• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.1
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• pp.115-129
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• 2003

This paper Is concerned with a new techno-economic model Ing of ATM based h19h-speed networks. Coupled with advances of technology, the rapid development of new telecommunication services significantly increases the magnitude of risk in making an Investment decision. Naturally, the success of techno-economic modeling depends on how effectively we manage underlying risk factors such as cost and technology To deal with risk factors, we need to rely on modern decision and risk analysis while Implementing mathematical optimization for solving a complex capacity expansion problem of telecommunication systems during the planning period. We provide a case study that will enhance our understanding of the techno-economic analysis for emerging telecommunication systems.

• Journal of Computing Science and Engineering
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• v.7 no.3
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• pp.198-203
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• 2013

In this paper, we focus on a novel Internet architecture, based on the urbanization of virtual machines. In this approach, virtual networks are built linking specific virtual elements (router, switch, firewall, box, access point, etc.). A virtual network represents a network with an independent protocol stack that shares resources from the underlying network infrastructure. Virtualization divides a real computational environment into virtual computational environments that are isolated from each other, and interact with the upper computational layer, as would be expected from a real, non-virtualized environment. Metamorphic networks enhance several concepts related to future networks, and mainly the urbanization of virtual machines. We present this new paradigm, and the methodology, based on the worldwide metamorphic network platform "M-Net". The metamorphic approach could solve many complex problems, especially related to Cloud computing services.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2425-2431
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• 1997

The increased usage of Magnetic Resonance Image (MRI) required the method for automatic segmentation of medical image that is more useful so as to diagnose the dissecitive information of a atient quickly and effectively through MR scans.The use of neural networks may give much hep to solving the complex problems concerned the matter. This paper proposes the new method for automatic segmentation of magnetic resonance (MR) images of the brain by using neural networks brained by back-propagation algorithm. The trained neural networks by the segmenting MR images of a patient produce an output that networks can segment MR images of the other patients automatically, too and show a clear image of the brain.

• The Journal of Information Systems
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• v.18 no.3
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• pp.375-399
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• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• Journal of information and communication convergence engineering
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• v.5 no.4
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• pp.300-304
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• 2007

Wireless sensor networks represent a new and exciting communication paradigm which could have multiple applications in future wireless communication. Therefore, performance analysis of such a wireless sensor network paradigm is needed in complex wireless channel. Wireless networks could be an important means of providing ubiquitous communication in the future. In this paper, the BER performance of uniform distributed wireless sensor networks is evaluated in non-Gaussian noise channel. Using an analytical approach, the impact of Av. BER performance relating the coherent BPSK system at the end of a multi-hop route versus the spatial density of sensor nodes and impulsive noise parameters A and $\Gamma$ is evaluated.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3001-3003
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• 2000

In this paper. we propose Fuzzy Self-Organizing Networks (FSON) using both Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FSON is generated from the mutually combined structure of both FNN and PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get the better output performance with superb predictive ability. In order to evaluate the performance of proposed models. we use the nonlinear data sets. The results show that the proposed FSON can produce the model with higher accuracy and more robustness than previous any other method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.4
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• pp.271-280
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• 2016

Predicting cutting forces in machine tools is essential to productivity improvement and process control in the manufacturing field. Furthermore, milling machining is more complicated than turning machining. Therefore, several studies have been conducted previously to simulate milling forces; this study aims to simulate the cutting forces in milling machines using multi-layered neural networks. In the experiments, the number of layers in these networks was 3 and 4 and the number of neurons in the hidden layers was varied from 20 to 200. The root mean square errors of simulated cutting force components were obtained from taught and untaught data for the various neural networks. Results show that the error trends for untaught data were non-uniform because of the complex nature of the cutting force components, which was caused by different cutting factors and nonlinear characteristics coming into play. However, trends for taught data showed a very good coincidence.

• ETRI Journal
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• v.38 no.1
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• pp.52-61
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• 2016

A microblog is a service typically offered by online social networks, such as Twitter and Facebook. From the perspective of information dissemination, we define the concept behind a spreading matrix. A new WeiboRank algorithm for identification of key nodes in microblog networks is proposed, taking into account parameters such as a user's direct appeal, a user's influence region, and a user's global influence power. To investigate how measures for ranking influential users in a network correlate, we compare the relative influence ranks of the top 20 microblog users of a university network. The proposed algorithm is compared with other algorithms - PageRank, Betweeness Centrality, Closeness Centrality, Out-degree - using a new tweets propagation model - the Ignorants-Spreaders-Rejecters model. Comparison results show that key nodes obtained from the WeiboRank algorithm have a wider transmission range and better influence.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.16-23
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• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• Journal of Hydrogen and New Energy
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• v.33 no.4
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• pp.318-329
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• 2022

This study analyzed keyword networks and Author's Affiliation networks of hydrogen-related papers published in Korea Citation Index (KCI) journals from 2016 to 2020. The study investigated co-occurrence patterns of institutions over time to examine collaboration trends of hydrogen scholars. The study also conducted frequency analysis of keyword networks to identify key topics and visualized keyword networks to explore topic trends. The result showed Collaborative research between institutions has not yet been extensively expanded. However, collaboration trends were much more pronounced with local universities. Keyword network analysis exhibited continuing diversification of topics in hydrogen research of Korea. In addition centrality analysis found hydrogen research mostly deals with multi-disciplinary and complex aspects like hydrogen production, transportation, and public policy.