• Journal of Information Technology Services
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• v.11 no.3
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• pp.17-38
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• 2012

Social network service(SNS), provided by social network sites such as Facebook, Twitter and Cyworld is rapidly growing in online business. Furthermore, many companies have growing interests in finding effective ways to use SNSs for their innovations, marketing and advertisement. In fact, firms have recognized the utility value of the SNS for their business. In this aspect, this study attempts to identify key factors influencing the intention to continuous use of SNSs. Based on the UTAUT(the Unified Theory of Acceptance and Usage of Technology)model, this study proposes the research model, including the effects of social network service characteristics(social relationship support, information sharing, image expression) and individual characteristics(self-disclosure, extroversion, familiarity) on performance expectancy as well as the moderating effect of perceived information security among UTAUT variables. The 412T sets of data collected in a survey were tested against the modeling using SEM using SmartPLS. Results indicated that social network service and individual characteristics had significant effect on performance expectancy with exception of self-disclosure. In addition, the moderating effect of perceived information security had significant effect. The results had important implications for firms providing SNSs hoping to develop a successful business model.

• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.6
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• pp.419-425
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• 2016

This study analyzed research methodologies based on network pharmacology to build a new system architecture optimized for Korean Medicine research. Results form studies using network pharmacology were collected and analyzed to evaluate the strength and weakness. Finally, an improved system architecture was proposed. Whether the predicted effects of drugs or herbs from network pharmacological analyses were in agreement with those from conventioanl knowledge of Korean Medicine was evaluated. These results were used to verify the applicability of research methodologies to the modern pharmacology and Korean Medicine respectively. Eighteen papers using TCMSP were collected and analyzed. The results suggest that the research methodology based on network pharmacology is appropriate only for the modern pharmacology but not for Korean Medicine. Information about compound-compound, herb-herb and drug-compound interactions need to be considered. We propose the modified system architecture with those information.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.12
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• pp.1690-1696
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• 2014

Network RTK generates spatial corrections by using differenced measurements from reference stations in the network, and the corrections are then provided to a rover. The rover, generally, uses linear interpolation, which assumes that the corrections at each reference station are spatially correlated, to obtain a precise correction of its location. However, an irregularity of the tropospheric delay is a real-world factor that violates this assumption. Tropospheric delay is a result of weather conditions, such as humidity, temperature and pressure, and it can cause spatial decorrelation when there are changes in the local climate. In this paper, we have defined the non-linear characteristics of the tropospheric delay between reference stations or user within a region as the "irregularity of tropospheric delay". Such an irregularity can negatively impact the network RTK performance. Therefore, we analyze the influence of the irregularity of tropospheric delay in network RTK based on meteorological data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• The Korean Journal of Physiology and Pharmacology
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• v.18 no.2
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• pp.177-182
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• 2014

This study was to determine the correlation between endothelial function and neuro-endocrine-immune (NEI) network through observing the changes of NEI network under the different endothelial dysfunction models. Three endothelial dysfunction models were established in male Wistar rats after exposure to homocysteine (Hcy), high fat diet (HFD) and Hcy+HFD. The results showed that there was endothelial dysfunction in all three models with varying degrees. However, the expression of NEI network was totally different. Interestingly, treatment with simvastatin was able to improve vascular endothelial function and restored the imbalance of the NEI network, observed in the Hcy+HFD group. The results indicated that NEI network may have a strong association with endothelial function, and this relationship can be used to distinguish different risk factors and evaluate drug effects.

• Journal of Internet Computing and Services
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• v.9 no.6
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• pp.15-25
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• 2008

Due to the differences between a mobile ad-hoc network and a sensor network, the pre-existing autoconfiguration for a mobile ad-hoc network cannot be simply applied to a sensor network. But. a mechanism is still necessary to assign locally unique addresses to sensor nodes efficiently. This paper proposes a hybrid IDs assignment scheme of local area sensor networks. The IDs assignment scheme of hybrid method combines a proactive IDs assignment with a reactive IDs assignment scheme. The proposed scheme considers efficient communication using reactive IDs assignment, and security for potential attacks using zone-based self-organized clustering with Byzantine Agreement in sensor networks. Thus, this paper has solved the shortage of security due to minimizing network traffic and the problem of repairing the network from the effects of an aberrant node in sensor networks.

• Journal of Pharmacopuncture
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• v.18 no.3
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• pp.11-18
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• 2015

Objectives: Systems biology is a novel subject in the field of life science that aims at a systems' level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era. Methods: The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction. Results: The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted. Conclusion: Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.377-382
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• 2007

This paper proposes a training algorithm for global optimization of the parameters of radial basis function networks. Since conventional training algorithms usually perform only local optimization, the performance of the network is limited and the final network significantly depends on the initial network parameters. The proposed hybrid simulated annealing algorithm performs global optimization of the network parameters by combining global search capability of simulated annealing and local optimization capability of gradient-based algorithms. Via experiments for function approximation problems, we demonstrate that the proposed algorithm can find networks showing better training and test performance and reduce effects of the initial network parameters on the final results.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.64-73
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• 1997

Network technology has been developed for very high-speed networking and multimedia data whose characteristics are the continuous and bursty transmission as well as a large amount of data. With this trend users wish to view the information about the application services as well as network devices and system hardware. However, it is rarely available for the users the information of performance or faults of the application services. Most of information is limited to the information related network devices or system hardware. Furthermore, users expect the best services without knowing the service environments in the network and there is no good way of delivering the service related problems and fault information of application services in a high speed network yet. In this paper we present a web-based application management system that we have developed for the past year. It includes a method to build an agent system that uses an existing network management standards, SNMP MIB and SNMP protocols. The user interface of the system is also developed to support visualization effects with web-based Java interface which offers a convenient way not only to access management information but also to control networked applications.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.141-146
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• 2019

These days, there are many applications using neural networks as parts of their system. On the other hand, adversarial examples have become an important issue concerining the security of neural networks. A classifier in neural networks can be fooled and make it miss-classified by adversarial examples. There are many research to encounter adversarial examples by using denoising methods. Some of them using GAN (Generative Adversarial Network) in order to remove adversarial noise from input images. By producing an image from generator network that is close enough to the original clean image, the adversarial examples effects can be reduced. However, there is a chance when adversarial noise can survive the approximation process because it is not like a normal noise. In this chance, we propose a research that utilizes high-level representation in the classifier by combining GAN network with a trained U-Net network. This approach focuses on minimizing the loss function on high representation terms, in order to minimize the difference between the high representation level of the clean data and the approximated output of the noisy data in the training dataset. Furthermore, the generated output is checked whether it shows minimum error compared to true label or not. U-Net network is trained with true label to make sure the generated output gives minimum error in the end. At last, the remaining adversarial noise that still exist after low-level approximation can be removed with the U-Net, because of the minimization on high representation terms.