• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.241-243
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• 2018

In this paper, in Simultaneous localization and mapping (SLAM), the robot acquire its map of environment while simultaneously localizing itself relative to the map. Cloud based SLAM, allows us to optimizing resource and data sharing like map of the environment, which allows us, as one of shared available online map. Doing so, unless we add or remove significant change in our environment, the essence of rebuilding new environmental map are omitted to new mobile robot added to the environment. As result, the requirement of additional sensor are curtailed.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.428-435
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• 2016

Hundreds of millions of new posts and information are being uploaded and propagated everyday on Online Social Networks(OSN) like Twitter, Facebook, or Instagram. This paper proposes and implements a GPS-location based SNS data mapping, analysis, and visualization system, called Smart SNS Map, which collects SNS data from Twitter and Instagram using hundreds of PlanetLab nodes distributed across the globe. Like no other previous systems, our system uniquely supports a variety of functions, including GPS-location based mapping of collected tweets and Instagram photos, keyword-based tweet or photo searching, real-time heat-map visualization of tweets and instagram photos, sentiment analysis, word cloud visualization, etc. Overall, a system like this, admittedly still in a prototype phase though, is expected to serve a role as a sort of social weather station sooner or later, which will help people understand what are happening around the SNS users, systems, society, and how they feel about them, as well as how they change over time and/or space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.583-600
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• 2014

Social media is considered a valuable platform for gathering and analyzing the collective and subconscious opinions of people in Internet and mobile environments, where they express, explicitly and implicitly, their daily preferences for brands and products. Extracting and tracking the various attitudes and concerns that people express through social media could enable us to categorize brands and decipher individuals' cognitive decision-making structure in their choice of brands. We investigate the cognitive network structure of consumers by building an inter-category map through the mining of big data. In so doing, we create an improved online recommendation model. Building on economic sociology theory, we suggest a framework for revealing collective preference by analyzing the patterns of brand names that users frequently mention in the online public sphere. We expect that our study will be useful for those conducting theoretical research on digital marketing strategies and doing practical work on branding strategies.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.863-868
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• 2002

In this paper we suggested a map management method for on line RPG supporting full 3D. In general, it is possible to support full 3D with client engine. Rut the online game server cannot show the expected performance under normal price constrains with current hardware technology, since the amount of data for management of 3D on-line game server increase exponentially with respect to the size of game world. To solve this problem. we introduced the "special object" which makes it possible for on-line game server with low performance hardware. This suggested method gave concrete from to manage full 3D for server as well as clients.s clients.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.535-544
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• 2018

As the spread of mobile devices equipped with various sensors and high-quality wireless network communications functionsexpands, the amount of spatio-temporal data generated from mobile devices in various service fields is rapidly increasing. In conventional research into processing a large amount of real-time spatio-temporal streams, it is very difficult to apply a Hadoop-based spatial big data system, designed to be a batch processing platform, to a real-time service for spatio-temporal data streams. This paper extends the MapReduce online framework to support real-time query processing for continuous-input, spatio-temporal data streams, and proposes a load management method to distribute overloads for efficient query processing. The proposed scheme shows a dynamic load balancing method for the nodes based on the inflow rate and the load factor of the input data based on the space partition. Experiments show that it is possible to support efficient query processing by distributing the spatial data stream in the corresponding area to the shared resources when load management in a specific area is required.

• Journal of Information Technology Services
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• v.5 no.3
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• pp.137-150
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• 2006

The authors map some of the current Business Models in the Massively Multiplayer Online Player scenario. These maps represent Value Creation Systems by resorting to Value Net constructs and notations, and are offered here as a proof of concept and utility. The authors claim that these mappings can enable readers, managers and IT experts, to build new insights onto such Business Models and develop requirements for Information System infrastructure. When approaching the Value Creation System as a Value Net the goal is to think outside the conceptual box of Value Chains and understand how the different activities interact, by exposing the multiplicity of value types and flows. In doing this study the authors are attempting to synthesize a new Business Model proposal that could underlie the development of an infrastructure for the collaborative creation, distribution and exploration of online massively multiplayer games, beyond the traditional producer-consumer roles.

