• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• Journal of Ginseng Culture
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• v.4
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• pp.103-127
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• 2022

The influx of modern culture in the early 20th century in Korea led to numerous changes in the country's ginseng industry. With the development of ginseng cultivation technology and commerce, the production and consumption of ginseng increased, and various ginseng products were developed using modern manufacturing technology. Consequently, competition for the sales of these products became fierce. At that time, newspaper advertisements showed detailed trends in the development and sales competition of ginseng products. Before 1920, however, there were few advertisements of ginseng in newspapers. This is thought to be because newspapers had not yet been generalized, and the ginseng industry had not developed that much. Ginseng advertisements started to revitalize in the early 1920s after the launch of the Korean daily newspapers Dong-A Ilbo and Chosun Ilbo. Such advertisements in this period focused on emphasizing the traditional efficacy of Oriental medicine and the mysterious effects of ginseng. There were many advertisements for products that prescribed the combination of ginseng and deer antler, indicating the great popularity of this prescription at the time. Furthermore, advertisements showed many personal experience stories about taking such products. Mail order and telemarketing sales were already widely used in the 1920s . In 1925, there were advertisements that ginseng products were delivered every day. The advertisements revealed that ginseng roots were classified more elaborately than they are now according to size and quality. Ginseng products in the 1920s did not deviate significantly from the scope of traditional Oriental medicine formulations such as liquid medicine, pill, and concentrated extract. In the 1930s, ginseng advertisements became more active. At this time, experts such as university professors and doctors in medicine or in pharmacy appeared in the advertisements. They recommended ginseng products or explained the ingredients and medicinal effects of the products. Even their experimental notes based on scientific research results appeared in the advertisements to enhance the reliability of the ginseng products. In 1931, modern tablet advertisements appeared. Ginseng products supplemented with vitamins and other specific ingredients as well as ginseng thin rice gruel for the sick appeared at this time. In 1938, ginseng advertisements became more popular, and advertisements using talents as models, such as dancer Choi Seunghee or famous movie stars, models appeared. Ginseng advertisements in the 1920s and 1930s clearly show a side of our rapidly changing society at the time.

• Korean Journal of Heritage: History & Science
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• v.39
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• pp.351-378
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• 2006

With the development of media, the methods for the documentation of intangible cultural heritage have been also developed and diversified. As well as the previous analogue ways of documentation, the have been recently applying new multi-media technologies focusing on digital pictures, sound sources, movies, etc. Among the new technologies, the documentation of intangible cultural heritage using the method of 'Motion Capture' has proved itself prominent especially in the fields that require three-dimensional documentation such as dances and performances. Motion Capture refers to the documentation technology which records the signals of the time varing positions derived from the sensors equipped on the surface of an object. It converts the signals from the sensors into digital data which can be plotted as points on the virtual coordinates of the computer and records the movement of the points during a certain period of time, as the object moves. It produces scientific data for the preservation of intangible cultural heritage, by displaying digital data which represents the virtual motion of a holder of an intangible cultural heritage. National Research Institute of Cultural Properties (NRICP) has been working on for the development of new documentation method for the Important Intangible Cultural Heritage designated by Korean government. This is to be done using 'motion capture' equipments which are also widely used for the computer graphics in movie or game industries. This project is designed to apply the motion capture technology for 3 years- from 2005 to 2007 - for 11 performances from 7 traditional dances of which body gestures have considerable values among the Important Intangible Cultural Heritage performances. This is to be supported by lottery funds. In 2005, the first year of the project, accumulated were data of single dances, such as Seungmu (monk's dance), Salpuri(a solo dance for spiritual cleansing dance), Taepyeongmu (dance of peace), which are relatively easy in terms of performing skills. In 2006, group dances, such as Jinju Geommu (Jinju sword dance), Seungjeonmu (dance for victory), Cheoyongmu (dance of Lord Cheoyong), etc., will be documented. In the last year of the project, 2007, education programme for comparative studies, analysis and transmission of intangible cultural heritage and three-dimensional contents for public service will be devised, based on the accumulated data, as well as the documentation of Hakyeonhwadae Habseolmu (crane dance combined with the lotus blossom dance). By describing the processes and results of motion capture documentation of Salpuri dance (Lee Mae-bang), Taepyeongmu (Kang seon-young) and Seungmu (Lee Mae-bang, Lee Ae-ju and Jung Jae-man) conducted in 2005, this report introduces a new approach for the documentation of intangible cultural heritage. During the first year of the project, two questions have been raised. First, how can we capture motions of a holder (dancer) without cutoffs during quite a long performance? After many times of tests, the motion capture system proved itself stable with continuous results. Second, how can we reproduce the accurate motion without the re-targeting process? The project re-created the most accurate motion of the dancer's gestures, applying the new technology to drew out the shape of the dancers's body digital data before the motion capture process for the first time in Korea. The accurate three-dimensional body models for four holders obtained by the body scanning enhanced the accuracy of the motion capture of the dance.

