• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.143-150
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• 2017

Absorption of microwave radio frequency signal by atmospheric rain is prevalent at frequencies above 10 GHz. This paper presents the studies on rain attenuation at 18 GHz for 3.2 km experimental link between Khumdang (Korea Telecom, KT station) and Icheon (National Radio Research Agency, RRA station). The received signal data for rain attenuation and rain rate were collected at 10 second intervals over a three year periods from 2013 to 2015. Out of several models, the paper present discussion and comparison of ITU-R P.530-16 model, Moupfouma model, Da Silva Mello model along with suitable rain attenuation prediction method. The limitation of research lies on the experimental system that is set up in only one location, however, the preliminary results indicate the application of suitable 1-minute rain attenuation model for specific site. The method provides useful information for microwave engineers and researchers in making decision over the choice of most suitable rain attenuation prediction in terrestrial links.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1324-1331
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• 2008

Recently, landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is introduced. The system is focused to debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes and gateway are deployed with Microstrain G-Link system. Five wireless sensor nodes and gateway are installed at the man-made slope to detect landslide. It is found that the acceleration data of each sensor node can be obtained via wireless sensor networks. Additionally, thresholds to determine whether the slope will be stable or not are proposed using finite element analysis. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7A
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• pp.550-556
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• 2005

This paper proposes a path prediction-based seamless handover scheme to minimize service disruption and packet loss due to handovers for mobile networks such as Dams. This scheme exploits a peculiar characteristics of trains which move on a predetermined path. The mobile router on the train can predict a handover point and then perform network-layer handover before link-layer handover occurs. This leads to the reduction of handover latency and packet loss during handovers. The performance evaluation showed that the proposed scheme could provide excellent performance, compared with the NEMO basic support scheme.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.47-51
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• 2007

The present paper reports the life time prediction of Acrylonitrile-Butadiene rubber (NBR) fuel cell gasket materials as a function of operational variables like acid concentration, ageing time and temperature. Both material and accelerated acid-heat aging tests were carried out to predict the useful life of the NBR rubber gasket for use as a fuel cell stack. The acid ageing of the gasket compounds has been investigated at 120, 140 and $160^{\circ}C$, with aging times from 3 to 600 h and increasing acid ($H_2SO_4$) concentrations of 5, 6, 7 and 10 vol%. Material characteristics the gas compound such as cross-link density, tensile strength and elongation at break were studied. The hardness of the NBR rubber was found to decrease with decreasing acid concentration at both 120 and $140^{\circ}C$, but at $160^{\circ}C$ interestingly the hardness of the NBR rubber increased abruptly in a very short time at different acid concentrations. The tensile strength and elongation at break were found to decrease with increase in both the acid concentrate ion & temperature. The life time of the compounds were evaluated using the Arrhenius equation.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.1-9
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• 2011

Wireless mobile networks that exploit generalized PF scheduling can dynamically allocate network resources by using scheduling parameters. There are limitations to predict throughputs by the conventional stochastic approach in general. Moreover the limitations make it difficult to find appropriate scheduling parameters for achieving the demanded throughputs. This paper derives a prediction algorithm that predicts throughputs of the networks by using deterministic approach. A throughput adjust algorithm and a throughput switching algorithm are derived from the prediction algorithm. The performance of the throughput prediction/switching algorithms is evaluated by a simulator based on IEEE 802.16m system.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.191-205
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• 2013

Recently, the topic of predicting interpersonal trust in online social networks is receiving considerable attention, because trust plays a critical role in controlling the spread of distorted information and vicious rumors, as well as reducing uncertainties and risk from unreliable users in social networks. Several trust prediction models have been developed on the basis of transitivity and composability properties of trust; however, it is hard to find empirical studies on whether and how transitivity and composability properties of trust are operated in real online social networks. This study aims to predict interpersonal trust between two unknown users in social networks and verify the proposition on whether and how transitivity and composability of trust are operated in social networks. For this purpose, we chose three social network sites called FilmTrust, Advogato, and Epinion, which contain explicit trust information by their users, and we empirically investigated the proposition. Experimental results showed that trust can be propagated farther and farther along the trust link; however, when path distance becomes distant, the accuracy of trust prediction lowers because noise is activated in the process of trust propagation. Also, the composability property of trust is operated as we expected in real social networks. However, contrary to our expectations, when the path is synthesized more during the trust prediction, the reliability of predicted trust did not tend to increase gradually.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.53-60
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• 2012

