• Journal of Broadcast Engineering
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• v.8 no.4
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• pp.325-338
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• 2003

Video adaptation in response to dynamic resource conditions and user preferences is required as a key technology to enable universal multimedia access (UMA) through heterogeneous networks by a multitude of devices In a seamless way. Although many adaptation techniques exist, selections of appropriate adaptations among multiple choices that would satisfy given constraints are often ad hoc. To provide a systematic solution, we present a general conceptual framework to model video entity, adaptation, resource, utility, and relations among them. It allows for formulation of various adaptation problems as resource-constrained utility maximization. We apply the framework to a practical case of dynamic bit rate adaptation of MPEG-4 video streams by employing combination of frame dropping and DCT coefficient dropping. Furthermore, we present a descriptor, which has been accepted as a part of MPEG-21 Digital Item Adaptation (DIA), for supporting terminal and network quality of service (QoS) in an interoperable manner. Experiments are presented to demonstrate the feasibility of the presented framework using the descriptor.

• Journal of Broadcast Engineering
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• v.13 no.2
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• pp.200-213
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• 2008

H.264| MPEG-4 AVC is a new video codingstandard defined by JVT (Joint Video Team) which consists of ITU-T and ISO/IEC. Many techniques are adopted fur the compression efficiency: Especially, an intra prediction in an inter frame is one example but it leads to excessive amount of encoding time due to the decision of a candidate mode and a RDcost calculation. For this reason, a fast determination of the best intra prediction mode is the main issue for saving the encoding time. In this paper, by using the result of statistical relation between intra $16{\times}16$ and $4{\times}4$ intra predictions, the number of candidate modes for $4{\times}4$ intra prediction is reduced. Firstly, utilizing motion vector obtained after inter prediction, prediction of a block mode for each macroblock is made. If an intra prediction is needed, the correlation table between $16{\times}16$ and $4{\times}4$ intra predicted modes is created using the probability during each I frame-coding process. Secondly, using this result, the candidate modes for a $4{\times}4$ intra prediction that reaches a predefined specific probability value are only considered in the same GOP For the experiments, JM11.0, the reference software of H.264|MPEG-4 AVC is used and the experimental results show that the encoding time could be reduced by 51.24% in maximum with negligible amounts of PSNR drop and bitrate increase.

• Journal of Bio-Environment Control
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• v.14 no.2
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• pp.106-113
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• 2005

This study was carried out to develop a novel hydroponic medium far fruit vegetable crops by using waste synthetic fibers. In physical analysis of the synthetic fiber medium (SFM), the bulk density and percent solid phase were lower, while the porosity and water content were greater in comparison with the rockwool slab. The SFM had pH of 6.5 and EC of $0.03dS{\cdot}m^{-1}$ both of which are similar to those of the rockwool slab. The CEC of 0.39me/100mL of the SFM was lower than compared with 3.29me/100mL of the rockwool slab. However, concentrations K, Ca, Mg and Na were slightly higher in the SFM than those in the rockwool slab. The 'Momotaro' tomato crop in the SFM gave comparable plant height, stem diameter, days to first flowering, fruit weight and percent marketable yield as the rockwool slab. In the SFM and in the rockwool slab, mean fiuit weight were 182g and 181g, percent marketable yield were $93.8\%$ and $92.0\%$, respectively. The marketable yield per 10a in the SFM was 12,799 kg, which was $97\%$ of that in the rockwool slab. Growth parameters such as leaf length and width, leaf number, stem diameter and chlorophyll content of an exportable cucumber crop grown in the SFM and the rockwool slab were not different. Fruit weight was greater in the rockwool slab, while percent marketable yield was greater in the SFM. The marketable fruit yield per 10a of 5,062kg in the SFM was $2\%$ greater than that in the rockwool slab. $NO_3$ concentration in nutrient solution during the crop cultivation was higher in the SFM than in the rockwool slab, while concentrations $NH_4$, K, Ca, Mg and $SO_4$ were not different between the two media.

• The Korean Journal of Pesticide Science
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• v.4 no.3
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• pp.19-26
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• 2000

In order to select potent bioactive isolates, 70 Fusarium isolates obtained from soil and 21 plant species were screened by antifungal, insecticidal, herbicidal, and duckweed bioassays after culturing in potato dextrose broth and rice solid media. Eight (11.4%) of the 70 liquid broth cultures showed disease-controlling activities more than 80% against at least one of the 6 plant diseases tested. Fusarium sp. FO-68 isolate exhibited the most potent antifungal activity; it controlled rice blast, wheat leaf rust, and barley powdery mildew with control values more than 95%. Out of 70 solid cultures, 21 (30.0%) controlled at least one plant disease more than 80% and F. equiseti FO-68 isolate showed disease-controlling activities more than 95% against 3 plant diseases such as rice blast, tomato late blight, and wheat leaf rust. As for tile insecticidal activities, 2 liquid and 1 solid cultures showed potent insecticidal activities against pest insects more than 80%, Liquid cultures of F. oxysporum FO-61 and Fusarium sp. FO-80 isolates exhibited insecticidal activities more than 80% against green peach aphid and diamondback moth, respectively. The solid culture of Fusarium sp. FO-510 isolate had 80% insecticidal activity against green peach aphid. However, none of liquid and solid cultures of the 70 Fusarium isolates showed potent herbicidal activities against 10 upland weeds. As the results of duckweed assay, 3 liquid cultures showed 70% growth inhibitory activity at concentrations less than 1.25% of culture supernatants and 9 solid cultures had a potent inhibitory activity against duckweed growth. On the other hand, there was a significant correlation between antifungal activities and herbicidal activities against duckweed of both liquid and solid cultures of tile 70 Fusarium isolates.

