• 한국경제지리학회지
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• 제16권1호
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• pp.50-70
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• 2013

창조경제 새로운 패러다임의 변화는 사회적 관계의 변화에 영향을 끼치고 있으며, 소셜 네트워크 서비스 등의 발전에 따라 나타나는 새로운 관계의 공간적 특성에도 영향을 끼치고 있다. 본 논문에서는 이러한 변화에 영향을 미치는 동력을 '소셜 네트워크 리소스(social network resource)'로 명명하고, 그에 따라 나타나는 제반 특징과 경제지리학적 관점에서의 공간적 특성을 설명하고자 하였다. '소셜 네트워크 리소스'는 개방성과 공유, 참여, 협력의 특징을 보여주는 동시에, 공간적으로는 로컬과 글로벌의 특징을 모두 아우르는 소위 '트랜스 로컬리티'의 특성을 보이고 있는데, 서울시의 사회적 지식공유 웹 플랫폼인 '위키서울닷컴'의 사례를 통해 그러한 특성을 확인할 수 있었다. 특히, 물리적자원, 인적자원, 정보자원의 특성과 함께 관계자원으로서의 특징이 모두 나타나고 있으며, 이러한 특징에 공간이 투영되어, 사회적 관계가 공간에 표출되는 질적 공간의 특성 또한 나타남을 확인할 수 있었다.

• 천문학논총
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• 제22권4호
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• pp.189-199
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• 2007

In this paper, we describe the proposed KVN (Korean VLBI Network) clock system in order to make the observation of the VLBI effectively. In general, the GPS system is widely used for the time information in the single dish observation. In the case of VLBI observation, a very high precise frequency standard is needed to perform the observation in accordance with the observation frequency using the radio telescope with over 100km distance. The objective of the high precise clock system is to insert the time-tagging information to the observed data and to synchronize it with the same clock in overall equipments which used in station. The AHM (Active Hydrogen Maser) and clock system are basically used as a frequency standard equipments at VLBI station. This system is also adopted in KVN. The proposed KVN clock system at each station consists of the AHM, GPS time comparator, standard clock system, time distributor, and frequency standard distributor. The basic experiments were performed to check the AHM system specification and to verify the effectiveness of implemented KVN clock system. In this paper, we briefly introduce the KVN clock system configuration and experimental results.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 추계학술발표대회
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• pp.234-237
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• 2014

This paper provides insights into the sFlow monitoring system of OF@KREONET. OF@KREONET is software defined network (SDN) testbed adapted by KREONET (Korea Research Environment Open NETwork). OF@KREONET uses SDN-based network virtualization to slice the network among multiple concurrent experimenter. Flow Monitoring of OF@KREONET using sFlow. sFlow and OpenFlow can be used to provide an integrated flow monitoring system where OpenFlow controller can be used to define flows to be monitored by sFlow. OF@KREONET flow monitoring system supports monitoring of per slice FlowSpace. An Experimental can monitor his/her own FlowSpace while network administrator can monitor all spaces.

• Nuclear Engineering and Technology
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• 제32권5호
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• pp.485-494
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• 2000

In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique . Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.51-56
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• 2022

In space based constellation network, users are allowed to enter or leave the network arbitrarily. Hence, the number, identities and transmitted data of active users vary with time and have considerable impacts on the receiver's performance. The so-called problem of multiuser detection means identifying the identity of each active user and detecting the data transmitted by each active user. Traditional methods assume that the number of active users is equal to the maximum number of users that the network can hold. The model of traditional methods are simple and the performance are suboptimal. In this paper a Maximum A Posteriori Probability (MAP) based multiuser detection method is proposed. The proposed method models the activity state of users as Markov chain and transforms multiuser detection into searching optimal path in grid map with BCJR algorithm. Simulation results indicate that the proposed method obtains 2.6dB and 1dB E_b/N₀ gains respectively when activity detection error rate and symbol error rate reach 10^-3, comparing with reference methods.

