• Cartoon and Animation Studies
• /
• s.33
• /
• pp.597-616
• /
• 2013

Character industry is not only knowledge-based industry chiefly with copyrights but also motive power for creative economy to take a role functionally over the fields of industries because it has industrial characteristic as complement product to promote sale value in manufacturing industry and service industry and increase profit on sales. Since 2003, the national policy related to character has aimed to maximize effect among connected industries, extend its business abroad, enforce copyrights through the improvement of marketing system, develop industrial infrastructure through raising quality of character products. With the result of this policy, the successful cases of connected contents have been crystallized and domestic character industry has stepped up methodically since 2007. It is needed to reset the scales of character industry and industrial stats because there are more know-how of self industry promotion and more related characters through strategy of market departmentalization starting with cartoon, animation, games, novels, movies and musicals. Especially, The Korea government set our target for 'Global Top Five Character Power' since 2009 and has started to carry out to find global star characters, support to establish network among connected industries, diversify promotion channels, and develop licensing business. Particularly, since 2013, There have been prospered the indoor character theme park with time management just like character experimental marketing or Kids cafes using characters, the demand market of digital character focusing on SNS emoticon, and the performance market for character musical consistently. Moreover, The domestic and foreign illegal black markets on off-line have been enlarged, so we need another policy alternative. To prepare for the era of exploding character demand market and diversifying platform, it is needed to set up a solid strategy that is required the elements of policy changes in character industry to vitalize character industry and support new character design and connected contents. the following shows that the elements of policy changes related to the existing policy, the current position of market. Nowadays, the elements of policy changes in domestic character industry are that variety of consumers in the digital character market according to platform diversification, Convergence contents of character goods for the Korean waves, legalization of the illegal black contents market, and controling the tendency of consumers in departmentalized market. This can help find the policy issue entirely deferent with the existing character powers like US, Japan or Europe. In its final analysis, the alternatives are the promotion of models with contract copyrights of domestic and foreign connected contents, the diversification of profit models of platform economy, the additive development of target market related to enlarging the Korean waves, and the strategy of character market for the age-specific tendency according to developing character demand market.

• Food Science and Preservation
• /
• v.20 no.6
• /
• pp.751-759
• /
• 2013

There is increasing interest in freshly cut products, that is, foods produced without washing and cutting. In this study, the quality of freshly cut sliced Deodeok was compared with that of what based on its washing methods. In bubble washing, the Deodeok rises to the water surface apace and is broken into centimeter sizes. Microbubble washing calls for the production of a great number of 0.1 mm-sized bubbles in anions-bearing water and their passing through a trumpet-shaped hole at a high pressure. To compare the product deterioration rates of the specimens, they were stored at $10^{\circ}C$ for 10 days. In the specimens washed with the control method and with hand washing, the deterioration rate was 80%; and in the specimens washed with bubble and microbubble washing, 20~30%. The L-value (an index of browning) was higher in the bubble and microbubble washing than in the control and the hand washing, which implies that browning was minimized during the storage. As for the viable cell and coliform group counts that were measured during the storage, the specimens washed with the control method showed the highest values. In contrast, the specimens washed with microbubble washing showed the lowest values. In the sensory test, the specimens washed with microbubble were highest in storage preference. In conclusion, the Deodeok that was stored after it was washed with microbubble washing was found to have had the best quality.

• International Commerce and Information Review
• /
• v.16 no.4
• /
• pp.191-225
• /
• 2014

Most of all, this paper analyzes the current situation of EU(European Union) and ascertain EU's economic condition in terms of tariff lines and non-tariff barriers. and the purpose of this article is to find out the problems of EU's tariff lines and non-tariff barriers. Next, We suggest some future direction of export promotion from Korea to EU more largely for our companies. First, this paper describes the characteristics and outline of EU. The EU is a politico-economic union of 28 member states that are primarily located in Europe. The EU traces its origins from the European Coal and Steel Community(ECSC) and the European Economic Community(EEC), formed by the Inner Six countries in 1951 and 1958, respectively. After that, The Maastricht Treaty established the European Union under its current name in 1993. The latest major amendment to the constitutional basis of the EU, the Treaty of Lisbon, came into force in 2009. There are a combined population of over 500 million inhabitants and generated a nominal gross domestic product(GDP) of 16.692 trillion US dollars in EU. The results are as follows ; First of all, In terms of tariff lines and customs duties, Our companies have to know precisely EU's real tariff lines and other customs duties, and such as value added tax and exercise tax, corporate tax regulated by EU commission and EU's 28 members. second, our companies have to confirm EU's non-tariff barriers. such as RoHS, WEEE, REACH. These non-tariff barriers could be hindrances or obstacles to trade with foreign companies in other countries. We perceive all companies exporting to EU are related with these Technical Barriers to Trade irrespective of their nationality. So, Our companies fulfill the requirements of EU Commission concerning safety, health, environment etc. Also, Our companies choose market-driven strategy to export more largely than before in the field of marketing and logistics.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.113-127
• /
• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.59 no.4
• /
• pp.511-520
• /
• 2014