• Journal of Science Education
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• v.35 no.1
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• pp.1-12
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• 2011

The purpose of this study was to examine the effects of science class using the multimedia materials on high school students' attitude toward science. The subjects were 222 high school students. For this study, 11th graders at a high school were assigned to a comparison group and an experimental group. The experimental group was received science class using multimedia materials for 3 months. The research design was pretest-posttest control group design, the data were analyzed using PASW statistics 18.0 program. The types of multimedia materials used in experimental group were science fiction movies, science documentaries, TV programs, and Power Point presentations created by students. Before and after treatment, the attitude toward science tests were administered. Pre-tests and post-test score differences between 2 groups were analyzed by ANCOVA. The differences of attitude toward science based on gender were compared by analysis of covariance. And the perception on science class with multimedia materials were also investigated. The results of this study were as follows: First, the attitude toward science was improved significantly after applying science classes using multimedia materials. Especially, there were significant difference between pre-test and post-test in the score of attitude toward science class and attitude toward science content which were sub-area of attitude toward science. Second, there was no significant difference between female and male students in total score of attitude toward science. However, the attitude toward science, scientists and society, which was a sub-area of attitude toward science, female students scored significantly higher than male students. Third, 84% student showed a positive perception that the science class enhanced their interest in science. 69% the students responded that we had thought about Science-Technology-Society. Multimedia material types which the students prefered were science fiction movie, science documentaries, science TV programs, respectively.

• Journal of Korean Classical Literature and Education
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• no.32
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• pp.193-224
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• 2016

The North Korean animation is a puppet movie that is adapted The Brother and Sister Who Became the Sun and the Moon, a traditional Korean lore. The quality of this animation is acknowledged because of not only North Korea's considerably advanced animation technology but also the animation's retention of the folklore's traditional essence rather than intention to disseminate ideological propaganda. Nevertheless, the animation reveals the trasformation of its original purpose from general educative intentions for children to the educative concept of salvation by heaven is replaced by salvation by people and cultural education insteadof salvation by heaven. The appearance of the hero Jangsoe is the key adaptation of this animation, and it suggests the main principal of salvation lies in man rather than in heaven. Such adaptation complies with the requirements of children's literature suggested by the North Korea's literary history office. Furthemore the hero Jangsoe as the examplary figure of revolutionary self-reliance ideology and as a leader. Theory of self-reliance literature stipulates that children's literature is used for ideological education that develops people to be successors of revolutionary feats and become active workers for the construction of socialism and communism, therefore it is possible to understand the purpose of the adaptation to reflect the educational aims. This study investigates the change in meaning form the original folktale through such adaptation, and highlights problems related to limiting the meaning implied in "heaven's salvation" in the original story only to the vague meaning of religious hope. This vague implied meaning is considered as "an awareness activity to examine their own existence in the universe". With regard to this, the concept of heaven's salvation that is prevalent in the classic stories can be interpreted as a positive self-belief that enables the use of rationality in any helpless situation that cannot be understood with existing empirical knowledge. It considers that heaven expresses the power that exists in the human mind through self-viability and self-belief. This creates the power of reason in the character to fight against the evil disguised as the mother, in the absence of the real mother.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.55-68
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• 2021

The purpose of this study is to analyze the characters and narratives of series films and to extract corresponding elements of film music to compare and analyze how musical elements were utilized. The scope of the study was analyzing the story structure and characters of the films "Mary Poppins" and "Mary Poppins Returns" and the corresponding film music. After comparing the contents of the film "Mary Poppins" and "Mary Poppins Returns," the film matched the film music equivalent to the similar scenes of the two films. As a result, seven of the 11 songs of "Mary Poppins" overlap with those used in similar narratives of "Mary Poppins Returns", and eight songs overlap in "Mary Poppins Returns". Seven songs from "Mary Poppins" and eight songs from "Mary Poppins Returns" can be divided into nine scenes in total when connected to a common narrative. Among them, "A Spoonful of Sugar" from "Mary Poppins", "Jolly Holiday", "A cover is not the Book" from "Mary Poppins Returns" and "Triple light fantastic" were overlapping songs with narratives. Based on this, it analyzes leitmotiv film music, focusing on characters from the films "Mary Poppins" and "Mary Poppins Returns." The common leitmotivs in the two films are Mary Poppins leitmotiv, Lesson leitmotiv, Lullaby leitmotiv, World leitmotiv, Chimney Sweeper leitmotiv, Up & Down leitmotiv, Chimney Sweeper leitmotiv, and Sky leitmotiv. The characteristic rhythm and pitch used in Mary Poppins leitmotivs were used in the overall song featuring Mary Poppins. Through this, the elements of music symbolizing Mary Poppins, a key figure, were matched to the films "Mary Poppins" and "Mary Poppins Returns" and modified according to the narrative flow. The analysis results of this work have theoretical significance in that it is necessary to analyze the narratives and film music of series films to discover common features and consider how they are matched in theoretical terms.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used