• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.492-500
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• 2011

The airborne backbone network(ABN) provides communication transport services between airborne nodes, surface nodes and satellite nodes. Such ABN is generally constructed with wide-body and high-capacity planes such as AWACS, which can fly long-term along pre-defined flight paths. In this paper, we propose an efficient method to improve routing performances by reconfiguring routing path before link failure based on the prediction of link state with the information of pre-defined backbone nodes' trajectories. Since the proposed method does not need additional information exchange between airborne nodes in order to acknowledge the link failure, it can be effectively used for airborne backbone network with limited bandwidths.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.465-474
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• 2014

Wireless sensor network (WSN) is considered to be one of the most important research fields for ubiquitous healthcare (u-healthcare) applications. Healthcare systems combined with WSNs have only been introduced by several pioneering researchers. However, most researchers collect physiological data from medical nodes located at static locations and transmit them within a limited communication range between a base station and the medical nodes. In these healthcare systems, the network link can be easily broken owing to the movement of the object nodes. To overcome this issue, in this study, the fast link exchange minimum cost forwarding (FLE-MCF) routing protocol is proposed. This protocol allows real-time multi-hop communication in a healthcare system based on WSN. The protocol is designed for a multi-hop sensor network to rapidly restore the network link when it is broken. The performance of the proposed FLE-MCF protocol is compared with that of a modified minimum cost forwarding (MMCF) protocol. The FLE-MCF protocol shows a good packet delivery rate from/to a fast moving object in a WSN. The designed wearable platform utilizes an adaptive linear prediction filter to reduce the motion artifacts in the original electrocardiogram (ECG) signal. Two filter algorithms used for baseline drift removal are evaluated to check whether real-time execution is possible on our wearable platform. The experiment results shows that the ECG signal filtered by adaptive linear prediction filter recovers from the distorted ECG signal efficiently.

• Transactions of Materials Processing
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• v.15 no.5 s.86
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• pp.373-381
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• 2006

The key technologies necessary to develop and utilize a double-action link-type hydraulic die set for the enclosed die forging are presented in this paper. Various die sets for the enclosed die forging are investigated and the technologies necessary to develop and to utilize a double-action link-type hydraulic die set are introduced in detail with emphasis on the mechanism of the die set and its kinematical behaviors, the force transmission mechanism, the criterion on the enclosed die forging and its application, the forming load prediction and the stress distribution of the link. A double-action link-type hydraulic die set is developed and it is applied to the enclosed die forging of a bevel gear.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.7
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• pp.1038-1043
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• 2002

The demand for multimedia service using Ka-band satellite communication are growing rapi이y. So, in this paper, we have analyzed rain attenuation with typical model, and proposed prediction model of rain attenuation in high frequency(over 20[GHz]). Path loss model by rain attenuation is based upon rain rate of representative region(6 cities). Proposed prediction model of rain attenuation and parameter of satellite link can be available for the Ka-band satellite communication.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Proceedings of the Korea Association of Information Systems Conference
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• 2002.11a
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• pp.289-312
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• 2002

This study suggests the Link weight analysis approach to choose input variables and an integrated model to make more accurate bankruptcy prediction model. the Link weight analysis approach is a method to choose input variables to analyze each input node's link weight which is the absolute value of link weight between an input nodes and a hidden layer. There are the weak-linked neurons elimination method, the strong-linked neurons selection method in the link weight analysis approach. The Integrated Model is a combined type adapting Bagging method that uses the average value of the four models, the optimal weak-linked-neurons elimination method, optimal strong-linked neurons selection method, decision-making tree model, and MDA. As a result, the methods suggested in this study - the optimal strong-linked neurons selection method, the optimal weak-linked neurons elimination method, and the integrated model - show much higher accuracy than MDA and decision making tree model. Especially the integrated model shows much higher accuracy than MDA and decision making tree model and shows slightly higher accuracy than the optimal weak-linked neurons elimination method and the optimal strong-linked neurons selection method.

• ETRI Journal
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• v.29 no.5
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• pp.569-595
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• 2007

Particularly in wireless communications, link errors severely affect the quality of the services due to the high error probability and the specific error characteristics (burst errors) in the radio access part of the network. In this work, we show that thorough analysis and appropriate modeling of radio-link error behavior are essential to evaluate and optimize higher layer protocols and services. They are also the basis for finding network-aware cross-layer processing algorithms which are capable of exploiting the specific properties of the link error statistics, such as predictability. This document presents the analysis of the radio link errors based on measurements in live Universal Mobile Telecommunication System (UMTS) radio access networks as well as new link error models originating from that analysis. It is shown that the knowledge of the specific link error characteristics leads to significant improvements in the quality of streamed video by applying the proposed novel network- and content-aware cross-layer scheduling algorithms. Although based on live UMTS network experience, many of the conclusions in this work are of general validity and are not limited to UMTS only.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.31-43
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• 2008

Travel time prediction is an indispensable to many advanced traveler information systems(ATIS) and intelligent transportation systems(ITS). In this paper we propose a method to predict travel time using $Na{\ddot{i}}ve$ Bayesian classification method which has exhibited high accuracy and processing speed when applied to classily large amounts of data. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. For a given route, we consider time-varying average segment velocity to perform more accuracy of travel time prediction. We compare the proposed method with the existing prediction algorithms like link-based prediction algorithm [1] and Micro T* algorithm [2]. It is shown from the performance comparison that the proposed predictor can reduce MARE (mean absolute relative error) significantly, compared with the existing predictors.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.19-23
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• 2015

In this paper, we present the return link ACM method to improve the link availability and system throughput for satellite communication service. Also, we describe the optimization of an algorithm for channel prediction using the LMS (Least Mean Square) adaptive filter and the MODCOD (Modulation & Code rate) decision. The simulation results show that the optimized filter taps and step-size of adaptive filter are 2 and 0.00026, respectively. And also confirms the required SNR margin for minimization of MODCOD decision error is 0.3dB.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.