• Educational Technology International
• /
• v.13 no.1
• /
• pp.1-25
• /
• 2012

Despite the relatively short history of Social Network Sites or Services (SNS), it has quickly gained popularity with more than seven hundred million users all over the globe. The SNS emerged as one of the strongest cultural influences for the contemporary society. The SNS would provide both chances and challenges for Education. The main purpose of the article was to explore the way education react and adapt to the emergence of social network and SNS. It tried to provide major theoretical grounds that bridge education and social network. In the due process, the researchers have examined the curriculum and instructional design process of education from the perspective of disruptive and sustainable aspect of SNS technology. Consequently, four major theoretical grounds were identified and reviewed: Gibson's theory of affordance, Vygotsky's social constructivism, Rha's human visual intelligence theory, and the network theory. By investigating these theories, the educational potentials of social network and SNS were emerged. The SNS was viewed as a new medium with abundant potentials of expanding the learning space, empowering the affective aspects of learning, and facilitating the formation of group intelligence. Finally, some future implications and challenges of SNS were suggested.

• Journal of Electrical Engineering and Technology
• /
• v.2 no.2
• /
• pp.157-164
• /
• 2007

Diagnosis techniques based on the dissolved gas analysis(DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using fuzzy clustering and a radial basis neural network(RBFNN). In the neural network, fuzzy clustering is effective for selecting the efficient training data and reducing learning process time. After fuzzy clustering, the RBF neural network is developed to analyze and diagnose the state of the transformer. The proposed method measures the possibility and degree of aging as well as the faults occurred in the transformer. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.

• Journal of Korean Society of Rural Planning
• /
• v.11 no.3 s.28
• /
• pp.29-42
• /
• 2005

This study aimed to apply a methodological approach, 'social network analysis' to a case study for the understanding of relational structure among stakeholders related to green tourism development. By doing so, this study argued that it is important to identify stakeholder's network structure to help green tourism planners develop collaborative relationship among stakeholders. This study identified the stakeholders regarding a community-based festival development in the southern area of Korea, and investigated two types of networks among them: decision-making power relational and intimate network. Interviewer-administrated survey and in-depth interview were employed for data collection. The data was analyzed by SPSS (version 10.0) and Net-MinerII (version 2.5.0), and by constant comparison method. The result revealed that low different groups of the stakeholders were separated in the intimate networt and that the festival organizational body was not connected with other stakeholders in the decision-making power relational network. The existence of separated groups and weak relationship among the stakeholders appeared to relate to age-group differences, and different views on the festival between the stakeholders.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2004.11a
• /
• pp.265-271
• /
• 2004

The interaction network of protein -protein plays an important role to understand the various biological functions of cells. Currently, the high -throughput experimental techniques (two -dimensional gel electrophoresis, mass spectroscopy, yeast two -hybrid assay) provide us with the vast amount of data for protein-protein interaction at the proteome scale. In order to recognize the role of each protein in their network, the efficient bioinformatical and computational analysis methods are required. We propose a systematic and mathematical method which can analyze the protein -protein interaction network rigorously and enable us to capture the biological and physical essence of a topological character and stability of protein -protein network, and sensitivity of each protein along the biological pathway of their network. We set up a Laplacian matrix of spectral graph theory based on the protein-protein network of yeast proteome, and perform an eigenvalue analysis and apply a perturbation method on a Laplacian matrix, which result in recognizing the center of protein cluster, the identity of hub proteins around it and their relative sensitivities. Identifying the topology of protein -protein network via a Laplacian matrix, we can recognize the important relation between the biological pathway of yeast proteome and the formalism of master equation. The results of our systematic and mathematical analysis agree well with the experimental findings of yeast proteome. The biological function and meaning of each protein cluster can be explained easily. Our rigorous analysis method is robust for understanding various kinds of networks whether they are biological, social, economical...etc

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.236-238
• /
• 1992

This paper proposes a new algorithm to estimate time-varying parameters by combining KFSM(Kalman Filter with Shift Matrix) with neural network compansator. While the time varying parameters are estimated from KFSM, the error coverence of system, R(k) are compansated by neural network concurrently. The casestudy using computer simulation proves the usefullness and advantages of the proposed algorithm in this paper.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.286-287
• /
• 2008

This paper proposes a power system blackout model and devises a method of identification and selection of higher-order contingencies that may threaten power system security. To study how failures spread in power grids, network observability based on topological concept is utilized which provide a means of monitoring network evolutions due to multiple contingencies. The simulations and results are presented using the IEEE 118-bus test system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.109.3-109
• /
• 2002

1. Introduction 2. Vision system 3. Effect of brightness variations 4. Color classifier using multi-layer neural network 5. Experimental result of color classifier 6. Applications for robot soccer system 7. Conclusion

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.106.5-106
• /
• 2002

1. Introduction 2. Artificial Immune Networks and Navigation Algorithm 3. Obstacle Avoidance and Goal Approach Behavior 4. Weights Adjustment Using Neural Network 5. Velocity Control and Local Minimum Avoidance 6. Simulation 7. Conclusion

• Proceedings of the Materials Research Society of Korea Conference
• /
• 1998.08a
• /
• pp.120.2-120
• /
• 1998

• Journal of the Korean Society of Mechanical Technology
• /
• v.20 no.6
• /
• pp.776-782
• /
• 2018

After CNN basic structure was introduced by LeCun in 1989, there has not been a major structure change except for more deep network until recently. The deep network enhances the expression power due to improve the abstraction ability of the network, and can learn complex problems by increasing non linearity. However, the learning of a deep network means that it has vanishing gradient or longer learning time. In this study, we proposes a CNN structure with MLP layer. The proposed CNNs are superior to the general CNN in their classification performance. It is confirmed that classification accuracy is high due to include MLP layer which improves non linearity by experiment. In order to increase the performance without making a deep network, it is confirmed that the performance is improved by increasing the non linearity of the network.