• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.491-499
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• 2018

Runway visual range (RVR), one of the important indicators of aircraft takeoff and landing, is affected by meteorological conditions such as temperature, humidity, etc. It is important to estimate the RVR at the time of arrival in advance. This study estimated the RVR of the local airport after 1 hour by upgrading the RVR estimation model using the proposed deep learning network. To this end, the advancement of the estimation model was carried out by changing the time interval of the meteorological data (temperature, humidity, wind speed, RVR) as input value and the linear conversion of the results. The proposed method generates estimation model based on the past measured meteorological data and estimates the RVR after 1 hour and confirms its validity by comparing with measured RVR after 1 hour. The proposed estimation model could be used for the RVR after 1 hour as reference in small airports in regions which do not forecast the RVR.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.760-766
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• 2008

This paper proposes an efficient location technique to find an indoor location under the IEEE 802.11 wireless local area networks. The proposed method is based on the range measurements obtained from a simple radio frequency propagation model. Thus, unlike the radio frequency fingerprint correlation method, it does not suffer from the computational burden during the real-time location service period and can quickly reply the location requests of many users at the same time. To increase the location accuracy in spite of the frequent non-line-of-sight error occurrences, the proposed method calibrates the distortion of the non-line-of-sight error by a simple measurement surveying procedure that does not require the surveyor's manual interaction. Experimental results show the capability of the proposed method.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.16-26
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• 2019

Wetlands that provide diverse ecosystem services, such as habitat provision and hydrological control of flora and fauna, constitute ecosystems through interaction between wetlands existing in a wetlandscape. Therefore, to evaluate the wetland functions such as resilience, it is necessary to analyze the ecological connectivity that is formed between wetlands which also show hydrologically dynamic behaviors. In this study, by defining wetlands as ecological nodes, we generated ecological networks through the connection of wetlands according to the dispersal model of wetland species. The characteristics of these networks were then analyzed using various network metrics. In the case of the dispersal based on a threshold distance, while a high local clustering is observed compared to the exponential dispersal kernel and heavy-tailed dispersal model, it showed a low efficiency in the movement between wetlands. On the other hand, in the case of the stochastic dispersion model, a low local clustering with high efficiency in the movement was observed. Our results confirmed that the ecological network characteristics are completely different depending on which dispersal model is chosen, and one should be careful on selecting the appropriate model for identifying network properties which highly affect the interpretation of network structure and function.

• The Korean Journal of Community Living Science
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• v.17 no.4
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• pp.67-80
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• 2006

This study conducted research on the actual state of community-oriented services for elderly rural inhabitants and their desire related to them to develop a local community service network model suitable to the characteristics of rural longevity villages. The research was conducted on 906 elderly people over 65 living in 20 rural longevity villages through questionnaires assessing filming and economy, economic activity, health care, learning and leisure activities as well as asking their wants and needs relative to local community services. As a result, it was found rural elderly people showed a high desire for local community services such as health, transportation and economy activity. In addition, they were mainly cultivating farm products as their economic activity and showed a high demand in the future as well. Most were found to take a walk in the healthcare field and showed a high demand for health examinations, health education, health consulting, hot spring bathing and basking in the woods. Respecting learning, social and leisure activities, they were mostly found to watch TV and do house chores, and showed a high desire for village environment repair, traditional farm music, visiting and tourism. With the above results, it is expected that the desire of rural elderly for such services can be satisfied, and the development of a local community service network model suitable to the characteristic of a local community is recommended.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• Tunnel and Underground Space
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• v.8 no.4
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• pp.275-286
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• 1998

The effective permeability and the representative element volume(REV) of fracture network model were evaluated based on the parameters such as permeability tensor, principal permeability and the direction of principal permeability. The effective permeability ranges between the harmonic mean and the arithmetic mean of the local permeabilities of subdivided blocks. From the numerical experiments, which were for investigating the influence of model volume on the variation of flux for the cubic models, it was found that the variation of flux became reduced as the model volume approached REV. The variation of groundwater flux into the tunnel in the fracture network model was mainly dependent on the ratio of the tunnel length to model size rather than REV. And it was found that groundwater flux into the tunnel was not completely consistent between the fracture network model and the equivalent porous media model, especially when the ratio of the tunnel length to model size is small.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.69-77
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• 2001

An artificial neural network model was developed to analyze and forecast daily steamflow flow a small watershed. Error Back propagation neural networks (EBPN) of daily rainfall and runoff data were found to have a high performance in simulating stremflow. The model adopts a gradient descent method where the momentum and adaptive learning rate concepts were employed to minimize local minima value problems and speed up the convergence of EBP method. The number of hidden nodes was optimized using Bayesian information criterion. The resulting optimal EBPN model for forecasting daily streamflow consists of three rainfall and four runoff data (Model34), and the best number of the hidden nodes were found to be 13. The proposed model simulates the daily streamflow satisfactorily by comparison compared to the observed data at the HS#3 watershed of the Baran watershed project, which is 391.8 ha and has relatively steep topography and complex land use.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1586-1589
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• 2005

GUMF (Global User Management Framework) that is proposed in this research can be applied to next generation network such as BcN (Broadband convergence Network), it is QoS guaranteed security framework for user that can solve present Internet's security vulnerability. GUMF offers anonymity for user of service and use the user's real-name or ID for management of service and it is technology that can realize secure QoS. GUMF needs management framework, UMS (User Management System), VNC (Virtual Network Controller) etc. UMS consists of root UMS in country dimension and Local UMS in each site dimension. VNC is network security equipment including VPN, QoS and security functions etc., and it achieves the QoSS (Quality of Security Service) and CLS(Communication Level Switching) functions. GUMF can offer safety in bandwidth consumption attacks such as worm propagation and DoS/DDoS, IP spoofing attack, and current most attack such as abusing of private information because it can offer the different QoS guaranteed network according to user's grades. User's grades are divided by 4 levels from Level 0 to Level 3, and user's security service level is decided according to level of the private information. Level 3 users that offer bio-information can receive secure network service that privacy is guaranteed. Therefore, GUMF that is proposed in this research can offer profit model to ISP and NSP, and can be utilized by strategy for secure u-Korea realization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3041-3063
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• 2018

Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric. This problem has recently drawn intense research interest. However, most metric-based algorithms tend to include irrelevant subgraphs in the identified community. Apart from the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of the k-hop and local distance dynamics model, which can naturally capture a community that contains the query node. The basic idea is to envision the nodes that k-hop away from the query node as an adaptive local dynamical system, where each node only interacts with its local topological structure. Relying on a proposed local distance dynamics model, the distances among nodes change over time, where the nodes sharing the same community with the query node tend to gradually move together, while other nodes stay far away from each other. Such interplay eventually leads to a steady distribution of distances, and a meaningful community is naturally found. Extensive experiments show that our community search algorithm has good performance relative to several state-of-the-art algorithms.