• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.396-402
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• 2016

Recently, as the performance of smart phones has advanced and their distribution has increased, various functions in existing devices are accumulated. In particular, functions in smart devices have matured through improvement of diverse sensors. Various applications with the development of smart phones get fleshed out. As a result, services from applications promoting physical activity in users have gotten attention from the public. However, these services are about diet alone, and because these have no exercise motion recognition capability to detect movement in the correct position, the user has difficulty obtaining the benefits of exercise. In this paper, we develop exercise motion-recognition software that can sense the user's motion using a sensor built into a smart phone. In addition, we implement a system to offer exercise with friends who are connected via web server. The exercise motion recognition utilizes a Kalman filter algorithm to correct the user's motion data, and compared to data that exist in sampling, determines whether the user moves in the correct position by using a DTW algorithm.

• The Journal of Bigdata
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• v.4 no.1
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• pp.63-72
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• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• Convergence Security Journal
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• v.11 no.1
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• pp.71-77
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• 2011

NCW(Network Centric- Warfare) encompasses the concept to use computer data processing and network linkage communications techniques, share information and furthermore, enhance the effectiveness of computer-operating systems. As IT(Information & Technology) have become developed in the recent years, the existing warfare system-centered conventional protocol is not use any longer. Instead, network-based NCW is being widely-available, today. Under this changing computer environment, it becomes important to establish algorithm and build the stable communication systems. Tools to identify malign node factors through Wireless Ad-hoc network cause a tremendous error to analyze and use paths of even benign node factors misreported to prove false without testing or indentifying such factors to an adequate level. These things can become an obstacle in the process of creating the optimum network distribution environment. In this regard, this thesis is designed to test and identify paths of benign node factors and then, present techniques to transmit data through the most significant open short path, with the tool of MP-SAR Protocol, security path search provider, in Ad-hoc NCW environment. Such techniques functions to identify and test unnecessary paths of node factors, and thus, such technique users can give an easy access to benign paths of node factors.

• Korean Journal of Culture and Social Issue
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• v.19 no.2
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• pp.259-283
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• 2013

It seems that Internet based communication has been settled down in everyday life. Internet based communication studies also have been done and they proposed that internet based communication modal differs from other communications modal. One of the major themes about internet based communication was the effect of internet based communication on relationships. Early studies suggested that internet has negative effect on life and relationships, although it has positive effect on economics and information distribution. Because there is relative anonymity, People and Researchers thought that people easily could be exposed to negative situations like pornography, instant relationship, negative reply and soon. However,Recently there have been on going un-solving arguments about effect of internet based communication.From the negative perspective, Internet based communication is negative to relationship, because internet based communication could displace face to fact communication and old off-line relationships. However, from the positive perspective, researchers focused on the motivation and purpose of internet users. In this paradigm, people could expand their life and relationships using internet because internet could remove the various restrictions for relationship. Moreover they also suggested that people could enlarge their relationships because they could easily disclose theirselves in anonymity. However, No conclusion has been drawn yet and there needs some organization of two standpoints. Accordingly, This study is integrating the two perspectives and proposing future direction of internet based communication and relationship.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.157-172
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• 2011

The latest active studies on distributed OLAP to adopt a distributed environment are mainly focused on DHT P2P OLAP and Grid OLAP. However, these approaches have its weak points, the P2P OLAP has limitations to multidimensional range queries in the cloud computing environment due to the nature of structured P2P. On the other hand, the Grid OLAP has no regard for adjacency and time series. It focused on its own sub set lookup algorithm. To overcome the above limits, this paper proposes an efficient central managed P2P approach for a cloud computing environment. When a multi-level hybrid P2P method is combined with an index load distribution scheme, the performance of a multi-dimensional range query is enhanced. The proposed scheme makes the OLAP query results of a user to be able to reused by other users' volatile cube search. For this purpose, this paper examines the combination of an aggregation cube hierarchy tree, a quad-tree, and an interval-tree as an efficient index structure. As a result, the proposed cloud P2P OLAP scheme can manage the adjacency and time series factor of an OLAP query. The performance of the proposed scheme is analyzed by a series of experiments to identify its various characteristics.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.2
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• pp.126-133
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• 2014

The main source of bridge destruction is due to scour. The bridge scour is the result of erosive action of flowing water taking away ground materials from near the abutment or pier. Furthermore, the water level must be also monitored whiling flooding, because it dangers not only the stability of bridge itself, but the safety of bridge users. This study is intended to develop a new measuring system for bridge scour by overcoming the current limitation of scour measurement technique. This measuring system is confirmed its excellence and validity through this study. The newly developed measuring system finds the distance between the water surface and the ground surface by detecting temperature difference along the abutment vertically. The measuring mechanism for monitoring the bridge scour and water level is based on identifying the temperature difference among mediums, including air, water and ground. In order to validate the new measuring system, the lab experiments and the field tests are conducted and compared. It has been confirmed that this system can effectively measure the bridge scour and the water level by analyzing the temperature distribution between mediums and the temperature variation over time.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.97-111
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• 2008

