. The treatment methods included recycling as animal feed at 47.5%, as compost at 36.4%, and the rest of food waste (12.6%) was treated in other ways. Remarkably, it was noticed that the amount of food waste treated in anaerobic digestion have been increased up to 5.4% since 2011. This implied that food waste treated in anaerobic digestion method is gradually increased according to government policy. Amongst 33 public agricultural product wholesale markets distributed all over the country, the trading volume dealt in Garakdong market in Seoul, Gangseo market in Seoul, Daegu market in Daeju, Eomgung market in Busan, Guri market in Guri accounted for 34.67, 7.47, 6.98, 5.41, and 5.30% of the total trading volume in Korea, respectively. 2.65% of the total trading agricultural products dealt in the markets were remained as food waste and treated. In 2006, Ministry of Agriculture, Food and Rural Affairs implemented the package policy for radish and Chinese cabbage in order to reduce the amount of food waste and indeed, food waste generated in Garakdong market declined to one third of the food waste before the package policy implementation. In 2010, the food waste amount treated by 'dehydration', 'discarding as raw materials', and 'drying after dehydration' accounted for 56.3%, 33.7%, and 10.0% of the total food waste generated in the public agricultural product wholesale market. However, in 2013, discarding as raw materials accounted for the most at 56.3% followed by dehydration at 37.5%, and by drying after dehydration at 6.3%. The remarkable increment of the food waste discarded as raw materials was attributed to the increase of them in Garakdong market in Seoul. In general, the agricultural food waste contains high amount of moisture at 88% in average and low amount of salt at 0.02% in average. Therefore, it is highly recommended to treat the agricultural food waste through drying after dehydration in order for improving the treatment capability. Also, this recommendation can be supported by the fact that the end-products via drying after dehydration can be utilized as compost material. Overall, the agricultural food waste recycling business can be developed through integration of the treatment via 'drying after dehydration' and compost production.

• International Journal of Industrial Entomology and Biomaterials
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• v.20 no.1
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• pp.29-35
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• 2010

To compare the quality of manufactured goods distributed in the domestic markets, 6 isolates of Dongchongxiacao products, namely, 4 Paecilomyces tenuipes specimens (J2P, 901A, 901B, and 901C), 1 Cordyceps militaris specimen (901D), and 1 Cordyceps sinensis specimen (CI-1), were analyzed for their physicochemical properties and sequence-structure relationships. P. tenuipes (J2P), a kind of Dongchongxiacao, was successfully inoculated on silkworms by percutaneous infection of Rural Development AdminstraionNam et al., 1999); fruiting bodies were then formed on the complete surface of the pupa. Since P. tenuipes (J2P) from silkworm larva was also proved to have remarkable pharmacological activities, it has been produced in bulk and has been successfully sold to buyers in the Korean market. Additionally, imitation products such as 901A, 901B, 901C, 901D, and CI-1 were sold simultaneously, resulting in deterioration of product quality. This research focuses on establishing quality standards to discriminate between the original and imitation products circulating in the market. The products obtained for the experiments included J2P, 901A, 901B, 901C, 901D, and CI-1; proximate analysis was performed for these products. The hosts and methods of conidia inoculation for proliferation of mycelia differed among the products. P. tenuipes (J2P) was proliferated in live silkworm larvae, and dead silkworm pupae were used to produce 901A, 901B, and 901C. On the other hand, 901D was produced on hulled rice medium. Quality analysis of C. sinensis revealed that CI-1, which was imported from China, smelled bad and proved to be a counterfeit with the fruiting body glued to the insect by twigs. The results of the proximate analysis of 901A, 901B, 901C, and 901D were similar to those of J2P with respect to the moisture content. Otherwise, J2P contained higher crude protein than 901A, 901B, 901C, and 901D, but contained very low fat. C. militaris (901D) and C. sinensis (CI-1) had low crude protein content-12.79% and 9.78% respectively-as compared to that of J2P, which was 62.38%. In contrast to the crude ash content of 6.4% in J2P, the crude ash content of CI-1 was 18.51% and this specimen was found to contain many impurities. phylogenetic analysis of P. tenuipes revealed that the sequence similarity of J2P, 901B, and 901C was in the range of 92.3~92.7%. Additionally, differences in the sequences were found at the positions 65 bp, 436 bp, 441 bp, 463 bp, etc.