Nearest-neighbor classification predicts the class of an input data with the most frequent class among the near training data of the input data. Even though nearest-neighbor classification doesn't have a training stage, all of the training data are necessary in a predictive stage and the generalization performance depends on the quality of training data. Therefore, as the training data size increase, a nearest-neighbor classification requires the large amount of memory and the large computation time in prediction. In this paper, we propose a prototype selection algorithm that predicts the class of test data with the new set of prototypes which are near-boundary training data. Based on Tomek links and distance metric, the proposed algorithm selects boundary data and decides whether the selected data is added to the set of prototypes by considering classes and distance relationships. In the experiments, the number of prototypes is much smaller than the size of original training data and we takes advantages of storage reduction and fast prediction in a nearest-neighbor classification.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.3
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• pp.471-481
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• 2023

Digital twin is considered as a key technology of industry 4.0, thus being essential for the future of industrial production. Despite the significance, a systematic analysis of its technological landscape is lacking. This study aims to investigate the technological development trends and newly emerging technological convergence opportunities in the domain of digital twin by exploiting patent information derived from U SPTO. For this purpose, this study visualized and predicted the convergence dynamics among patent classification codes by adopting patent co-classification analysis and link prediction approach. The findings show that the number of digital twin-related patent applications has increased significantly since 2018. The CPC code G06F showed the highest eigenvector centrality, while G05B was characterized by highest betweenness centrality. According to the predictive model, 41 novel links were revealed, acting as potential technology convergence opportunities. These links were then categorized into 11 different domains. The most dominant category was "digital data processing and artificial intelligence", which could play a foundational role in the diffusion of digital twin technology. The presence of digital twin technology is dominant in manufacturing, but its applications are expected to expand, including "climate change", "healthcare" and "aerospace engineering". The derived insights can support R&D managers and policy makers in formulating R&D strategies and directing future R&D investment decisions.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.12
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• pp.9-16
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• 2005

A new online routing algerian is proposed in this paper, which use the interference-prediction to solve the network congestion originated from extension of Internet scope and increasing amount of traffic. The end-to-end QoS has to be guaranteed in order to satisfy service level agreements (SLAs) in the integrated networks of next generation. For this purpose, bandwidth is allocated dynamically and effectively, moreover the path selection algorithm is required while considering the network performance. The proposed algorithm predicts the level of how much the amount of current demand interferes the future potential traffic, and then minimizes it. The proposed algorithm considers the bandwidth on demand, link state, and the information about ingress-egress pairs to maximize the network performance and to prevent the waste of the limited resources. In addition, the interference-prediction supports the bandwidth guarantee in dynamic network to accept more requests. In the result, the proposed algorithm performs the effective admission control and QoS routing. In this paper, we analyze the required conditions of routing algorithms, the aspect of recent research, and the representative algorithms to propose the optimized path selection algorithm adequate to Internet franc engineering. Based on these results, we analyze the problems of existing algorithms and propose our algorithm. The simulation shows improved performance by comparing with other algorithms and analyzing them.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.37-52
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• 2015

The recent global trends display expansion and growing solidity in both cooperative collaboration between industry, education, and research and R&D network systems. A greater support for the network and cooperative research sector would open greater possibilities for the evolution of new scholar and industrial fields and the development of new theories evoked from synergized educational research. Similarly, the national need for a strategy that can most efficiently and effectively support R&D network that are established through the government's R&D project research is on the rise. Despite the growing urgency, due to the habitual dependency on simple individual personal information data regarding R&D industry participants and generalized statistical data references, the policies concerning network system are disappointing and inadequate. Accordingly, analyses of the relationships involved for each subject who is participating in the R&D industry was conducted and on the foundation of an educational-industrial-research network system, possible changes within and of the network that may arise were predicted. To predict the R&D network transitions, Common Neighbor and Jaccard's Coefficient models were designated as the basic foundational models, upon which a new prediction model was proposed to address the limitations of the two aforementioned former models and to increase the accuracy of Link Prediction, with which a comparative analysis was made between the two models. Through the effective predictions regarding R&D network changes and transitions, such study result serves as a stepping-stone for an establishment of a prospective strategy that supports a desirable educational-industrial-research network and proposes a measure to promote the national policy to one that can effectively and efficiently sponsor integrated R&D industries. Though both weighted applications of Common Neighbor and Jaccard's Coefficient models provided positive outcomes, improved accuracy was comparatively more prevalent in the weighted Common Neighbor. An un-weighted Common Neighbor model predicted 650 out of 4,136 whereas a weighted Common Neighbor model predicted 50 more results at a total of 700 predictions. While the Jaccard's model demonstrated slight performance improvements in numeric terms, the differences were found to be insignificant.