• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.57-66
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• 2015

Considerable research efforts are being directed towards analyzing unstructured data such as text files and log files using commercial and noncommercial analytical tools. In particular, researchers are trying to extract meaningful knowledge through text mining in not only business but also many other areas such as politics, economics, and cultural studies. For instance, several studies have examined national pending issues by analyzing large volumes of text on various social issues. However, it is difficult to provide successful information services that can identify R&D documents on specific national pending issues. While users may specify certain keywords relating to national pending issues, they usually fail to retrieve appropriate R&D information primarily due to discrepancies between these terms and the corresponding terms actually used in the R&D documents. Thus, we need an intermediate logic to overcome these discrepancies, also to identify and package appropriate R&D information on specific national pending issues. To address this requirement, three methodologies are proposed in this study-a hybrid methodology for extracting and integrating keywords pertaining to national pending issues, a methodology for packaging R&D information that corresponds to national pending issues, and a methodology for constructing an associative issue network based on relevant R&D information. Data analysis techniques such as text mining, social network analysis, and association rules mining are utilized for establishing these methodologies. As the experiment result, the keyword enhancement rate by the proposed integration methodology reveals to be about 42.8%. For the second objective, three key analyses were conducted and a number of association rules between national pending issue keywords and R&D keywords were derived. The experiment regarding to the third objective, which is issue clustering based on R&D keywords is still in progress and expected to give tangible results in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.15-24
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• 2015

$CH_4$ is a trace gas and one of the key greenhouse gases, which requires continuous and systematic monitoring. The application of eddy covariance technique for $CH_4$ flux measurement requires a fast-response, laser-based spectroscopy. The eddy covariance measurements have been used to monitor $CO_2$ fluxes and their data processing procedures have been standardized and well documented. However, such processes for $CH_4$ fluxes are still lacking. In this note, we report the first measurement of $CH_4$ flux in a rice paddy by employing the eddy covariance technique with a recently commercialized wavelength modulation spectroscopy. $CH_4$ fluxes were measured for five consecutive days before and after the rice transplanting at the Gimje flux monitoring site in 2012. The commercially available $EddyPro^{TM}$ program was used to process these data, following the KoFlux protocol for data-processing. In this process, we quantified and documented the effects of three key corrections: (1) frequency response correction, (2) air density correction, and (3) spectroscopic correction. The effects of these corrections were different between daytime and nighttime, and their magnitudes were greater with larger $CH_4$ fluxes. Overall, the magnitude of $CH_4$ flux increased on average by 20-25% after the corrections. The National Center for AgroMeteorology (www.ncam.kr) will soon release an updated KoFlux program to public users, which includes the spectroscopic correction and the gap-filling of $CH_4$ flux.

• Korean Journal of Ecology and Environment
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• v.39 no.2 s.116
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• pp.187-197
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• 2006

A mathematical modeling program called Hydrological Simulation Program-FORTRAN (HSPF) developed by the United States Environmental Protection Agency(EPA) was applied to the Yongdam Watershed to examine its applicability for loading estimates in watershed scale. It was run under BASINS (Better Assessment Science for Integrating point and Nonpoint Sources) program, and the model was validated using monitoring data of 2002 ${\sim}$ 2003. The model efficiency of runoff was high in comparison between simulated and observed data, while it was relatively low in the water quality parameters. But its reliability and performance were within the expectation considering complexity of the watershed and pollutant sources and land uses intermixed in the watershed. The estimated pollutant load from Yongdam watershed for BOD, T-N and T-P was 1,290,804 kg $yr{-1}$, 3,753,750 kg $yr{-1}$ and 77,404 kg $yr{-1}$,respectively. Non-point source (NPS) contribution was high showing BOD 57.2%, T-N 92.0% and T-P 60.2% of the total annual loading in the study area. The NPS loading during the monsoon rainy season (June to September) was about 55 ${\sim}$ 72% of total NPS loading, and runoff volume was also in a similar rate (69%). However, water quality was not necessarily high during the rainy season, and showed a decreasing trend with increasing water flow. Overall, the BASINS/HSPF was applied to the Yongdam watershed successfully without difficulty, and it was found that the model could be used conveniently to assess watershed characteristics and to estimate pollutant loading in watershed scale.

