• Korean Journal of Social Welfare
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• v.59 no.3
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• pp.281-304
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• 2007

The purpose of this study is to verify the applicability of concept mapping in the process of planning of network outcomes in social welfare. Planning and evaluation of network outcomes involve many stakeholders. Recognizing the value and range of individuals' perspectives in the creation of a common framework is one of the biggest methodological challenges for planning of network outcomes. Concept mapping is a kind of methodology that creates a stakeholder-authored visual geography of ideas from a group. It uses both a quantitative and qualitative approach, including brainstorming, structuring the statement, specific analysis and data interpretation methods to produce maps that can then be used to guide planning and evaluation efforts on the issues that matter to the group. 13 network managers who work in the social welfare centers in Busan are core participants. The 50 statements on network outcomes from brainstorming session fell into six distinct clusters. After the interpretation session these clusters were rated according to the seven rating scales. This paper explores applicability of concept mapping in the process of planning of network outcomes in social welfare. Concept mapping helps stakeholders with different value and ideas about network outcomes to consensus on common conceptual framework. In addition, a multidimensional conceptualization of network outcomes was made. It will assist in designing future outcomes evaluation and guide the evaluators through a selection of key activities and outcomes.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.41.5-41
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• 2001

A novel method is introduced for determining the weights of a regularization network which approximates one-to-many mapping. A conventional neural network will converges to the average value when outputs are multiple for one input. The capability of proposed network is demonstrated by an example of learning inverse mapping.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.653-663
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• 2012

Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called "super-resolution mapping" to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10a
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• pp.124-140
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• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.1010-1019
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• 1994

It is shown that there exists a nonlinear mapping which transforms image features and their changes to the desired camera motion without measuring of the relative distance between the camera and the object. This nonlinear mapping can eliminate several difficulties occurring in computing the inverse of the feature Jacobian as in the usual feature-based visual feedback control methods. Instead of analytically deriving the closed form of this mapping, a Fuzzy Membership Function-based Neural Network (FMFNN) incorporating a Fuzzy-Neural Interpolating Network is used to approximate the nonlinear mapping. Several FMFNN's are trained to be capable of tracking a moving object in the whole workspace along the line of sight. For an effective implementation of the proposed FMF network, an image feature selection process is investigated. Finally, several numerical examples are presented to show the validity of the proposed visual servoing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1793-1800
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• 2016

Network virtualization is considered as an enabling technology to accomodate recently introduced new services such as cloud services and data center networks on top of the existing network environment. In this paper, an efficient virtual network mapping method is proposed which takes account of the location and resource requirements of virtual nodes and the bandwidth requirements of virtual links. The proposed method sets bandwidth as the priority metric for network mapping, and searches for a set of substrate nodes and paths that houses the virtual nodes and virtual links. Through the simulation experiments, it is verified that the proposed method provides better cost to revenue ratio and fast experiment time without degrading success rate of virtual network mapping.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.423-432
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• 2018

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.261-267
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• 2001

In this paper, we have illustrated implementations and there results of network attacks and detections. We consider two attacks, smurf attach and network mapping attack, which are one of the typical intrusions using the ICMP The NFR/sup TM/ is used to capture all of our interesting packets within the network traffic. We implement the smurf and network mapping attacks with the UNIX raw socket, and build the NFR's backend for it's detection. The N-Code programming is used to build the backend. The implementing results show the possibility of preventing illegal intruding to network systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.171-177
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• 2012

Network mobility support has been taken into consideration for users who have multiple terminals to enjoy the seamless connectivity. This paper proposes a network mobility support scheme in the LISP architecture. During the mobile router attachment, the EID-to-RLOC mapping database is refreshed in the map server. Furthermore, map update is developed to support smooth handoff for the mobile network. An analysis of performance is given by comparing the proposed scheme with NEMO.

• ETRI Journal
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• v.41 no.2
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• pp.207-223
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• 2019

Network function virtualization can significantly improve the flexibility and effectiveness of network appliances via a mapping process called service function chaining. However, the failure of any single virtualized network function causes the breakdown of the entire chain, which results in resource wastage, delays, and significant data loss. Redundancy can be used to protect network appliances; however, when failures occur, it may significantly degrade network efficiency. In addition, it is difficult to efficiently map the primary and backups to optimize the management cost and service reliability without violating the capacity, delay, and reliability constraints, which is referred to as the reliability-aware service chaining mapping problem. In this paper, a mixed integer linear programming formulation is provided to address this problem along with a novel online algorithm that adopts the joint protection redundancy model and novel backup selection scheme. The results show that the proposed algorithm can significantly improve the request acceptance ratio and reduce the consumption of physical resources compared to existing backup algorithms.