• Journal of Digital Convergence
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• v.15 no.11
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• pp.251-259
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• 2017

Recently, the smart healthcare industry that grafted the Internet of Things (IoT) onto the healthcare and medical services is being highlighted. Smart healthcare refers to the healthcare and medical services offered on the basis of information communication technologies including application, wearable devices and platforms, etc. The number of next generation smart healthcare devices are increasing in the smart healthcare industry through the combination of information technology (IT) and Bio Technology (BT), which are the most active areas among the 6T, which are the areas of the next generation industry. With the emergence of a diverse range of smart healthcare systems or devices, whether the smart healthcare system can be linked with other systems organically and the security and quality of the system have become important elements of evaluation. In this Study, the characteristics and service types of smart healthcare systems were examined, and the trends in the technology and industry of the smart healthcare system were analyzed. Moreover, this Study aims to develop the evaluation method for security and standards for quality evaluation by deducing the factors for the evaluation of smart healthcare system on the basis of the results of aforementioned examination. It is anticipated that this can induce improvement of quality and development of highly reliable products of a smart healthcare system, and will become the core technology to substitute the technology protection barrier.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Journal of radiological science and technology
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• v.31 no.1
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• pp.1-10
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• 2008

Ubiquitous health care system, which is one of the developing solution technologies of IT, BT and NT, could give us new medical environments in future. Implementing health information systems can be complex, expensive and frustrating. Healthcare professionals seeking to acquire or upgrade systems do not have a convenient, reliable way of specifying a level of adherence to communication standards sufficient to achieve truly efficient interoperability. Great progress has been made in establishing such standards-DICOM, IHE and HL7, notably, are now highly advanced. IHE has defined a common framework to deliver the basic interoperability needed for local and regional health information networks. It has developed a foundational set of standards-based integration profiles for information exchange with three interrelated efforts. HL7 is one of several ANSI-accredited Standards Developing Organizations operating in the healthcare arena. Most SDOs produce standards (protocols) for a particular healthcare domain such as pharmacy, medical devices, imaging or insurance transactions. HL7's domain is clinical and administrative data. HL7 is an international community of healthcare subject matter experts and information scientists collaborating to create standards for the exchange, management and integration of electronic healthcare information. The ASTM specification for Continuity of Care Record was developed by subcommittee E31.28 on electronic health records, which includes clinicians, provider institutions, administrators, patient advocates, vendors, and health industry. In this paper, there are suggestions that provide a test bed, demonstration and specification of how standards such a IHE, HL7, ASTM can be used to provide an integrated environment.

• The KIPS Transactions:PartC
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• v.8C no.5
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• pp.629-636
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• 2001

Network is changed from voice-based network into multimedia-based network by development of communication technology and multimedia service. We need a large bandwidth for multimedia service. The optical fiber is a more suitable medium than existing copper-based cable for large bandwidth. But, it is so expensive than copper-based cable. So, Minimizing total cost becomes a more important concept. In order to construct a minimum cost network, we have to consider existing conduits in network. On the other hand, optical fiber network allows that larger amount of traffic can be transmitted than copper-based network does. However, a failure of a node or link can make a serious damage to the network service. Thus, we have to get multiple paths to support continuous service even if a loss of failure occurs in some point of the network. The network survivability problem is to design the network that can provide reliable service to customers anytime with minimum total cost. In an existing solution of the network survivability problem with conduits, a conduit is considered only one time. But, the conduit is reusable if the network satisfies the required survivability. Proposed algorithm can more effectively considered already existed conduit. Network survivability and edge cost is predetermined. The proposed algorithm finds the best solution by conduit sharing within the limits of network survivability. According to the simulation result, the proposed method can decrease 7% of total cost than an existing method by effective conduits adaption.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.127-143
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• 1996

