• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.391-401
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• 2004

In this paper, we introduce a novel coding method to efficiently enable spatial random access for high resolution video. In terms of resolution and display size, standard display devices (such as cathode-ray tubes. monitors. PDAs, and LCDs) do not sufficiently support high resolution video such as digital cinema and panoramic video. Currently, users have no choice but to view video at lower resolution as a result of down-sampling, or only a partial region of the video due to display size limitations. Our proposed method. which we call the B-map, represents the set of starting locations of the coded segments in a picture frame. This information, or B-map, is first sent to the decoder prior to the coded data stream of the frame and is then used for fast local decoding. To test our method, we compare our B-map with JPEG tiling and the JPEG Resynchronization marker. Experimental results show that the proposed coding method requires less overhead than existing methods during the same decoding time. The results show promise for future panoramic or digital cinema applications.

• Journal of Environmental Impact Assessment
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• v.28 no.1
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• pp.23-34
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• 2019

Global warming becomes a serious issue that poses subsidiary issues like a sea level rise or a capricious climate over the world. Because of severe heat-wave of the summer in Korea in 2016, a big attention has been focused on urban heat island since then. Not just about heat-wave itself, many researches have been concentrated on how to adapt in this trendy warming climate and weather in a small scope. A big part of existing studies is mitigating "Urban Heat Island effect" and that is because of huge impervious surface in urban area where highly populated areas do diverse activities. It is a serious problem that this thermal context has a high possibility causing mortality by heat vulnerability. However, there have been many articles of a green infrastructures' cooling impact in summer. This research pays attention to measure cooling effect of a street planting considering urban canyon and type of green infrastructures in neighborhood scale. This quantitative approach was proceeded by ENVI-met simulation with a spatial scope of a commercial block in Seoul, Korea. We found the dense double-row planting is more sensitive to change in temperature than that of the single-row. Among the double-row planting scenarios, shrubs which have narrow space between the plant and the land surface were found to store heat inside during the daytime and prevent emitting heat so as to have a higher temperature at night. The quantifying an amount of vegetated spaces' cooling effect research is expected to contribute to a study of the cost and benefit for the planting scenarios' assessment in the future.

• Information Systems Review
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• v.21 no.2
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• pp.117-137
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• 2019

In the Big Data era, data science has become popular with the production of numerous data in various domains, and the power of data has become a competitive power. There is a growing interest in unstructured data, which accounts for more than 80% of the world's data. Along with the everyday use of social media, most of the unstructured data is in the form of text data and plays an important role in various areas such as marketing, finance, and distribution. However, text mining using social media is difficult to access and difficult to use compared to data mining using numerical data. Thus, this study aims to develop Korean Natural Language Application (KoALA) as an integrated application for easy and handy social media text mining without relying on programming language or high-level hardware or solution. KoALA is a specialized application for social media text mining. It is an integrated application that can analyze both Korean and English. KoALA handles the entire process from data collection to preprocessing, analysis and visualization. This paper describes the process of designing, implementing, and applying KoALA applications using the design science methodology. Lastly, we will discuss practical use of KoALA through a block-chain business case. Through this paper, we hope to popularize social media text mining and utilize it for practical and academic use in various domains.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Progress in Medical Physics
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• v.20 no.4
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• pp.277-289
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• 2009

Recently, functional MRI has been used to investigate the neurobiological mechanisms of acupuncture and the specificity of acupoint. The group data tend to be regarded as more important than the individual data in the most of previous studies. This study was designed to investigate the effect of the variability of individual data on the group results. A functional MRI (fMRI) of the whole brain was performed in fifteen healthy subjects during placebo and acupuncture stimulations at the ST36 acupoint. After remaining at rest for 30 seconds, the acupuncture was inserted and twisted at the rate of 2 Hz for 45 seconds and then the acupuncture was removed immediately. This process was repeated three times. Individual and group analyses were performed by voxel-based analyses using SPM2 software. Visual inspections of the activation and deactivation maps from individual sessions have shown the large variability across fifteen subjects. This means that the group data reflected the brain activation responses of only a few subjects. We suggest that the individual data should be presented to demonstrate the effect of acupuncture.

