• The Journal of Information Systems
• /
• v.22 no.3
• /
• pp.1-24
• /
• 2013

SNS(Social Network Services) is being recognized as an important part in our society, individual lives and corporate business aspects, and the influence of SNS is growing explosively as expansion and supply of infrastructures that support mobile environments increase. Previous studies related to SNS were focused on user acceptance of new technology, based on Technology Acceptance Model(TAM). However, they had a limitation to focus on technology acceptance, without the consideration of personal and behavioral factors in SNS use. However, above all, successful SNS requires the understanding of users who are active on the network. Therefore, from the user's perspective, this study attempted a multi-dimensional approach by reflecting characteristics that come from SNS usage. This study considered user innovation, virtual skill, self-efficacy, social pressure and network effect as independent variables, and perceived ease-of-use, perceived usefulness and perceived value as mediating variables, and intention-to-use as dependent variable. The result showed that user innovation, self-efficacy, social pressure and network effect had a significant effect on the mediating variables. The practical contribution of this study is to suggest useful decision alternatives concerned to marketing strategy for acquiring and retaining lone-term customers related to SNS business.

• Journal of Korean Society for Quality Management
• /
• v.29 no.4
• /
• pp.38-53
• /
• 2001

In resolving industrial quality control problems, a vector of multiple quality characteristic variables is involved rather than a single variable. However, it is not guaranteed that a multivariate control chart based on statistical methods can monitor abnormal signal in case that small changes of relationship between each variables causes abnormal production process. Hence a quality control system for real-time monitoring of the multi-dimensional quality characteristic vector under a multivariate normal process is needed to enhance tile production system quality performance. A pattern analysis approach based on self-organizing map (SOM), an unsupervised learning technique of neural network, is applied to the design of such a quality control system. In this study we present a new material quality control system based on pattern analysis approach and illustrate the effectiveness of proposed system using actual electronic company material data.

• Journal of Technology Innovation
• /
• v.25 no.4
• /
• pp.211-238
• /
• 2017

This study was conducted using social network analysis, variance analysis, and regression analysis to investigate the effects of cooperation between innovators on technical performance. Based on data of joint applicants filed with the Korean Intellectual Property Office from 2009 to 2012, we derive network structural variables and characteristic variables, and identify network characteristics that affect the overall network structural type, roles for each subject of innovation, and innovation performance. The findings are as follows: ⅰ) The network of this study is a distributed, small-world network within which relatively small groups of innovators are distributed. ⅱ) Universities were found to play the most important role in cooperation, but diversity of cooperating partners exhibited similar effects. ⅲ) It was shown that access to quick and accurate knowledge from familiar partners exhibits a more positive influence on generating innovative performance than unfamiliar knowledge from a variety of cooperating partners in non-familiar fields.

• Korean Journal of Health Education and Promotion
• /
• v.19 no.2
• /
• pp.73-98
• /
• 2002

This study was carried out to investigate the relationship between social support, social network and health behaviors as surveyed by cross-sectional study in 744 rural people aged above 30 of a community dwelling sample of one county for 6 days of July in 2000. Objectives of this study was in order to establish an effective health promotion. The sample was accrued by face to face interview of direct visiting from clustered sampling method. Interview was conducted by trained medical students with the questionnaire consisted of socio-demographic data, health behavior, social support and social network based on previous literature. The summarized results were as follows: 1. There were significant difference in the level of social support and social network by general characteristic variables except occupation and residency type(p〈0.05). 2. There were significant difference in knowledge about hypertension, smoking status, status of physical exercise, diet patterns by social support and social network in spite of variation of social support and social network subconcept(p〈0.05). And there were significant difference in alcohol drinking status, body weight control and diet pattern according to level of social network(p〈0.05). But smoking status by social support and network results opposite direction(p〈0.05). 3. There were no regular or consistent result in the relationship between social support, social network and health behavior. 4. Major predictors for health behavior on the multiple logistic regression that included general characteristic, social support and social network were age, instrumental social support and worry about health. Significant variables of multiple logistic regression for health behavior that included social support(instrumental and emotional) and social network were instrumental social support and social network. These results suggest that only a instrumental element and social network may be associated with health behavior. Inconsistent with prior research in these some item, a positive consistent relationship was not found between social support, social network and health behavior. So the study should be replicated to determined the reliability of our findings.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.05a
• /
• pp.1661-1662
• /
• 2006

