• Journal of Korean Home Economics Education Association
• /
• v.25 no.2
• /
• pp.1-19
• /
• 2013

The purpose of this study was to develope and implement a project teaching learning process plan in order to improve a creativity and character for 'residential space utilization' section of Technology Home Economics in middle school. The teaching learning process plan consisting of 15-session lessons had been developed and implemented according to the ADDIE model mixed with 6 project learning steps. In the development stage, 8 activity materials(7 individual and 1 group activity sheets) and 7 teaching learning materials(2 sets of pictures & photos, 4 moving pictures and 1 space plan resources book) were developed for the 15-session lessons. The plans applied to 5 classes 163 students in the second grade of G middle school in Gwangju during Oct. 17th to 18th of Nov. 2011. The results from the survey and portfolio showed that the 15-session lessons had overall achieved the general goal of the project teaching learning process plan to improve a creativity and character. Students were stimulated by individual and group activities with creativity and character elements in the class. The students evaluated the whole process of 15 lessons were interesting and helpful to improve creativity and consideration and cooperation of aspect of character. The individual and group results of the portfolio were excellently and creatively done with the average of nearly 85% points. The researcher also found the improving process of students in the whole classes. This plan might apply to other parts of housing as well as various other areas of home economics.

• Journal of Bio-Environment Control
• /
• v.18 no.1
• /
• pp.29-39
• /
• 2009

The influence of windbreak to minimize the ventilation velocity near the plant canopy of a greenhouse strawberry was thoroughly investigated using computational fluid dynamics (CFD) technology. Windbreaks were constructed surrounding the plant canopy to control ventilation and maintain the concentration of the supplied $CO_2$ from the soil surface close to the strawberry plants. The influence of no windbreak, 0.15 m and 0.30 m height windbreaks with varied air velocity of 0.5, 1.0 and 1.5 m/s were simulated in the study. The concentrations of supplied $CO_2$ within the plant canopy of were measured. To simplify the model, plants were not included in the final model. Considering 1.0m/s wind velocity which is the normal wind velocity of greenhouses, the concentrations of $CO_2$ were approximately 420, 580 and 653 ppm ($1{\times}10^{-9}kg/m^3$) for no windbreak, 0.15 and 0.30 m windbreak height, respectively. Considering that the maximum concentration of $CO_2$ for the strawberry plants was around 600-800 ppm, the 0.30 m windbreak height is highly recommended. This study revealed that the windbreak was very effective in preserving $CO_2$ gas within the plant canopy. More so, the study also proved that the CFD technique can be used to determine the concentration of $CO_2$ within the plant canopy for the plants consumption at any designed condition. For an in-depth application of this study, the plants as well as the different conditions for $CO_2$ utilization, etc. should be considered.

• The Korean Journal of Air & Space Law and Policy
• /
• v.33 no.2
• /
• pp.345-366
• /
• 2018

Article 37 of the International Convention on Civil Aviation requires that rules should be adopted to keep in compliance with international standards and recommended practices established by ICAO. As SARPs are revised annually, each ICAO Member State needs to reflect the new content in its national aviation Acts in a timely manner. In recent years, data-driven international standards have been developed because of the important roles of aviation safety data and information-based legislation in accident prevention based on human factors. The Safety Management System and crew Fatigue Risk Management Systems were reviewed as examples of the result of data-driven rulemaking. The safety management system was adopted in 2013 with the introduction of Annex 19 and Chapter 5 of the relevant manual describes safety data collection and analysis systems. Through analysis of safety data and information, decision makers can make informed data-driven decisions. The Republic of Korea introduced Safety Management System in accordance with Article 58 of the Aviation Safety Act for all airlines, maintenance companies, and airport corporations. To support the SMS, both mandatory reporting and voluntary safety reporting systems need to be in place. Up until now, the standard of administrative penal dispensation for violations of the safety management system has been very weak. Various regulations have been developed and implemented in the United States and Europe for the proper legislation of the safety management system. In the wake of the crash of the Colgan aircraft, the US Aviation Safety Committee recommended the US Federal Aviation Administration to establish a system that can identify and manage pilot fatigue hazards. In 2010, a notice of proposed rulemaking was issued by the Federal Aviation Administration and in 2011, the final rule was passed. The legislation was applied to help differentiate risk based on flight according to factors such as the pilot's duty starting time, the availability of the auxiliary crew, and the class of the rest facility. Numerous amounts data and information were analyzed during the rulemaking process, and reflected in the resultant regulations. A cost-benefit analysis, based on the data of the previous 10 year period, was conducted before the final legislation was reached and it was concluded that the cost benefits are positive. The Republic of Korea also currently has a clause on aviation safety legislation related to crew fatigue risk, where an airline can choose either to conform to the traditional flight time limitation standard or fatigue risk management system. In the United States, specifically for the purpose of data-driven rulemaking, the Airline Rulemaking Committee was formed, and operates in this capacity. Considering the advantageous results of the ARC in the US, and the D4S in Europe, this is a system that should definitely be introduced in Korea as well. A cost-benefit analysis is necessary, and can serve to strengthen the resulting legislation. In order to improve the effectiveness of data-based legislation, it is necessary to have reinforcement of experts and through them prepare a more detailed checklist of relevant variables.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.33 no.2
• /
• pp.103-111
• /
• 1988

This study was carried out with two cultivars under two levels of pH and four levels of nitrogen fertilization in a field and nutri-culture experiments to obtain the information about the effects of pH and nitrogen fertilization on the growth and yield of soybean. Acidic condition suppressed the growth of soybean plants, and thus yield and yield components of soybean decreased under acidic condition. But they increased with increased nitrogen fertilization. Especially, these respones were more remarkable under acidic condition and in the variety Jangbaegkong. Grain yield of soybean were highly correlated with the content of allantoin and total nitrogen of soybean plants in the variety Jangbaegkong, but this was not in the variety Danyeobkong. The content of protein and fat of soybean seeds decreased under acidic condition, and more nitrogen fertilization increased the protein content, but decreased the fat content.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.38 no.4
• /
• pp.350-359
• /
• 1993

The effect of gibberellic acid ($GA_3) and abscisic acid (ABA) on KCl-enhanced proteolysis of senescing leaves of rice(Oryza sativa L. cv. Chilsung) was studied. Emphasis was given to their effects on KCI-enhanced efflux of amino acids and proteinase activity. When treated singly, $GA_3 affected leaf proteolysis little, while ABA increased proteolysis, the rate of amino acid efflux, and ribulose -1,5 -bisphosphate carboxylase / oxygenase (Rubisco)-degrading endoproteinase activity. An additive increase in all three parameters mentioned above was observed when leaves were treated with ABA and KCl. No such an additive effect was found when $GA_3 was treated with KCl. Both $GA_3 and ABA helped to alleviate the KCI-suppressed activity of Rubisco-degrading exoproteinases. The additive increase in proteolysis of rice leaves in the presence of both ABA and KCl could thus be ascribed to a further increase in the efflux of protein hydrolyzates and Rubisco-degrading endoproteinase activity. An increase in proteolysis was accompanied by a decrease in water absorption, and the combined treatment of ABA with KCl resulted in a further reduction of water absorption.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.67-88
• /
• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.1-21
• /
• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.183-203
• /
• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.