• Journal of the Economic Geographical Society of Korea
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• v.16 no.1
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• pp.50-70
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• 2013

The creation of new economic paradigm shift in creative economy age have influence on the characteristics of social networks and space, it leads to the formation of new relationship in space depending on social network service development. In this paper, it gives a name to 'social network resource' the power affecting these features and to find the meaning of spatial changes in the economic geography perspectives. 'Social network resource' shows the characteristics of openness, sharing, participation and cooperation, with features of encompassing all the features of local and global characteristics in space. This features are related the meaning of 'trans-locality' and can be found in the case of 'WikiSeoul.com (http:/www.wikiseoul.com)', Seoul's social knowledge sharing web platform. In particular, physical resources, human resources, information resources, and the characteristics of the relationship as a resource features was found and these features appear in space is projected to the space of social relations, it reflects the characteristics of qualitative space regarding social network resource.

• Publications of The Korean Astronomical Society
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• v.22 no.4
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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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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• v.32 no.5
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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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• v.14 no.6
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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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• v.22 no.12
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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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• v.10 no.4
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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.

• Proceedings of the IEEK Conference
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• 2005.11a
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.861-866
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• 2007

A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.

• Journal of the Korean Society for information Management
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• v.26 no.3
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• pp.7-24
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• 2009

Since the Web is dynamic, it is necessary to analyze scholarly Web space with both quantitative and qualitative methods for better understanding of communication characteristics. In this study, we analyzed contents of pages and links to ascertain the characteristics of Korean scholarly Web space in terms of network structure and communication behavior. The result shows that the structure of the original network with all the external links remained is not much different from that of the network with activated external links only. However, the purposes of linking vary among scholarly institutions. The centrality measures correlate more strongly with the clustering coefficient than with the constraint index implying the similar explanatory power of the two types of structural indices.