• Proceedings of the Korean Geotechical Society Conference
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• 1998.05a
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• pp.35-81
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• 1998

Distinct Element Method(DEM) has a great advantage to model the discontinuous behaviour of jointed rock masses such as rotation, sliding, and separation of rock blocks. Geometrical data of joints by a field monitoring is not enough to model the jointed rock mass though the results of DE analysis for the jointed rock mass is most sensitive to the distributional properties of joints. Also, it is important to use a properly joint law in evaluating the stability of a jointed rock mass because the joint is considered as the contact between blocks in DEM. In this study, a stochastic modelling technique is developed and the dilatant rock joint is numerically modelled in order to consider th geometrical and mechanical properties of joints in DE analysis. The stochastic modelling technique provides a assemblage of rock blocks by reproducing the joint distribution from insufficient joint data. Numerical Modelling of joint dilatancy in a edge-edge contact of DEM enable to consider not only mechanical properties but also various boundary conditions of joint. Preprocess Procedure for a stochastic DE model is composed of a statistical process of raw data of joints, a joint generation, and a block boundary generation. This stochastic DE model is used to analyze the effect of deviations of geometrical joint parameters on .the behaviour of jointed rock masses. This modelling method may be one tool for the consistency of DE analysis because it keeps the objectivity of the numerical model. In the joint constitutive law with a dilatancy, the normal and shear behaviour of a joint are fully coupled due to dilatation. It is easy to quantify the input Parameters used in the joint law from laboratory tests. The boundary effect on the behaviour of a joint is verified from shear tests under CNL and CNS using the numerical model of a single joint. The numerical model developed is applied to jointed rock masses to evaluate the effect of joint dilation on tunnel stability.

• Journal of Oral Medicine and Pain
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• v.33 no.4
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• pp.305-316
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• 2008

The mandibular advancement device(MAD) has been used to help manage snoring and obstructive sleep apnea. The aims of this study were to specify the demographic and clinical characteristics of the patients receiving long-term treatment with MAD and to quantify the compliance with and side effects of the use of the device. Of 103 patients who were treated with MAD for at least one full year after delivery date, 49 were able to be contacted with telephone and complete follow-up questionnaires were obtainable. They were telephoned to determine whether they were still using the device. If not, they were asked when and why they stopped using it. Patients were also asked how much effectiveness of the MAD in decreasing snoring and how much they and their bed-partners were satisfied with the MAD therapy. The initial respiratory disturbance indices and pre-treatment snoring frequency and intensity were obtained from the medical records of initial visit. All the data were compared between users and nonusers. The results were as follows: 1. Of 49 patients 25 are still using the device, but 24 stopped using it. Among nonusers nobody stopped wearing the device within first 1 month, but 37.5% of nonusers stopped wearing it in the following 6 months, and another 4.2% before the end of the first year. 2. The one-year compliance of the MAD therapy was 79.59%. 3. There were no significant differences in mean age, mean body mass index, and gender distribution between users group and nonusers group. 4. There was no significant difference in mean respiratory disturbance index at initial visit between users group and nonusers group. 5. There was no significant difference in pre-treatment snoring frequency and intensity between users group and nonusers group. 6. The degree of decrease in snoring with use of MAD was significantly higher in the users when compared to nonusers. 7. Patient's overall satisfaction with treatment outcome was significantly higher in the users when compared to nonusers. 8. Bed partner's satisfaction with treatment outcome tended to be higher in the users when compared to nonusers. 9. The most frequent reasons why patients discontinued wearing the MAD were: jaw pain(25%), dental pain(20.83%), broken appliance(20.83%), hassle using(16.67%), lost weight(8.3%), dental work(8.3%), no or little effect(4.17%), sleep disturbance(4.27).