Advanced countries are very active in deploying the National Information I infrastructure which provides universal service to promise fruitful quality of futuree life. Even in the distributed environment, we can closely converse, work together, and share information in a very convenient way. This is actually enabled with the help of groupware technology, which are currently focused and researched in a larger popularity. The aim of this study is to design a portable pack for group communications management to support the development of groupware application. In the paper we begin with technical survey, continue to build our own model for group communications man agement, and design its architecture and procedure. We also suggest group addressing mechanism under Internet environment such as how to create IP multicast address and IP port number dynamically and as a globally unique value for the communication session, with the help of the hierarchical and distributed address managers. We also indicate the reliable data transmission services to remedy the unreliable feature of the UDP multicast services, and finally the architecture/ applied to support the practical applications is briefly discussed for verification of the designed concept.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.279-290
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• 2013

In digital forensics log files have been stored as a form of large data for the purpose of tracing users' past behaviors. It is difficult for investigators to manually analysis the large log data without clues. In this paper, we propose a text mining technique for extracting intrusion logs from a large log set to recommend reliable evidences to investigators. In the training stage, the proposed method extracts intrusion association words from a training log set by using Apriori algorithm after preprocessing and the probability of intrusion for association words are computed by combining support and confidence. Robinson's method of computing confidences for filtering spam mails is applied to extracting intrusion logs in the proposed method. As the results, the association word knowledge base is constructed by including the weights of the probability of intrusion for association words to improve the accuracy. In the test stage, the probability of intrusion logs and the probability of normal logs in a test log set are computed by Fisher's inverse chi-square classification algorithm based on the association word knowledge base respectively and intrusion logs are extracted from combining the results. Then, the intrusion logs are recommended to investigators. The proposed method uses a training method of clearly analyzing the meaning of data from an unstructured large log data. As the results, it complements the problem of reduction in accuracy caused by data ambiguity. In addition, the proposed method recommends intrusion logs by using Fisher's inverse chi-square classification algorithm. So, it reduces the rate of false positive(FP) and decreases in laborious effort to extract evidences manually.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.155-169
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• 2016

In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.

• BMB Reports
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• v.39 no.4
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• pp.339-345
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• 2006

The blood-neural barrier (BNB), including blood-brain barrier (BBB) and blood-retinal barrier (BRB), is an endothelial barrier constructed by an extensive network of endothelial cells, astrocytes and neurons to form functional 'neurovascular units', which has an important role in maintaining a precisely regulated microenvironment for reliable neuronal activity. Although failure of the BNB may be a precipitating event or a consequence, the breakdown of BNB is closely related with the development and progression of CNS diseases. Therefore, BNB is most essential in the regulation of microenvironment of the CNS. The BNB is a selective diffusion barrier characterized by tight junctions between endothelial cells, lack of fenestrations, and specific BNB transporters. The BNB have been shown to be astrocyte dependent, for it is formed by the CNS capillary endothelial cells, surrounded by astrocytic end-foot processes. Given the anatomical associations with endothelial cells, it could be supposed that astrocytes play a role in the development, maintenance, and breakdown of the BNB. Therefore, astrocytes-endothelial cells interaction influences the BNB in both physiological and pathological conditions. If we better understand mutual interactions between astrocytes and endothelial cells, in the near future, we could provide a critical solution to the BNB problems and create new opportunities for future success of treating CNS diseases. Here, we focused astrocyte-endothelial cell interaction in the formation and function of the BNB.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.612-622
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• 2017

This paper is about on the most representative ethnic of China Mongolian as the research object, through the questionnaire survey to establish the understanding of national cultural symbol and the system of national identity, and lay a theoretical foundation for the application of Mongolian communication and design in the future. In order to achieve the objectivity of the data, so a questionnaire survey was conducted on 300 populations of Mongolian and other nationalities. The result is that the majority of the Mongolians believe that as the Mongolian people have a sense of pride, and the Mongolian nationality is a representative of china. Mongolian is a kind of aesthetic, creative, reliable, aggressive and like the decoration of the nation. The cultural symbols for design elements are cyan, Gen Gi Khan graphics, agate, and peaceful meaning and so on. The cultural symbols are used for celebration, as well as clothing accessories. The symbol of culture has played a positive role in the establishment of Inner Mongolia identity and the propaganda of the nation. The construction of Mongolian cultural symbol system plays an important role in the establishment of Mongolian national identity. To combine the meaning of nation and the mission of culture with national cultural resources. It is not only to help the development of minority culture, but also to promote the sense of pride of ethnic minorities.