• 한국사회복지학
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• 제59권3호
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• pp.281-304
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• 2007

사회복지 서비스 부문에서 최근 상호조직간 네트워크의 필요성이 증가하면서 상호조직간 네트워크 사업이 활성화되고 있다. 이와 함께, 네트워크 성과에 대한 책임성의 요구도 증가하고 있다. 네트워크 성과를 성공적으로 수행하기 위해서는 성과에 관한 상호조직 간의 공통된 이해의 틀을 갖추는 것이 필요하며, 성과 개념에 관한 합의 도출이 이루어져야 한다. 따라서, 본 연구는 사회복지 실천현장에서 각기 다른 이해관계에 놓인 조직들이 네트워크 성과측정을 기획하기 위한 기초 작업으로서 성과의 개념적 틀을 개발하고 개념적 합의를 도출할 수 있도록 돕는 concept mapping(개념도 연구법)을 적용한 사례를 소개하고 실천현장에서의 활용가능성을 모색해 보았다. 개념도 연구법을 적용한 결과 협력적인 수행과 지속적인 의사결정 과정 속에 놓여 있는 네트워크 조직에서의 성과측정 기획을 위한 실천적 함의를 발견할 수 있었으며, 사회복지 실천에의 적용 유용성을 향상하기 위한 대안들을 제시하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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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.

• 한국측량학회지
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• 제30권6_2호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 95 KFIS Workshop Realization of Human Friendly System Based on Soft Computiong Techniques
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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.

• 대한전기학회논문지
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• 제43권6호
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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.

• 한국통신학회논문지
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• 제41권12호
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• pp.1793-1800
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• 2016

네트워크의 가상화는 최근에 등장한 클라우드 서비스, 데이터 센터 네트워크 등 새로운 서비스를 기존의 네트워크 환경에서 제공하기 위한 하나의 방편으로 인식되고 있다. 본 논문에서는 네트워크 가상화를 위한 가상 네트워크 대응 방안을 가상 노드의 위치, 자원, 가상 링크의 대역폭 요구조건을 고려하면서 효율적인 방안을 제안한다. 제안한 방안은 대응의 우선순위를 대역폭으로 설정하여 대응 가능한 실제 노드들 간의 경로가 존재하는 조합을 발견할 때까지 반복하는 방법을 사용한다. 모의실험 결과 제안한 방안이 가상 네트워크 대응 성공률은 떨어지지 않으면서도 수익률은 높고 실행시간도 빠른 효율적인 방안임을 확인하였다.

• 한국측량학회지
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• 제36권5호
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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.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2001년도 춘계학술대회논문집:21세기 신지식정보의 창출
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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.

• 한국인터넷방송통신학회논문지
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• 제12권3호
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• pp.171-177
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• 2012

최근 복수개의 단말을 가진 사용자들이 끊김없는 연결성을 유지하기 위한 망 이동성 지원에 관한 연구가 진행되어 왔다. 본 논문에서는 LISP 구조에서 망 이동성 지원 스킴을 제안하였다. 제안된 스킴에서 모바일 라우터 접속 동안에 맵 서버에서 EID-to-RLOC 매핑 데이터베이스가 리프레쉬(refresh)된다. 또한 이동 망을 위한 자연스런(smooth) 핸드오프를 지원하기 위한 맵 갱신 방법을 제안하였으며, 성능 분석을 위해 제안된 스킴을 NEMO와 비교하였다.

• ETRI Journal
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• 제41권2호
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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.