Kenaf (Hibiscus cannabinus L.) was recognized as a potential source of forage. To reduce the production cost, we should insure large cultivation area. The one of the best candidate places to expand the useful kenaf production was 'Saemangeum' reclaimed land. To confirm the possibility of kenaf growth in reclaimed land, we seeding and cultivated the kenaf in 'Saemangeum'. The germination percentage of kenaf on 5.0 dS/m soil salinity was 18%. It is less 66% than that of 4.0 dS/m soil salinity and at 6.0 dS/m, the germination percentage of kenaf was under 10%. The growth and development of kenaf in reclaimed land grew worse with increasing soil salinity. The stem diameter which the most important factor that decide the value and yield of product was upper 2.6 cm when soil salinity maintained under 4.0 dS/m, but if soil salinity marked over 4.0 dS/m, the stem diameter of kenaf was drop under 2.0 cm and it deteriorate the number of leaves per plant by 20~46%. The necrosis on older tip and marginal leaves were noted approximately first month after seeding which was correlated directly with the salinity levels of reclaimed soil. Reduction of total yield was coincide with increasing levels of EC. If soil salinity over 5.0 dS/m, the amount of decreased by soil salinity was 51% than that of non-reclaimed region. The allowable soil salinity level of which could be maintained within 20% reduction rate was 4.2 dS/m. Consequently kenaf can be grown successfully with moderately saline soil condition. However, salt levels in excess of 4.2 dS/m severely have restricted plant growth and development and will result in significant yield reduction.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_3
• /
• pp.1173-1185
• /
• 2019

We, for the first time, retrieved sulfur dioxide (SO₂) vertical column density (VCD) in industrial and volcanic areas from TROPOspheric Monitoring Instrument (TROPOMI) using the Principle component analysis(PCA) algorithm. Furthermore, SO₂ VCDs retrieved by the PCA algorithm from TROPOMI raw data were compared with those retrieved by the Differential Optical Absorption Spectroscopy (DOAS) algorithm (TROPOMI Level 2 SO₂ product). In East Asia, where large amounts of SO₂ are released to the surface due to anthropogenic source such as fossil fuels, the mean value of SO₂ VCD retrieved by the PCA (DOAS) algorithm was shown to be 0.05 DU (-0.02 DU). The correlation between SO₂ VCD retrieved by the PCA algorithm and those retrieved by the DOAS algorithm were shown to be low (slope = 0.64; correlation coefficient (R) = 0.51) for cloudy condition. However, with cloud fraction of less than 0.5, the slope and correlation coefficient between the two outputs were increased to 0.68 and 0.61, respectively. It means that the SO₂ retrieval sensitivity to surface is reduced when the cloud fraction is high in both algorithms. Furthermore, the correlation between volcanic SO₂ VCD retrieved by the PCA algorithm and those retrieved by the DOAS algorithm is shown to be high (R = 0.90) for cloudy condition. This good agreement between both data sets for volcanic SO₂ is thought to be due to the higher accuracy of the satellite-based SO₂ VCD retrieval for SO₂ which is mainly distributed in the upper troposphere or lower stratosphere in volcanic region.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.93-110
• /
• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of Korean Society of Forest Science
• /
• v.105 no.1
• /
• pp.93-102
• /
• 2016