Internet communication network quality characteristics are analyzed using Beta probability distribution. Beta probability distribution is chosen for the underlying probability distribution because it is an extremely flexible probability distribution used to model bounded random variables. Based on the fitted Beta probability distribution, a dataset regarding each network quality characteristic is summarized concisely.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1998.04a
• /
• pp.542-547
• /
• 1998

As the concern for a global energy conservation and environmental protection are increasing, it has been more important thing to correspond with CFC depletion. Alternate refrigerants have merit such as lower global warming effect, but also have demerits such as lower efficiency, miscibility, increasing noise and poor reliability problems. Then we have to develop more efficient, silent and robust compressors to satisfying world-wide demand. In this paper, parametric study on rotary compressor muffler for a room air-conditioner was carried out to investigate the effect of important design variables on noise by using Taguchi robust design method with signal-to-noise(S/N) ratio. Taguchi method seems to be helpful for finding optimum value of design variables for noise level. We also applied neural network to find optimal value of design variables.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.6
• /
• pp.374-382
• /
• 2020

The purpose of this study is to improve the learning speed of an ammunition stockpile reliability classification artificial neural network model by proposing a normalization method that reduces the number of input variables based on the characteristic of Ammunition Stockpile Reliability Program (ASRP) data without loss of classification performance. Ammunition's performance requirements are specified in the Korea Defense Specification (KDS) and Ammunition Stockpile reliability Test Procedure (ASTP). Based on the characteristic of the ASRP data, input variables can be normalized to estimate the lot percent nonconforming or failure rate. To maintain the unitary hypercube condition of the input variables, min-max normalization method is also used. Area Under the ROC Curve (AUC) of general min-max normalization and proposed 2-step normalization is over 0.95 and speed-up for marching learning based on ASRP field data is improved 1.74 ~ 1.99 times depending on the numbers of training data and of hidden layer's node.

• Korean Management Science Review
• /
• v.24 no.2
• /
• pp.127-142
• /
• 2007

This study deals with the optimal design of mobile access network connecting base stations(BSs) and mobile switching centers(MSCs). Generally mobile operators constitute their access networks by leasing communication lines. Using the characteristic of leased line rate based on administration region, we build an optimization model for mobile access network design which has much smaller number of variables than the existing researches. And we develop a GUI based optimization tool integrating the well-known softwares such as MS EXCEL. MS VisualBasic, MS PowerPoint and Ip_solve, a freeware optimization software. Employing the current access network configuration of a Korean mobile carrier, this study using the optimization tool obtain an optimal solution for both single MSC access network and nation-wide access network. Each optimal access network achieves 7.45% and 9.49% save of lease rate, respectively. Considering the monthly charge and total amount of lease line rate, our optimization tool provides big amount of save in network operation cost. Besides the graphical representation of access networks makes the operator easily understand and compare current and optimal access networks.

• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.337-339
• /
• 2004

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs). Proceeding the layer, this model creates the optimal network architecture through the selection and the elimination of nodes by itself. So, there is characteristic of flexibility. We use a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. GAs is applied to improve the performance with optimal input variables and number of input variables and order. To evaluate the performance of the GAs-based FPNNs, the models are experimented with the use of Medical Imaging System(MIS) data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.35-37
• /
• 2021

Analyzing and predicting foreign tourists' demand is a crucial research topic in the tourism industry because it profoundly influences establishing and planning tourism policies. Since foreign tourist data is influenced by various external factors, it has a characteristic that there are many subtle changes over time. Therefore, in recent years, research is being conducted to design a prediction model by reflecting various external factors such as economic variables to predict the demand for tourists inbound. However, the regression analysis model and the recurrent neural network model, mainly used for time series prediction, did not show good performance in time series prediction reflecting various variables. Therefore, we design a foreign tourist demand prediction model that complements these limitations using a convolutional neural network. In this paper, we propose a model that predicts foreign tourists' demand by designing a one-dimensional convolutional neural network that reflects foreign tourist data for the past ten years provided by the Korea Tourism Organization and additionally collected external factors as input variables.