• Horticultural Science & Technology
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• v.34 no.1
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• pp.195-205
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• 2016

Sugar beet cyst nematode, Heterodera schachtii, causes serious economic losses worldwide in Brassicaceae crops. In 2011, this nematode was first found in highland vegetable cultivation areas in Korea, and thereafter spread to the surrounding healthy Chinese cabbage fields. However, little has been documented on the biological and ecological characteristics of the sugar beet nematode in highland vegetable cultivation areas. In this study the dispersal of the sugar beet cyst nematode was examined, focusing on spreading through soil and/or water infested with the nematode. When farmers with work boots trampled on Chinese cabbage fields for 10 minutes, the number of cysts recovered from the soil attached to the working differed depending on the research sites. Under irrigation conditions of 2, 4, and 8 liters of water per $m^2$, the amounts of soils attached on the bottom of the work boots and the number of healthy cysts in the soils increased significantly with the increase in soil moisture, although the total number of cysts, eggs, and juveniles did not increase significantly. After driving on the farm road adjacent to cabbage fields infested with the sugar beet cyst nematode, cysts were also recovered from the soil attached to the vehicle's tires, suggesting that the sugar beet cyst nematode can spread to new fields through soil carried by vehicles and by farmers. An excavator and a motor truck could deliver 41 kg and 224 g, respectively, of soil on the shovel shoes and the wheels to other locations during the Chinese cabbage harvest, suggesting that farming equipment and transportation vehicles may be vital means by which the cyst nematode spreads to non-infested fields in the highland area of Korea. Runoff water also contained cysts, whose amounts increased after water irrigation onto the ridges to simulate rainy conditions, with no significant difference in the number of cysts with increasing amounts of irrigation water. All of these results confirmed that the sugar beet cyst nematode spreads through soil attached to work boots, harvesting tools, and transportation vehicles, especially during the harvest time, and through runoff water on rainy days. These observations suggest that personal sanitization and cleaning of working tools and vehicles are one of the most important means to prevent the dispersal of the sugar beet cyst nematode in fields.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Progress in Medical Physics
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• v.23 no.4
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• pp.279-284
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• 2012

The X ray has been widely used in both diagnosis and treatment. Recently, a miniature X ray tube has been developed for radiotherapy. The miniature X ray tube is directly inserted into the body irradiated, so that X rays can be guided to a target at various incident angles according to collimator geometry and, thus, minimize patient dose. If such features of the miniature X ray tube can be applied to development of X ray imaging as well as radiation treatment, it is expected to open a new chapter in the field of diagnostic X ray. However, the miniature X ray tube requires an added filter and a collimator for diagnostic purpose because it was designed for radiotherapy. Therefore, a collimator and an added filter were manufactured for the miniature X ray tube, and mounted on. In this study, we evaluated beam characteristics of the miniature X ray tube for diagnostic X ray system and accuracy of measuring the HVL. We used the Si PIN Photodiode type Piranha detector (Piranha, RTI, Sweden) and estimated the HVL of the miniature X ray tube with added filter and without added filter. Through an another measurement using Al filter, we evaluated the accuracy of the HVL obtained from a direct measurement using the automatic HVL calculation function provided by the Piranha detector. As a result, the HVL of the miniature X ray tube was increased around 1.9 times with the added filter mounted on. So we demonstrated that the HVL was suitable for diagnostic X ray system. In the case that the added filter was not mounted on, the HVL obtained from use of the automatic HVL calculation function provided by Piranha detector was 50% higher than the HVL estimated using Al filter. Therefore, the HVL automatic measurement from the Piranha detector cannot be used for the HVL calculation. However, when the added filter was mounted on, the HVL automatic measurement value using the Piranha detector was approximately 15% lower than the estimated value using Al filter. It implies that the HVL automatic measurement can be used to estimate the HVL of the miniature X ray tube with the added filter mounted on without a more complicated measurement method using Al filter. It is expected that the automatic HVL measurement provided by the Piranha detector enables to make kV-X ray characterization easier.