This research was conducted for major cultivated chestnut (Castanea spp.) in Korea to compare chestnut quality characteristics according to storage temperatures ($4^{\circ}C$ vs. $-1^{\circ}C$) and cultivars. Color, hardness, soluble solids content (SSC), rate of decay and sensory evaluation were investigated during 16 weeks storage. Cultivars 'Tsukuba' and 'Ishizuchi' showed the least pericarp color change while cultivars 'Riheiguri' and 'Daebo' showed the most pericarp color change during storage. Chestnut fruits stored at $-1^{\circ}C$ showed less pericarp color change than those stored at $4^{\circ}C$. Cultivars 'Arima' and 'Tanzawa' exhibited the decrease tendency of hardness with lowest hardness during storage at $-1^{\circ}C$. Chestnut fruits stored at $4^{\circ}C$ showed high hardness than those stored at $-1^{\circ}C$. Cultivars 'Riheiguri' showed the highest increase of SSC, while cultivar 'Arima' showed the lowest increase of SSC after 16 weeks of storage. The SSC increased in nuts from all cultivars at both temperatures, but nuts stored at $-1^{\circ}C$ showed higher increases in SSC than nuts stored at $4^{\circ}C$. Cultivars 'Ishizuchi' and 'Riheiguri' showed high rates of decayed nuts in contrast to cultivars 'Daebo', 'Okkwang' and 'Tanzawa' that showed low rates of decayed nuts during storage. The chestnut fruit stored at $-1^{\circ}C$ showed less decay than fruit stored at $4^{\circ}C$. Texture and sweetness were tested for sensory evaluation. Among the tested cultivars, 'Riheiguri', 'Daebo' and 'Tsukuba' showed hard texture and very sweetness, while 'Tanzawa' showed relatively soft texture. 'Arima' and 'Okkwang' showed weak sweetness during storage. Nuts stored at $4^{\circ}C$ exhibited harder texture than nuts stored at $-1^{\circ}C$ while $-1^{\circ}C$ exhibited more sweetness than nuts stored at $4^{\circ}C$. Chestnuts for hard texture and short-term storage (less than one month), $4^{\circ}C$ will be a proper storage temperature, while in order to store long-term (more than 4 month), $-1^{\circ}C$ will be a proper storage temperature. Result from this study provide base-line data of postharvest management for Korean cultivated chestnut as well as contributing increased product value and income for chestnut producers.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.38 no.7
• /
• pp.935-945
• /
• 2009

This study was performed to find the optimum ratio of ingredients in the Chinese quince jam. The experiment was designed according to the D-optimal design of mixture design, which included 14 experimental points with 4 replicates for three independent variables (Chinese quince paste $45{\sim}60%$, pectin $1.5{\sim}4.5%$, sugar $45.5{\sim}63.5%$). A mathematical analytical tool was employed for the optimization of typical ingredients. The canonical form and trace plot showed the influence of each ingredient in the mixture against final product. By use of F-test, sweetness, pH, L, b, ${\Delta}E$, and firmness were expressed by a linear model, while the spreadmeter value, a, and sensory characteristics (appearance, color, smell, taste, and overall acceptability) were by a quadratic model. The optimum formulations by numerical and graphical method were similar: Chinese quince paste 54.48%, pectin 2.45%, and sugar 53.07%. Optimum ingredient formulation is expected to improve use of Chinese quince and contribute to commercialization of high quality Chinese quince jam.

• Analytical Science and Technology
• /
• v.25 no.6
• /
• pp.447-459
• /
• 2012

In this study, the emission characteristics of volatile and odorant species released from salted meat product (Spam) was investigated as a function of time. Gas samples released from Spam samples were analyzed for volatile organic compounds (VOC) and sulfur compounds (RSC) at five different times for the elapsed (E) days of 0, 1, 3, 6, and 9 (E-0 to E-9) by GC/MS and GC/PFPD system, respectively. Results indicated that reduced sulfur, aldehyde, and ketone groups were the dominant odorants. Especially, hydrogen sulfide was the predominant in concentration and odor activity value (OAV) during the fresh stage. Its concentration was 1465 ${\mu}g/m^3$ (60.0% of the total mass) in E-0 and 455 ${\mu}g/m^3$ (28.0%) in E-1, while its OAV was 19.4 (78.3%: E-0) and 6.02 (41.7%: E-1). On the other hand, the concentration of acetone showed the maximum values in the decaying stage (E-3: 451 (43.2%), E-6: 369 (64.2%), and E-9: 1150 ${\mu}g/m^3$ (70.2%)). Furthermore, the concentration of 2,3-butanedione was also detected considerably from decaying sample (E-3: 17.6 (1.68%), E-6: 16.1 (2.80%), and E-9: 179 ${\mu}g/m^3$ (10.9%)). However, OAV of acetone was insignificant (<0.01%) in the decaying stage, while that of 2,3-butanedione was relatively high in the range of 1.14-11.6 (14.5-76.2% of ${\Sigma}OAV$). It thus confirmed that the major odorant groups generated from Spam samples changed with the progress of decay such as sulfur (fresh stage), aldehyde (intermediate stage), and ketone compounds (decaying stage).