• Journal of the East Asian Society of Dietary Life
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• v.25 no.1
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• pp.183-192
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• 2015

This study aimed to produce fermented extracts with sugar made from lotus root (LR) and Platycodon grandiflorum (PG), using lactic acid fermentation, and confirmed their physiological and anti-oxidative activities as basic data for manufacturing functional drinks through sensory tests. For the final sugar concentrations, PG showed $48.1^{\circ}brix$ and LR showed $52.0^{\circ}brix$. Sugar concentrations during lactic acid fermentation following dilution of sugar to $12^{\circ}brix$, ranged from $11.5{\sim}12.1^{\circ}brix$ for PG and $11.9{\sim}12.4^{\circ}C$ for LR. During lactic acid fermentation, lactic acid bacteria numbers tended to decrease in both fermented LR and PG extracts with sugar as the fermentation period increased. For DPPH radical scavenging ability, LR was three times higher in control without lactic acid fermentation while PG showed significant increases in L. acidophilus (77%), L. brevis (90%), and L. delbrueckii (177%) during lactic acid fermentation. For total polyphenol content, LR showed a higher concentration than PG, and except for fermented L. delbrueckii extract showing similarity with the control, contents of fermented extracts decreased. In the case of PG, CUPRAC, increased significantly in L. brevis, whereas FRAP, increased significantly in L. delbrueckii with lactic acid fermentation. For reducing power, except for fermentation with L. brevis, all PG showed lower reducing power activities. In the sensory test of fermented LR and PG extracts with sugar, both fermented extracts showed better results with L. brevis or L. delbrueckii than control or those with L. acidophilus in every item. Based on these results, it is highly possible to develop fermented extract drinks with sugar using LR or PG. In particular, lactic acid bacteria such as L. delbrueckii and L. brevis showed generally higher activities with potential as a functional drink.

• Conservation Science in Museum
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• v.26
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• pp.161-182
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• 2021

A jungwangu, a type of medium-sized mortar, is a firearm with a barrel and a bowl-shaped projectileloading component. A bigyeokjincheonroe (bombshell) or a danseok (stone ball) could be used as a projectile. According to the Hwaposik eonhae (Korean Translation of the Method of Production and Use of Artillery, 1635) by Yi Seo, mortars were classified into four types according to its size: large, medium, small, or extra-small. A total of three mortars from the Joseon period have survived, including one large mortar (Treasure No. 857) and two medium versions (Treasure Nos. 858 and 859). In this study, the production method for medium mortars was investigated based on scientific analysis of the two extant medium mortars, respectively housed in the Jinju National Museum (Treasure No. 858) and the Korea Naval Academy Museum (Treasure No. 859). Since only two medium mortars remain in Korea, detailed specifications were compared between them based on precise 3D scanning information of the items, and the measurements were compared with the figures in relevant records from the period. According to the investigation, the two mortars showed only a minute difference in overall size but their weight differed by 5,507 grams. In particular, the location of the wick hole and the length of the handle were distinct. The extant medium mortars are highly similar to the specifications listed in the Hwaposik eonhae. The composition of the medium mortars was analyzed and compared with other bronze gunpowder weapons. The surface composition analysis showed that the medium mortars were made of a ternary alloy of Cu-Sn-Pb with average respective proportions of (wt%) 85.24, 10.16, and 2.98. The material composition of the medium mortars was very similar to the average composition of the small gun from the Joseon period analyzed in previous research. It also showed a similarity with that of bronze gun-metal from medieval Europe. The casting technique was investigated based on a casting defect on the surface and the CT image. Judging by the mold line on the side, it appears that they were made in a piece-mold wherein the mold was halved and using a vertical design with molten metal poured through the end of the chamber and the muzzle was at the bottom. Chaplets, an auxiliary device that fixed the mold and the core to the barrel wall, were identified, which may have been applied to maintain the uniformity of the barrel wall. While the two medium mortars (Treasure Nos. 858 and 859) are highly similar to each other in appearance, considering the difference in the arrangement of the chaplets between the two items it is likely that a different mold design was used for each item.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.