• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.766-772
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• 2009

Generally, wind industry has been oriented to large power systems which require large windy areas and often need to overcome environment restrictions. However, small-scale wind turbines are closer to the consumers and have a large market potential, and much more efforts are required to become economically attractive. In this paper, a prototype of a small-scale urban wind generation system for battery charging application is described and a neural network based MPPT(Maximum Power Point Tracking) algorithm which can be effectively applied to urban wind turbine system is proposed. Through Matlab based simulation studies and actual implementation of the proposed algorithm, the feasibility of the proposed scheme is verified.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.373-380
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• 2022

The field of land surveying is changing dramatically due to the way data is processed, analyzed and presented. Also, there is a growing demand for digital spatial information, coming primarily from the GIS (Geographical Information System) user community. Such a demand has created a strong development potential for a new land surveying software. An overview of the development and capabilities of a land surveying software platform based on the Windows system, SurveyingMap, is presented. Among its many features, SurveyingMap provides a lot of adaptability for networks adjustment, geodetic and plane coordinates transformation, contouring, sectioning, DTM (Digital Terrain Model) generation, and large scale mapping applications. The system output is compatible with well known computer aided drafting (CAD) /GIS packages to expand its scope of applications. SurveyingMap is also suitable for non-technical users due to the user-friendly graphic user interface. The system could be used in engineering, architecture, GIS, and academic teaching and research, among other fields. Two applications of SurveyingMap, extension of field control and large scale mapping, for the case study area are established. The results demonstrate that the system is adaptable and reasonably priced for use by college and university students.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.89-95
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• 2023

In recent decades, human action recognition (HAR) has demonstrated potential applications in sports analysis, human-robot interaction, and large-scale signage content. In this paper, spatial temporal attention graph convolutional network (STAGCN)-based HAR system is proposed. Spatioal-temmporal features of skeleton sequences are assigned different weights by STAGCN, enabling the consideration of key joints and viewpoints. From simulation results, it has been shown that the performance of the proposed model can be improved in terms of classification accuracy in the NTU RGB+D dataset.

• Journal of Information Management
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• v.43 no.4
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• pp.31-48
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• 2012

Social network analysis was performed on 545 job descriptions in 32 university libraries in Seoul, and drew the job distribution and their relations. Furthermore, for finding the differences according to scale of libraries, this study performed secondary analysis by dividing them two groups. Results show that large scale library show lower density and loose connectivity than small scale library. And while jobs of small scale library were clustered 3 groups, large scale university cluster 4 groups containing 1 technical job and 3 diverse user services. And the jobs that has high specificity, such as catalog or classification, shows high degree centrality in the case of small scale library. Whereas in large scale library they show lower degree centrality, so it can be said that these jobs were performed somewhat independently in large scale libraries.

• Smart Structures and Systems
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• v.30 no.6
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.598-600
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• 2004

Mobile Ad hoc Network (MANET)는 자율적으로 작동할 수 있는 멀티 홈 무선 네트워크로서 토폴로지 변화가 자주 일어나며 예측할 수 없는 특성을 갖는다. 특히 scale 이 큰 MANET 환경에서는 더욱더 예측할 수 없는 특성을 갖게 된다. 최근 MANET에서의 라우팅을 하기 위한 주소 할당에 관심이 모아지고 있는 상황에서 각 노드는 DHCP 같은 서버의 역할 없이 주소를 할당할 수 있는 기능을 가져야 하며, 이동 단말들이 모두 IP stack을 지원하는 것을 감안해서 IP주소를 사용해야할 것이다. 따라서 본 논문에서는 scale이 큰 MANET 환경에서 동적으로 IP 주소를 할당하는 방법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3602-3620
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• 2016

Dynamic topology is one of the main influence factors on network performability. However, it was always ignored by the traditional network performability assessment methods when analyzing large-scale mobile ad hoc networks (MANETs) because of the state explosion problem. In this paper, we address this problem from the perspective of complex network. A two-layer hierarchical modeling approach is proposed for MANETs performability assessment, which can take both the dynamic topology and multi-state nodes into consideration. The lower level is described by Markov reward chains (MRC) to capture the multiple states of the nodes. The upper level is modeled as a small-world network to capture the characteristic path length based on different mobility and propagation models. The hierarchical model can promote the MRC of nodes into a state matrix of the whole network, which can avoid the state explosion in large-scale networks assessment from the perspective of complex network. Through the contrast experiments with OPNET simulation based on specific cases, the method proposed in this paper shows satisfactory performance on accuracy and efficiency.

• Human Ecology Research
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• v.51 no.5
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• pp.473-481
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• 2013

The purpose of this study was to investigate the moderating effects of a size of the friendship network and quality of friendship on the associations between mutual antipathy and maladjustment. The subjects were 678 fifth- and sixth-grade primary school children who were recruited from a public school in Bucheon City. The Peer Nomination Inventory was used to assess mutual antipathy, peer victimization, social withdrawal, aggression, and the friendship network. The children were given a classroom roster and asked to nominate up to three classmates who fit each description. Additionally, the children reported the quality of their friendships using the Friendship Quality Scale. Each child was asked to indicate his or her one best friend and rate how accurately a sentence describe done of their best friends on the scale. The results revealed that the friendship network and friendship quality significantly moderated the relationships between mutual antipathy and social withdrawal, and peer victimization. The magnitude of the association between mutual antipathy and social withdrawal was not significant for large friendship networks and high quality friendships. Although mutual antipathy was significantly associated with peer victimization, the association was stronger at lower levels than at higher levels of the friendship network and quality. However, there was no moderating effect of the friendship network and quality on the association between mutual antipathy and aggression. A large friendship network and high quality friendship could be protective factors among those who have mutual antipathy in peer groups.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.614-621
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• 2018

This paper proposes a method of restoring corrupted depth image captured by depth camera through unsupervised learning using generative adversarial network (GAN). The proposed method generates restored face depth images using 3D morphable model convolutional neural network (3DMM CNN) with large-scale CelebFaces Attribute (CelebA) and FaceWarehouse dataset for training deep convolutional generative adversarial network (DCGAN). The generator and discriminator equip with Wasserstein distance for loss function by utilizing minimax game. Then the DCGAN restore the loss of captured facial depth images by performing another learning procedure using trained generator and new loss function.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.3
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• pp.267-275
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• 1987

This paper concerns a study on the theory of tearing which analyzes a large scale network by partitioning it into a number of small subnetworks by cutting through some of the existing nodes and branches in the network. By considering of the relationship its voltage and current of node cutting before and after, the consititutive equations of tearing method is equvalent to renumbering the nodes of untorn network equations. Therefore the analysis of network is conveniently applied as same algorithm that is used in untorn network. Also the proposed nodal admittnace matrix is put in block diagonal form, therefore this method permit parallel processing analysis of subnetworks. 30 nodes network was tested and the effectiveness of the proposed algorithm was proved.