• Journal of radiological science and technology
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• v.31 no.4
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• pp.389-399
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• 2008

Corrections of attenuation, scatter and resolution are important in order to improve the accuracy of single photon emission computed tomography (SPECT) image reconstruction. Especially, the heart movement by respiration and beating cause the errors in the corrections. Myocardial phantom is used to verify the correction methods, but there are many different parts in the current phantoms in actual human body. Therefore the results using a phantom are often considered apart from the clinical data. We developed a new phantom that implements the human body structure around the thorax more faithfully. The new phantom has the small mediastinum which can simulate the structure in which the lung adjoins anterior, lateral and apex of myocardium. The container was made of acrylic and water-equivalent material was used for mediastinum. In addition, solidified polyurethane foam in epoxy resin was used for lung. Five different sizes of myocardium were developed for the quantitative gated SPECT (QGS). The septa of all different cardiac phantoms were designed so that they can be located at the same position. The proposed phantom was attached with liver and gallbladder, the adjustment was respectively possible for the height of them. The volumes of five cardiac ventricles were 150.0, 137.3, 83.1, 42.7 and 38.6ml respectively. The SPECT were performed for the new phantom, and the differences between the images were examined after the correction methods were applied. The three-dimensional tomography of myocardium was well reconstructed, and the subjective evaluations were done to show the difference among the various corrections. We developed the new cardiac and torso phantom, and the difference of various corrections was shown on SPECT images and QGS results.

• Ocean and Polar Research
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• v.37 no.4
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• pp.341-356
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• 2015

A nation has a sovereign right to develop and use its natural resources according to its policies with regard to development and the relevant environment. A nation also has an obligation not to harm other countries or damage environments of neighboring countries as consequences of such actions of developments or use of natural resources. However, international precedents induce a nation to take additional actions not to cause more damages from the specific acts causing environmental damages beyond national borders, when such acts have economic and social importance. That is to say that there is a tendency to resolve such issues in a way to promote the balance between the mutual interests by allowing such actions to continue. A solution to China's Three Gorges Dam dilemma based on a soft law approach is more credible than relying on a good faith approach of national responsibilities and international legal proceedings since the construction and operation of the dam falls within the category of exercising national sovereign rights. If a large scale construction project such as the Three Gorges Dam or operation of a nuclear power plant causes or may cause environmental damage beyond the border of a nation engaged in such an undertaking, countries affected by this undertaking should jointly monitor the environmental effects in a spirit of cooperation rather than trying to stop the construction and should seek cooperative solutions of mutual understanding to establish measures to prevent further damages. If China's Three Gorges Dam construction and operation cause or contain the possibility of causing serious damages to marine environment, China cannot set aside its national responsibility to meet international obligations if China is aware of or knows about the damage that has occurred or may occur but fail to prevent, minimize, reverse or eliminate additional chances of such damages, or fails to put in place measures in order to prevent the recurrence of such damages. However, Korea must be able to prove a causal relationship between the relevant actions and resulting damages if it is to raise objections to the construction or request certain damage-prevention actions against crucial adverse effects on the marine environment out of respect for China's right to develop resources and acts of use thereof. Therefore, it is essential to cumulate continuous monitoring and evaluations information pertaining to marine environmental changes and impacts or responses of affected waters as well as acquisition of scientific baseline data with observed changes in such baseline. As China has adopted a somewhat nonchalant attitude toward taking adequate actions to protect against marine pollution risks or adverse effects caused by the construction and operation of China's Three Gorges Dam, there is a need to persuade China to adopt a more active stance and become involved in the monitoring and co-investigation of the Yellow Sea in order to protect the marine environment. Moreover, there is a need to build a regular environmental monitoring system that includes the evaluation of environmental effects beyond borders. The Espoo Convention can serve as a mechanism to ease potential conflicts of national interest in the Northeast Asian waters where political and historical sensitivities are acute. Especially, the recent diplomatic policy advanced by Korea and China can be implemented as an important example of gentle cooperation as the policy tool of choice is based on regional cooperation or cooperation between different regions.