• Annual Conference of KIPS
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• 2013.05a
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• pp.179-182
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• 2013

We develop Framework architecture of Online Shopping Services based on M2M and IoT for Handheld Devices in Cloud Computing. MapReduce model will be used as a method to simplify large scale data processing when user search for purchasing products online which provide efficient, and fast respond time. Therefore, providing user with a enhanced Quality of Experience (QoE) as well as Quality of Service (QoS) when purchasing/searching products Online from big data.

• Journal of the Korea Fashion and Costume Design Association
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• v.20 no.4
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• pp.57-71
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• 2018

Since consumers have become more comfortable with providing and receiving information online, 'online word of mouth' has been gaining consideration as one of the major information sources. Also, the shopping orientation of consumers has been proven to be an important determinant of consumer behavior. Therefore, the study investigated the differences in online WOM behavior based on shopping orientation. Hedonic, loyal, and syntonic styles were the types of shopping orientation considered, and the study focused on information retrieval tendencies, the motivation of online WOM search, searching online WOM sources, and the contents for the online WOM behavior. The research conducted an off-line survey targeting females in their twenties. The total number of data sets used in the empirical study was 125, and these were analyzed by SPSS 20.0: factors analysis, Cronbach's ${\alpha}$, k-means cluster, ANOVA, Duncan's multiple range test, Kruskal-Wallis, Mann-Whitney, and Bonferroni correction. The participants were divided into 3 kinds of shopping orientation groups named 'trend-pursuit', 'passive', and 'loyal'. As a result, there were significant differences in online WOM behavior discovered between the groups. Firstly, the 'trend-pursuit' group had the highest number of ongoing searches while the 'loyal' group had the highest number of pre-purchase search. Secondly, the 'trend-pursuit' and 'loyal' groups both had the motivations of online WOM search, hedonic and utility, whereas the 'passive' group had the lowest motivations for both motivations. Thirdly, the 'loyal' group frequently referred to reviews on shopping malls as online WOM sources. The research provided a better understanding of the online WOM behavior of present consumers and suggests that fashion related corporations map out marketing strategies with the understanding of these behaviors.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.603-611
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• 2003

This paper proposes an effective feature compensation scheme based on speech model for achieving robust speech recognition. Conventional model-based method requires off-line training with noisy speech database and is not suitable for online adaptation. In the proposed scheme, we can relax the off-line training with noisy speech database by employing the parallel model combination technique for estimation of correction factors. Applying the model combination process over to the mixture model alone as opposed to entire HMM makes the online model combination possible. Exploiting the availability of noise model from off-line sources, we accomplish the online adaptation via MAP (Maximum A Posteriori) estimation. In addition, the online channel estimation procedure is induced within the proposed framework. For more efficient implementation, we propose a selective model combination which leads to reduction or the computational complexities. The representative experimental results indicate that the suggested algorithm is effective in realizing robust speech recognition under the combined adverse conditions of additive background noise and channel distortion.

• The Research Journal of the Costume Culture
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• v.27 no.5
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• pp.496-511
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• 2019

The purpose of this study was to create a theoretical structure for the concept of purchasing risks by identifying the structure of purchasing risks that lead to obstacles in the purchasing decisions of consumers in fashion consumption via online channels. This was a secondary research using books, articles, prior researches, and academic journals on the five topics of "characteristics of fashion consumption," "the concept of purchasing risks," "purchasing risks by product types," "purchasing risks by channel types," and "purchasing risks of fashion consumption on online shopping channels." According to the arguments of prior researches, the study divided the purchasing risks of fashion consumption through online shopping into four categories : (1) fundamental purchasing risks including financial risk and time loss risk pertaining to any product or channel, (2) online channel purchase risks, which include risks in payment, Information leaks, and delivery and return/exchange risk, (3) fashion product risk related to product quality or experience of other people, which includes social risks and risks associated with quality, and (4) the online channel${\times}$fashion product risks, which include the aesthetic and psychological hazards especially amplified in online channels. The four risk factors were then described with a concept map to systemize the multi-dimensional and stereoscopic psychological structure of purchasing risks. Of the four risk factors, consumers placed the most emphasis on the online channel${\times}$fashion product risks, hence, reducing this risk factor is of utmost priority for marketing of online shopping channels.