• Journal of Distribution Science
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• v.18 no.2
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• pp.69-77
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• 2020

Purpose - This paper is aimed to analyze the effects of the mandatory closing regulation targeting large retailers, which has been implemented since 2012 to protect small retailers. We examine the changes in consumers' choice of retailers and their purchasing patterns of agri-food following the implementation of such regulation. Research design, data, and methodology - Household spending patterns were identified through the historical data of household food purchase, consumer panel provided by the Rural Development Administration. Clustering was employed to determine the household spending patterns. Moreover, the different household spending patterns before and after the regulation were comparatively studied. The patterns of consumers' choice of retail stores and shopping baskets by the type of retailers, derived from the respective datasets before and after the regulation, were compared to analyze the effects of the regulation. Results -After the regulation, some consumers who used to shop at large retailers before the regulation changed their shopping places to small retailers. However, the product categories that consumers had mainly purchased before the regulation were rarely changed even after the regulation. Conclusions - Although the regulation helped migrate some of the consumers to small retailers, the regulation seemed to have failed to stimulate consumers to purchase the goods, normally bought at large retailers, from traditional markets. In other words, traditional markets are not effective substitutes for regulation-affected retailers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.139-144
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• 2011

Most real world system such as world economy, management, medical and engineering applications contain a series of complex phenomena. One of common methods to understand these system is to build a model and analyze the behavior of the system. As a first step, Determining the best clusters on data. As a second step, Determining the model of the cluster. In this paper, we investigated heuristic search methods for efficient clustering. It is also confirmed that the Bayesian Information Criterion more reliable than Cheeseman-Stutz ones.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.817-824
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• 2017

Residual stress is a critical element in determining the integrity of parts and the lifetime of welded structures. It is necessary to estimate the residual stress of a welding zone because residual stress is a major reason for the generation of primary water stress corrosion cracking in nuclear power plants. That is, it is necessary to estimate the distribution of the residual stress in welding of dissimilar metals under manifold welding conditions. In this study, a cascaded support vector regression (CSVR) model was presented to estimate the residual stress of a welding zone. The CSVR model was serially and consecutively structured in terms of SVR modules. Using numerical data obtained from finite element analysis by a subtractive clustering method, learning data that explained the characteristic behavior of the residual stress of a welding zone were selected to optimize the proposed model. The results suggest that the CSVR model yielded a better estimation performance when compared with a classic SVR model.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Asia Marketing Journal
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• v.17 no.1
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• pp.69-95
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• 2015

This paper examines a highly used social networking site (SNS) by studying the behavior of more than 11 million members over a 20 month period. The importance of the most highly active members to the overall network is demonstrated by the significant fraction of total visits by extremely active members in a given period but such members have surprisingly short lifespans (an average of only 2.5 months) as social hubs. We form and test a number of hypotheses concerning these social hubs and the determinants of their lifespan. We find that the speed of achieving social hub status increases the lifespan of a social hub. The norm of reciprocity is strongly confirmed to be present in the social hub population as visits are reciprocated. We also find that increasing local dominance in terms of activities over neighboring agents leads to a longer lifespan of a social hub. Contrary to expectations, local clustering in the vicinity of social hubs is smaller (rather than larger) than overall clustering. We discuss managerial implications in the paper.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.55.5-56
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• 2017