• 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.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.145-162
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• 2008

One of the big concerns in e-society is privacy issue. In special, in developing robust ubiquitous smart space and corresponding services, user profile and preference are collected by the service providers. Privacy issue would be more critical in context-aware services simply because most of the context data themselves are private information: user's current location, current schedule, friends nearby and even her/his health data. To realize the potential of ubiquitous smart space, the systems embedded in the space should corporate personal privacy preferences. When the users invoke a set of services, they are asked to allow the service providers or smart space to make use of personal information which is related to privacy concerns. For this reason, the users unhappily provide the personal information or even deny to get served. On the other side, service provider needs personal information as rich as possible with minimal personal information to discern royal and trustworthy customers and those who are not. It would be desirable to enlarge the allowable personal information complying with the service provider's request, whereas minimizing service provider's requiring personal information which is not allowed to be submitted and user's submitting information which is of no value to the service provider. In special, if any personal information required by the service provider is not allowed, service will not be provided to the user. P3P (Platform for Privacy Preferences) has been regarded as one of the promising alternatives to preserve the personal information in the course of electronic transactions. However, P3P mainly focuses on preserving the buyers' personal information. From time to time, the service provider's business data should be protected from the unintended usage from the buyers. Moreover, even though the user's privacy preference could depend on the context happened to the user, legacy P3P does not handle the contextual change of privacy preferences. Hence, the purpose of this paper is to propose a mutual P3P-based negotiation mechanism. To do so, service provider's privacy concern is considered as well as the users'. User's privacy policy on the service provider's information also should be informed to the service providers before the service begins. Second, privacy policy is contextually designed according to the user's current context because the nomadic user's privacy concern structure may be altered contextually. Hence, the methodology includes mutual privacy policy and personalization. Overall framework of the mechanism and new code of ethics is described in section 2. Pervasive platform for mutual P3P considers user type and context field, which involves current activity, location, social context, objects nearby and physical environments. Our mutual P3P includes the privacy preference not only for the buyers but also the sellers, that is, service providers. Negotiation methodology for mutual P3P is proposed in section 3. Based on the fact that privacy concern occurs when there are needs for information access and at the same time those for information hiding. Our mechanism was implemented based on an actual shopping mall to increase the feasibility of the idea proposed in this paper. A shopping service is assumed as a context-aware service, and data groups for the service are enumerated. The privacy policy for each data group is represented as APPEL format. To examine the performance of the example service, in section 4, simulation approach is adopted in this paper. For the simulation, five data elements are considered: $\cdot$ UserID $\cdot$ User preference $\cdot$ Phone number $\cdot$ Home address $\cdot$ Product information $\cdot$ Service profile. For the negotiation, reputation is selected as a strategic value. Then the following cases are compared: $\cdot$ Legacy P3P is considered $\cdot$ Mutual P3P is considered without strategic value $\cdot$ Mutual P3P is considered with strategic value. The simulation results show that mutual P3P outperforms legacy P3P. Moreover, we could conclude that when mutual P3P is considered with strategic value, performance was better than that of mutual P3P is considered without strategic value in terms of service safety.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.