• Korean Journal of Social Welfare
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• v.43
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• pp.7-41
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• 2000

This study attempted to identify the type of social network interventions and barriers to implement them. Few empirical studies have been conducted concerning social network interventions as professional activities. Although social support and social network interventions have been noted as important practice concepts, the existing studies ten us little about how social workers perceive and experience social network interventions. This study used seven types of social network interventions identified in the previous studies. And based on "obstacles to social network interventions scale" developed by Biegel, Tracy & Song (1995), a twenty-two item scale was developed by the authors. The results from this study show that social workers little implement social network interventions such as community empowerment, family caregiver enhancement, and support group, and that they perceive organization characteristics and profession-oriented culture as important obstacles to implement social network interventions. The findings from this study suggest various strategies to address these barriers.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• Journal of KIBIM
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• v.5 no.1
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• pp.54-62
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• 2015

The Cities that recently developed have been applied to private network for establishing information communication network system. The local governments planning or pursuing U-City construction should also choose the private network in consideration of operation and maintenance. In viewpoint of agency operating u-City, it is necessary to integrate traditional and new network. However, there has been lack of guides to choose U-City network considering the economic analysis between private and leased network. This study analyzed the characteristics of private and leased network, and the cost-benefit by estimating the network cost and communication demand focused on U-services that are recently applied. This study purpose a guide for efficient U-City information network selected by estimating ROI(Return On Investment) and BEP(Break Even Point) for establishing private and leased network.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.1
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• pp.33-41
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• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.

• Women's Health Nursing
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• v.17 no.2
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• pp.157-168
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• 2011

Purpose: This study was done to examine differences in mammography screening according to breast cancer and social network characteristic. Methods: Data were collected from 187 married women 35 years and older who were using public health centers, health promotion centers, cultural centers, obstetrics and gynecology hospitals or other relevant community sites. Data were collected between October 24 and December 4, 2008. Data were analyzed using the SPSS/WIN 15.0 program. Results: The participation rate for mammography screening was 35.3%. The following general and breast cancer characteristics showed statistically significant differences: religion, family incomes, regular medical-care, general health examinations during past 2 years, and history of breast disease. The following social network characteristics showed statistically significant differences: social norms and subjective norms. Using logistic regression analysis, regular medical-care, breast cancer risk appraisal, social norm, and subjective norms were highly predictive of subsequent mammography. Conclusion: The results of this study indicate that it is important to develop and provide tailored intervention programs through integrated socially mediated programs. By consciously including social network and support systems, breast cancer detection efforts would not end as a one-time event, but naturally build on network structure of adults women, thus facilitating regular mammography screening.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Women's Health Nursing
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• v.9 no.1
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• pp.50-60
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• 2003

Purpose : The purpose of this study was to evaluate the factors which are related to the maternal role performance of first-time mother to improve the health of infant. Specifically a basic hypothetical model was developed based on the previous study about a model of social networks. Method : The survey was done from January to February in 2001. Total 257 mothers who have four to twelve month old first-time baby was interviewed in five community health center around country(Seoul, Choung-ju, Asan, Cheon-an, Jeju). Finally 247 data was analyzed. Data analysis was done with LISREL 8.20 program for covariance structural analysis. Results : Compared to the hypothetical model, the revised model has become parsimonious and had a better fit to the data ($X^2=167.55$ (p값=.00), $x^2/df=1.48$, GFI=0.97, AGFI=0.95, RMR=0.049, NFI=0.98, NNFI=0.99, CN=222.53). All predictive variables of the maternal role of first-time mother explained 30% of total variance in model. Social network structural characteristics and social network interactional characteristics had significant effect on the emotional support and the information support. And social network interactional characteristics had significant effect on the service support, material support and social companionship support. The service support and social companion ship support had significant effect on the maternal role strain. The emotional support and the social companion ship support had significant effect on the maternal role of first-time mother. Conclusion : As the conclusion of this study, there is in need of the developing the programmes focussed on the social network for the first-time mother.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.497-511
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• 2019

The purpose of this study is to examine the effects of entrepreneurship and personal characteristics on entrepreneurial intention and to analyze what factors influence the entrepreneurial intention through the moderating effects of social networks. So, the survey was conducted on 374 employees in their 30s or older. Descriptive statistics were used to understand the actual state of entrepreneurial intentions, and exploratory factor analysis was conducted to analyze the validity. In addition, a correlation analysis was conducted to identify relationships with variables, finally, a hierarchical regression analysis was performed to identify the effect of moderating social networks in entrepreneurship and personal characteristics. The study found that, firstly, Entrepreneurship was positively correlated with extroversion, achievement desire, self-efficacy, social network, entrepreneurial intention, and negatively correlated with introversion. Second, The extroversion of personal characteristics showed positive correlations with achievement desire, self-efficacy, social network, and entrepreneurial intention and negatively correlated with introversion. Introversion has a negative correlation with achievement desire, self-efficacy, social network, and entrepreneurial intention, and self-efficacy has a positive correlation with social network and entrepreneurship. Third, social networks have been shown to moderate the relationship between personal characteristics and entrepreneurial intention. Reflecting the results from this study, we expect that those preparing for future start-ups will be a meaningful reference to validate their capabilities and start them.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.183-199
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• 2022

With the consumption of smart phone content increasing rapidly, the short clip market in China is rapidly growing. TikTok, a short clip platform, has achieved great business success. However, there is not much research done on TikTok platform from the customers' perspective. To this end, this study aimed to verify the relationship between the social network characteristics on the TikTok platform, attachment toward the TikTok platform, user participation, social identity, psychological distance and reuse intention through an empirical investigation. In August 2021, a survey was conducted on consumers on the subject of TikTok platform in China. The results of the study are as follows. First, the social network characteristics significantly affected the user participation and the attachment. Second, both the attachment and the user participation had a significant impact on reuse intention. Third, user participation had a significant impact on attachment. Fourth, social identity played a significant moderating role in the relationship between social network characteristics and user participation. Fifth, Psychological distance played a significant moderating role in the relationship between social network characteristics and attachment. The results of this study are expected to provide theoretical and practical implications for research on TikTok platform.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.261-264
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• 2004

We propose a location management with adaptive binding idle lifetime scheme for IP-based wireless network. In our proposed scheme, the binding idle lifetime value is adaptively varied according to user characteristics. The main idea is that the mobile node (MN) does location update (LU) even in idle state. Furthermore a sequential paging scheme is used to reduce the paging cost. The proposed scheme can be used in both cellular network and IP-based network.