Galaxy dynamics probes weak gravity at accelerations below the de Sitter scale of acceleration adS = cH, where c is the velocity of light and H is the Hubble parameter. Low and high redshift galaxies hereby offer a novel probe of weak gravity in an evolving cosmology, satisfying H(z) = H0(1 + A(6z + 12z^2 +12z^3+ 6z^4+ (6/5)z^5)/(1 + z) with baryonic matter content A sans tension to H0 in surveys of the Local Universe. Galaxy rotation curves show anomalous galaxy dynamics in weak gravity aN < adS across a transition radius r beyond about 5 kpc for galaxy mass of 1e11 solar mass. where aN is the Newtonian acceleration based on baryonic matter content. We identify this behavior with a holographic origin of inertia from entanglement entropy, that introduces a C0 onset across aN=adS with asymptotic behavior described by a Milgrom parameter satisfying a0=omega/(2pi), where omega=sqrt(1-q)H is a fundamental eigenfrequency of the cosmological horizon. Extending an earlier confrontation with data covering 0.003 < aN/adS < 1 at redshift z about zero in Lellie et al. (2016), the modest anomalous behavior in the Genzel et al. sample at redshifts 0.854 < z <2.282 is found to be mostly due to clustering 0.36 < aN/adS < 1 close to the C0 onset to weak gravity and an increase of up to 65% in a0.

• Journal of Applied Reliability
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• v.18 no.1
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• Journal of the Korean Society of Costume
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• v.24
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• pp.217-233
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• 1995

The ultimate purpose of this study is to suggest the most effective marketing strategy for the clothing consumer market based on the new generation consumer's clothing selection behavior analysis. In this thesis, it is appempted to make a progress in the new gen-eration consumer's clothing life style types, in clothing purchase behavior analysis among the clothing life style, and also in the marketing strategy for marketers. The subjects selected for the final analysis are 412 the new gerneration women of age 20 thru 34 in seoul and satellite town area. Data were processed the spss package program. As for the analytic method, factor analysis, clustering analysis, XCross-tubulation, F-test with ANOVA, frequency and percentage were applied in the survey. The major findings are as following : life style is classified into four types : The characteristic fashion-directory type(25.7%) ; The reason traditional type(9.0%) ; The sen-sitivity fashion-following type(11.0%) ; The community brand-conscious type(54.3%). 2 Clothing life style types characteristic of the new generation consumer proved that clothing life style types are a significant difference according to the life style, the fashion consciousness and the average monthly spend-ing on clothing. 3. There is an important discrimination according to the clothing life style types in their clothing purchase behavior such as infor-mation usage, clothing choice criterion and brand loyalty. 4. Based on the result of our analysis and the review of literature, the marketing strategy is suggested that characteristic and new design development is efficient way to consumer's purchase need. Therefore apparel industary which pursue an added value must frame marketing strategy on the basis of the target consumer's sensitivity characteristic according to the life style and fashion consciousness.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2A
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• pp.71-77
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• 2011

In this paper elasto-plastic dynamic behavior of solid is analyzed by using smoothed particle hydrodynamics (SPH) without tensile instability which caused by a clustering of SPH particles. In solid body computations, the instability may corrupt physical behavior by numerical fragmentation which, in some cases of elastic or brittle solids, is so severe that the dynamics of the system is completely wrong. The instability removed by using an artificial stress which introduces negligible errors in long-wavelength modes. Applications to several test problems show that the artificial stress works effectively. These problems include the collision of rubber cylinders, fracture and crack of plate.

• Journal of Korean Academy of Nursing Administration
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• v.10 no.2
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• pp.255-264
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• 2004

Purpose: The Purpose of this study was to identify the relationship between 7S factors of the nursing organizational culture and organizational effectiveness. Method: The data were gathered from the self-reported questionnaires of 717 nurses who work for eight different general hospitals located around Seoul and Kyounggi province. The period of data collection was from November 12 to December 7, 2002. For data analysis, descriptive statistics, clustering analysis, and t-test with SPSS Program were used. Result: The nurses who highly perceived 7S factors of nursing organizational culture showed higher job satisfaction and organizational commitment in comparison with the nurses who lowly perceived 7S factors of nursing organizational culture. And the nurses who highly perceived 7S factors of nursing organizational culture showed higher organizational citizenship behavior in comparison with the nurses who lowly perceived 7S factors of nursing organizational culture. Among subdimension of organizational citizenship behavior, altruism and civic virtue were significant. Conclusion: From the above results, the high group with 7S factors of nursing organizational culture has strong culture, therefore nursing organization with strong culture is very implicative to enhance the organizational effectiveness.