• Cartoon and Animation Studies
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• s.39
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• pp.413-450
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• 2015

Although cartoons have been recognized for their great potential and value, they have failed to bloom in Korea. This is because wrong perception and irregular distribution of cartoons have been repeated for the last several years. Presently, however, cartoons are escaping from chronic problems they have had for long and welcoming splendid chances now. From the mid- and late-1990's, there have been large-scale events having cartoons as their theme, and social recognition on cartoons is becoming more and more positive. Their contents are diversified, readers are increased, and they are escaping from stereotypes through harmony with other media. Lately, either large or small exhibitions for cartoons are being planned, and Korean cartoons are going overseas and producing exhibitions there. Particularly, visitors' appreciative eye is getting keener, and they begin to see them not as a genre underestimated as low culture like in the past but as a kind of art on which independent research is being actively conducted. One of the biggest factors that have allowed cartoons to be positioned as visual art is the form of exhibitions that combine them with other genres artistically. Especially the cartoon exhibitions being held these days are aggressively introducing various elements of the cartoon genre through the medium of exhibitions not just as a mere tool of seeing to help understand cartoon writers or works. The genre of cartoons is now regarded as an active subject that can reflect its own unique essence in this rapidly changing cultural environment and extend the range of it itself. The latest cartoon exhibitions are characterized by trans-genre and complex aspects in terms of their direction or organization according to the contents, space, or theme. This trend of cartoon exhibitions implies that they are subdividing, analyzing, and planning various factors not in a horizontal way that was centered around image as in the past. It means that cartoon exhibitions are evolving as a form of mobilizing, combining, and reproducing various methods. Although a number of cartoon exhibitions are being held with a variety of themes, there is still lack of research on cartoon exhibitions concerning their forms and contents. Therefore, this researcher sees cartoon exhibitions as a factor that allows cartoons to escape from negative recognition and examines various cartoon exhibitions, from Seoul International Cartoon Animation Festival to the ones that are recently held, to figure out the meaning of Korean cartoon exhibitions. Furthermore, this researcher will find out the factors of planning and popularity in international exhibitions or personal cartoon exhibitions being presently held and figure out new directions and potentials for Korean cartoon exhibitions based on that. To meet the needs of visitors whose expectations have become even higher, it is needed to try not just previous methods but experimental and original planning as well constantly. To realize that, it is necessary to keep providing a field of opportunity where cartoon works, cartoon writers, and visitors can communicate as in an exhibition. It is expected that this study will trigger research on cartoon exhibitions to be performed multilaterally and produce new discourse on cartoon exhibitions afterwards.

• Cartoon and Animation Studies
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• s.39
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• pp.261-295
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• 2015

Cyber singer appeared in the late 1990s has disappeared briefly appeared. although a few attempts in the 2000s, it did not show significant successes. cyber singer was born thanks to the technical development of the IT industry and the emergence of an idol training system in the music industry. It was developed by Vocaloid 'Seeyou' starting from 'Adam'. cyber singer that differenatiated typical digital characters in a cartoon or game may be subject to idolize to the music as a medium. They also feature forming a plurality of fandom. therefore, such attempts and repeated failures, this could be considered a fashion, but it flew content creation and ongoing attempts to take advantage of the new media, such as Vocaloid can see that there are expectations for a true Cyber-born singer. Early-Cyber singer is made only resemble human appearance, but 'Sciart' and 'Seeyou' has been evolving to becoming more like the human capabilities. in this paper, stylized cyber singer had disappeared in the past in the process of developing the technology to evolve into own artificial life does not end in failure cases, gradually led to a change in public perceptions of the image look looking machine was an attempt in that sense. With the direction of the evolution of the mechanical function to obtain a human, fun and human exchanges and mutual feelings. And it is equipped with an artificial life form that evolved with it only in appearance and function. in order to support this logic, I refer to the study of the coevolution of man and machine at every Bruce Mazlish. And, I have analyzed the evolution of cyber singer Bruce research from the perspective of the development process since the late 1990s, the planning of the eight singers who have appeared and design of the cyber character and important voices to be evaluated as a singer (vocal). The machine has been evolving coevolution with humans. cyber singer ambivalent development targets are recognized, but strive to become the new artificial creatures of horror idea of human desire and death continues. therefore, the new Cyber-organisms are likely to be the same style as 'Seeyou'. because, cartoon forms and whirring voice may not be in the form of a signifier is the real human desires, but this is because the contemporary public's desire to be desired and the technical development of this type can be created at the point where the cross-signifier.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.12a
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• pp.543-559
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• 2003

The information drive of the businesses requires new alternatives in that the promotion of business efficiency through information process technologies ends up conflicting with the protection of information human rights on laborers’side. Nevertheless, apathy on information protection has a tendency to be distorted by the efficiency of the businesses. Should the capital and mass media warn economic red lights, political circles with uneasiness would ignore the significance of information protection on the behalf of business efficiency. Therefore, the importance of information protection is considered a smaller interest than that of business efficiency with the infringements of human rights on laborers’side arising. Informatization of the businesses along with the developments of information process technologies has enabled the management to monitor and control the behaviors of laborers. This new problem needs to establish both information protection mechanism and institutional devices to regulate those labor controls. The security of business activity without human rights infringement warrants both basic rights of the public and spirit of the Constitution. The study suggests the establishment and revision of laws suitable to the period of information human rights. On top of that, the establishment of the basic law for information protection of individuals’with the common principle that integrates the related laws and rules on-off line is needed. This will warrant the active participation of labor unions and create specific alternatives for information protection.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.