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.51-78
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• 2016

Online-to-offline (O2O) commerce is the new trend that merges online commerce with traditional industries in various fields. The primary purpose of this paper is to find out which factors influence customers' intention to switch from call-based driver-for-hire services to O2O app-based services. This study used variables and factors based on Theory of Switching Intention, and Extended Unified Theory of Acceptance and Use of Technology in order to design research questions. We surveyed 500 users of call-based driver-for-hire services. According to the result of this study, dissatisfaction with the current call-based driver-for-hire services is estimated to be a significant factor that strengthens customers' intention to switch from the call-based driver-for-hire services to the app-based services. Loyalty to the previous call-based driver-for-hire services was not seen as a crucial motivator that causes customers to switch to the new O2O driver service. Switching cost also did not play a key role in explaining the relationship between dissatisfaction with the current call-based service and the intention to use the new app-based service. Performance expectancy, easiness in use, the level of user's knowledge or available assistance in relation to the use of app-based services, and expectancy for reasonable price was found to have meaningful impacts on customers' intention to switch from the call-based driver-for-hire services to the app-based services. Age, gender and user experience on the new service were found incapable of moderating the relationship between aforementioned factors which influence customers' choice of the app-based driver-for-hire service, and customers' intent to switch to the app-based service.

• Atmosphere
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• v.22 no.4
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• pp.489-497
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• 2012

Recently released a top secret document explicitly shows that the early development plan for an earth observation satellite in the USA has a hidden and more important purpose for a concept of 'free space' than the scientific purpose. At that time, the hidden and secret concept imbedded within the early space development plan prevail other national policies of the USA government for purpose of the national security. Under these circumstances, it is quite reasonable to accept a possibility that the meteorological satellites which play a key role in the every area of meteorology and climatology was also born for the hidden purposes. Even it is so, it is quite amazing that the first meteorological satellite is launched in the USA despite of the facts that the major users of the meteorological satellites were not very enthusiastic with the meteorological satellite and the program was not started as a formal meteorological satellite project. This was only possible because of the external socio-political impact caused by the successful launch of the Russian Sputnik satellite and a few key policy developers who favored the meteorological satellite program. It is also interesting to note that the beginning of the first Korean meteorological satellite program was initiated by a similar socio-political influence occurred by the launch of a North Korean satellite.

• Cartoon and Animation Studies
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• s.28
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• pp.235-263
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• 2012

Regarding the theme park business as an area of cultural content business, this study focuses on the trend of pursuing indoor theme parks as a small-scale small capital strategy escaped from the existing approach oriented to large-scale outdoor complex theme parks. It is because although existing large-scale outdoor complex theme parks require the capital with the scale of hundreds of billion won and also high-level technique and the latest operational know-how that they have a great barrier for new entry as well as enormous risk, the rent indoor theme parks succeed in market entry with efficient risk management and flexible market strategies. Thereupon, this study examines the current status of the children's indoor theme park market with Korean characters as their theme as a new market among the indoor theme parks and also investigates the market strategies of this market in the two aspects of expansion: the expansion of Korean characters' property value and the expansion of the local theme park market. For that, this article reviewed the advanced researches on theme parks and divided the types of theme parks existing in Korea with the criteria of classification by space and theme or classification by main users. Also, among the children's indoor theme parks with Korean characters as their theme, this study visited five ones located in the capital area to examine the current status. And about two located in the capital area and also four in the local area, the current data were received from the persons in charge of the companies for analysis. Also, with the subjects of spectators visiting the 'DIBO VILLAGE, Cheonggye-cheon' newly opened on April 25th, 2012, the research on satisfaction was conducted for analysis. Through that, this study analyzed the structure of the existing children's indoor theme park business with Korean characters as their theme and suggested the ground to analyze the effectiveness of market strategies being implemented. It is expected that this study will establish the clues of systematic and profound discussion for the indoor theme park business that can be said to be the niche market of the theme park business and allow the small-scale areal indoor theme parks to be examined as a significant business model for the local theme park industry. In the aspect of character business as well, it is expected that this will give a chance to establish a new model of spatial storytelling expansion in terms of the property value of Korean animation characters.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.482-488
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• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.