• Journal of information and communication convergence engineering
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• 제10권4호
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• pp.349-358
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• 2012

Over a wireless sensor network (WSN), accurate localization of sensor nodes is an important factor in enhancing the association between location information and sensory data. There are many research works on the development of a localization algorithm over three-dimensional (3D) space. Recently, the complexity-reduced 3D trilateration localization approach (COLA), simplifying the 3D computational overhead to 2D trilateration, was proposed. The method provides proper accuracy of location, but it has a high computational cost. Considering practical applications over resource constrained devices, it is necessary to strike a balance between accuracy and computational cost. In this paper, we present a novel 3D localization method based on the received signal strength indicator (RSSI) values of four anchor nodes, which are deployed in the initial setup process. This method provides accurate location estimation results with a reduced computational cost and a smaller number of anchor nodes.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.1023-1026
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• 2005

In the ubiquitous computing environment, an intelligent vehicle is defined as a sensor node with a capability of intelligence and communication in a wire and wireless network space. To make it real, a lot of problems should be addressed in the aspect of vehicle mobility, in-vehicle communication, common service platform and the connection of heterogeneous networks to provide a driver with several intelligent information services beyond the time and space. In this paper, we present an intelligent information system for managing in-vehicle sensor network and a vehicle gateway for connecting the external networks. The in-vehicle sensor network connected with several sensor nodes is used to collect sensor data and control the vehicle based on CAN protocol. Each sensor node is equipped with a reusable modular node architecture, which contains a common CAN stack, a message manager and an event handler. The vehicle gateway makes vehicle control and diagnosis from a remote host possible by connecting the in-vehicle sensor network with an external network. Specifically, it gives an access to the external mobile communication network such as CDMA. Some experiments was made to find out how long it takes to communicate between a vehicle's intelligent information system and an external server in the various environment. The results show that the average response time amounts to 776ms at fixed place, 707ms at rural area and 910ms at urban area.

• 한국통신학회논문지
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• 제32권9C호
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• pp.861-866
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• 2007

본 논문은 가우시안 확률분포함수 (Gaussian Probability Distribution Function) 데이터 군집화를 위해 중심신경망 (Centroid Neural Network, CNN)에 Bhattacharyya 커널을 적용한 군집화 알고리즘 (Bhattacharyya Kernel based CNN, BK-CNN)을 제안한다. 제안된 BK-CNN은 무감독 알고리즘인 중심신경망을 기반으로 하고 있으며, 커널 방법을 이용하여 데이터를 특징공간에서 투영한다. 입력공간의 비선형 문제를 선형적으로 해결하기 위해 제안한 커널 방법인데, 확률분포 사이의 거리측정을 위해 Bhattacharyya 거리를 이용한 커널방법을 사용하였다. 제안된 BK-CNN을 영상데이터 분류의 문제에 적용했을 때, 제안된 BK-CNN 알고리즘이 Bhattacharyya 커널을 적용한 k-means, 자기조직지도(Self-Organizing Map)와 중심 신경망등의 기존 알고리즘보다 1.7% - 4.3%의 평균 분류정확도 향상을 가져옴을 확인할 수 있었다.

• 정보관리학회지
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• 제26권3호
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• pp.7-24
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• 2009

학술적 웹 공간을 대상으로 하는 연구는 페이지와 링크의 역동성 때문에 정량적인 방법과 함께 내용 분석 등의 정성적인 방법을 사용하는 것이 필요하다. 따라서 이 연구에서는 내용 분석의 한 방법으로 한국 학술적 웹 공간 내에서 외부 링크로 연결된 페이지 및 링크의 유형을 분류한 후 이를 네트워크 구조 분석에 반영하여 한국 학술적 웹 공간의 특성을 자세히 살펴보았다. 분석 결과 데이터의 수집 시점을 나타내는 기본 네트워크와 내용 분석 시점을 나타내는 활성 네트워크 사이에 구조적으로 큰 차이가 없었으나, 기관 유형별로 다른 기관들을 링크하는 목적이 다르게 나타났다. 그리고 한국 학술적 웹 공간은 여러 중앙성 지수들과 결속계수 간의 설명력이 유사하게 나타나는 형태의 네트워크임을 확인하였다.