• Cartoon and Animation Studies
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• s.29
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• pp.209-241
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• 2012

Is a failed blockbuster film? Is there no room for reconsideration of the value of the film in terms of its contents and forms? The purpose of this study is to answer these questions. In 2007, SHIM Hyung-rae's was in the limelight due to the nationalist discourse around the film rather than evaluation of the film itself. In terms of its narrative and formal properties, the film showed the difference from the Korean nationalist blockbuster films. It led to the disaccord and hard-to-understand results of having somewhat disappointed box-office success of 8,500,000 audiences in comparison to the input, of receiving well by a generous part of the audiences absorbed by nationalism, and of getting the critics' cynic criticism of the film's cinematic value. Eventually only provided the cultural battlefield of nationalism, was left as an unnoticed film in the realm of industry and criticism. However, it was interesting that there was a common ground between the film's supporters and the cynic critics. Both sides were being acknowledged that the spectacle of was way out of proportion to the degree that the spectacle was unbalanced with the story unfolding, achieved more than expected. Its spectacle overwhelming the narrative enfever a few audiences, and at the same time, it provided some reasons making critics face away from the film. In this context, the purpose of this study is to examine 's aesthetics that 'the spectacle dominating narrative' or 'the narrative as a pretext for showing spectacle,' leading to discussion of artistic/theoretical/critical value and to find out cinematic value of the film being regarded as a failure. In addition, this study is significant in that it suggests that is a new kind of moving image that it cannot be analyzed with existed critical methods of narrative film criticism; as a result, this study provides the chance to be evaluated through a new conceptual frame of the film. In order to grasp the narratological aesthetics, this study focuses on the concept of trickality that Andre Gaudreault suggests, and Tom Gunning's 'the cinema of attractions,' referring to the spectacle-oriented narrativity or the mode of production displaying the spectacle more than the narrative.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.57-75
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• 2019

In this paper, we conducted a study focusing on the innovation direction of the documentary model on the Internet of Things industry, which is the most actively industrialized among the core technologies of the 4th Industrial Revolution. Policy, economic, social, and technical issues were derived using PEST analysis for global trend analysis. It also presented future prospects for the Internet of Things industry of ICT-related global research institutes such as Gartner and International Data Corporation. Global research institutes predicted that competition in network technologies will be an issue for industrial Internet (IIoST) and IoT (Internet of Things) based on infrastructure and platforms. As a result of the PEST analysis, developed countries are pushing policies to respond to the fourth industrial revolution through cooperation of private (business/ research institutes) led by the government. It was also in the process of expanding related R&D budgets and establishing related policies in South Korea. On the economic side, the growth tax of the related industries (based on the aggregate value of the market) and the performance of the entity were reviewed. The growth of industries related to the fourth industrial revolution in advanced countries overseas was found to be faster than other industries, while in Korea, the growth of the "technical hardware and equipment" and "communication service" sectors was relatively low among industries related to the fourth industrial revolution. On the social side, it is expected to cause enormous ripple effects across society, largely due to changes in technology and industrial structure, changes in employment structure, changes in job volume, etc. On the technical side, changes were taking place in each industry, representing the health and medical sectors and manufacturing sectors, which were rapidly changing as they merged with the technology of the Fourth Industrial Revolution. In this paper, various management methodologies for innovation of existing business model were reviewed to cope with rapidly changing industrial environment due to the fourth industrial revolution. In addition, four criteria were established to select a management model to cope with the new business environment: 'Applicability', 'Agility', 'Diversity' and 'Connectivity'. The expert survey results in an AHP analysis showing that Business Model Canvas is best suited for business model innovation methodology. The results showed very high importance, 42.5 percent in terms of "Applicability", 48.1 percent in terms of "Agility", 47.6 percent in terms of "diversity" and 42.9 percent in terms of "connectivity." Thus, it was selected as a model that could be diversely applied according to the industrial ecology and paradigm shift. Business Model Canvas is a relatively recent management strategy that identifies the value of a business model through a nine-block approach as a methodology for business model innovation. It identifies the value of a business model through nine block approaches and covers the four key areas of business: customer, order, infrastructure, and business feasibility analysis. In the paper, the expansion and application direction of the nine blocks were presented from the perspective of the IoT company (ICT). In conclusion, the discussion of which Business Model Canvas models will be applied in the ICT convergence industry is described. Based on the nine blocks, if appropriate applications are carried out to suit the characteristics of the target company, various applications are possible, such as integration and removal of five blocks, seven blocks and so on, and segmentation of blocks that fit the characteristics. Future research needs to develop customized business innovation methodologies for Internet of Things companies, or those that are performing Internet-based services. In addition, in this study, the Business Model Canvas model was derived from expert opinion as a useful tool for innovation. For the expansion and demonstration of the research, a study on the usability of presenting detailed implementation strategies, such as various model application cases and application models for actual companies, is needed.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.