Commercial data mining tools basically support binary attributes only in mining association rules, that is, they can mine binary association rules only. In general, however. transaction databases contain not only binary attributes but also quantitative attributes. Thus, in this paper we propose a systematic approach to mine quantitative association rules---association rules which contain quantitative attributes---using commercial mining tools. To achieve this goal, we first propose an overall working framework that mines quantitative association rules based on commercial mining tools. The proposed framework consists of two steps: 1) a pre-processing step which converts quantitative attributes into binary attributes and 2) a post-processing step which reconverts binary association rules into quantitative association rules. As the pre-processing step, we present the concept of domain partition, and based on the domain partition, we formally redefine the previous bipartition and multi-partition techniques, which are mean-based or median-based techniques for bipartition, and are equi-width or equi-depth techniques for multi-partition. These previous partition techniques, however, have the problem of not considering distribution characteristics of attribute values. To solve this problem, in this paper we propose an intuitive partition technique, named standard deviation minimization. In our standard deviation minimization, adjacent attributes are included in the same partition if the change of their standard deviations is small, but they are divided into different partitions if the change is large. We also propose the post-processing step that integrates binary association rules and reconverts them into the corresponding quantitative rules. Through extensive experiments, we argue that our framework works correctly, and we show that our standard deviation minimization is superior to other partition techniques. According to these results, we believe that our framework is practically applicable for naive users to mine quantitative association rules using commercial data mining tools.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.239-249
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• 2010

The Geostationary Ocean Color Imager (GOCI) data-processing system (GDPS), which is a software system for satellite data processing and analysis of the first geostationary ocean color observation satellite, has been developed concurrently with the development of th satellite. The GDPS has functions to generate level 2 and 3 oceanographic analytical data, from level 1B data that comprise the total radiance information, by programming a specialized atmospheric algorithm and oceanic analytical algorithms to the software module. The GDPS will be a multiversion system not only as a standard Korea Ocean Satellite Center(KOSC) operational system, but also as a basic GOCI data-processing system for researchers and other users. Additionally, the GDPS will be used to make the GOCI images available for distribution by satellite network, to calculate the lookup table for radiometric calibration coefficients, to divide/mosaic several region images, to analyze time-series satellite data. the developed GDPS system has satisfied the user requirement to complete data production within 30 minutes. This system is expected to be able to be an excellent tool for monitoring both long-term and short-term changes of ocean environmental characteristics.

• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.133-154
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• 2021

Today smart phone is the most common media with a vehicle called 'application'. In order to understand how media users select applications and build their repertoire, this study conducted two-step approach using big data from smart phone log for 4 weeks in November 2019, and finally classified 8 media repertoire groups. Each of the eight media repertoire groups showed differences in time spent of mobile application category compared to other groups, and also showed differences between groups in demographic distribution. In addition to the academic contribution of identifying the mobile application repertoire with large scale behavioral data, this study also has significance in proposing a two-step approach that overcomes 'outlier issue' in behavioral data by extracting prototype vectors using SOM (Sefl-Organized Map) and applying it to k-means clustering for optimization of the classification. The study is also meaningful in that it categorizes customers using e-commerce services, identifies customer structure based on behavioral data, and provides practical guides to e-commerce communities that execute appropriate services or marketing decisions for each customer group.

• Management & Information Systems Review
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• v.29 no.4
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• pp.123-142
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• 2010

This study suggest that firms can use online brand communities as an IMC tool to achieve high brand loyalty through marketer-controlled or loyal customer-controlled brand contacts. In this perspective, the online brand community as a marketing communication tool can help the firm in eliciting favorable responses from customers. This study finds that an online brand community, as a critical marketing promotion tool, helps a firm elicit favorable relationship with customers and build strong brand loyalty. In particular, this study suggests several important theoretical and managerial implications. First, this study confirm that "advertising usefulness" is the most powerful and important factor that affects cgerial 's positive emotionomehile "sales promotion usefulness" impacmehin "interactivity" but dies not impacmhin "cgerial iexperience"ltyevent usefulness" impacmehin "cgerial iexperience"but dies not impacmhin "interactivity." In addition, "cgerial iexperience" signifn "itly impacmehin "cgerial -to-cgerial iinteractivity." This indicates that online environment provides participapacmwith a fun and exciting environment. In that sense, enhancing the online brand community experiencemwould be a critical factor for building strong brand. Thi", mword of mouth can play a riclly important role in making many cgerial s to trust brand and to enhance online brand community loyalty. Web users are becoming web authoore owning and creating content limited only by their imaginations.