• Journal of Contemporary Eastern Asia
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• v.17 no.2
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• pp.237-253
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• 2018

In order to understand China's internet governance, this paper examined 1,931 Internet policies of China by bibliometric techniques. Specifically, the bibliometric techniques include simple document counting, co-word analysis, collaboration network analysis and citation analysis. The findings include: (1) China's Internet legislations mainly emphasized e-commerce and Internet governance, and, to some extent, neglected personal data protection; (2) China's Internet is under intensive multiple regulatory controls by central government. A large number of government agencies are involved in Internet policy-making. The Propaganda Department of the Central Committee of the Communist Party of China and the State Information Leading Group of the State Council, enforced fewer policy documents, but occupy higher positions in the Internet governance hierarchy; (3) China's Internet legislation system is primarily composed of industry-specific administrative rules, rather than laws or administrative regulations. Nevertheless, laws and administrative regulations received significantly more citations owing to their superior force. This paper also discussed current gaps in China's internet governance and how the country's internet policies are situated in the broader global context.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.53-57
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• 2010

Recently, Lee et al. proposed a delegation-based authentication protocol for secure and private roaming service in global mobility networks. In this paper, we show that the protocol cannot protect the privacy of an user even though the protocol provides the user anonymity. To prove the weakness, we show that the protocol cannot provide the unlinkability and also examine the weakness of the protocol caused by the lack of the unlinkability.

• Journal of Aerospace System Engineering
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• v.14 no.6
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• pp.1-9
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• 2020

Global e-commerce and delivery companies are actively pursuing last-mile delivery service using drones, and various delivery schedule planning studies have been conducted. In this study, separate individual route networks were constructed to reflect drone route constraints such as prohibited airspace and truck route constraints such as rivers, which previous studies did not incorporate. The A* algorithm was used to calculate the shortest path distance matrix between the starting point and destinations. In addition, we proposed an optimal delivery schedule plan using genetic algorithms and applied it to compare the efficiency with that of vehicle-only delivery.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.219-223
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• 2022

The Internet can deliver various information and services at the lowest cost without time and space constraints while targeting the world among all existing means of communication. Unlike traditional media such as TV, newspapers, and radio in the past, promotions through mobile environments allow target customers to use two-way low-cost, high-efficiency promotional strategies regardless of time and place. With the development of the Internet, social media has developed into a place to acquire information about favorite companies and their products. Social media greatly contributes to the production of text, photos, videos, and various networks, and has expanded global communication and communication media through the interaction and sharing of various information. In addition, through social media, users can communicate in various ways, reveal themselves, and share and exchange information such as knowledge and personal thoughts. In line with these changes, corporate marketers and sellers are striving to provide consumers with appropriate information more quickly. We aims to find out about social media marketing strategies useful for companies.

• Annual Conference of KIPS
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• 2009.11a
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• pp.541-542
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• 2009

MANET은 유선 기반망의 도움 없이 무선 노드들 간에 서로 협력하여 다중-홉으로 정보를 주고 받을 수 있도록 해주는 네트워크이다. 최근 MANET과 인터넷 망과의 통신이 요구되고 있고 이를 위해 무선 노드가 글로벌 IP 주소를 할당 받을 수 있어야 한다. 본 논문에서는 인터넷 게이트웨이를 통해 인터넷 망과 연결된 MANET 구조에서 무선 노드가 빠르게 글로벌 IP 주소를 설정할 수 있는 기법을 제안한다. 제안하는 기법의 성능 평가를 위해 NS-2로 실험을 수행했으며, 그 결과 기존 방법보다 빠르게 글로벌 IP 주소를 설정하고 네트워크 부하를 감소 시킬 수 있음을 입증하였다.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.366-370
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• 2005

The construction environment is dynamic in nature and is characterized by various degrees of uncertainties. The uncertainties such as lack of coordination, non availability of resources, condition of temporary structures and varying weather conditions have a significant impact on estimating the duration of activities. These are subjective, vague and imprecisely defined and are expressed in subjective measures rather than mathematical terms. Conventionally, various quantitative techniques such as CPM and PERT have emerged in construction industry. These techniques cannot solve the above problems and rely on human experts which may not always be possible. In such situations Artificial Intelligence tools such as fuzzy sets and neural networks handle such variables and provide global strategies. The present paper evaluates the effect of qualitative factors to identify the activity duration using new intelligent approach. The results are compared with conventional methods for effective project management. A case study is considered to demonstrate the applicability of fuzzy logic for project scheduling.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.423-426
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• 2021

본 논문은 전역 스타일 토큰(Global Style Token)을 기준으로 하여 감정의 세기를 조절할 수 있는 방법을 소개한다. 기존의 전역 스타일 토큰 연구에서는 원하는 스타일이 포함된 참조 오디오(reference audio)을 사용하여 음성을 합성하였다. 그러나, 참조 오디오의 스타일대로만 음성합성이 가능하기 때문에 세밀한 감정 조절에 어려움이 있었다. 이 문제를 해결하기 위해 본 논문에서는 전역 스타일 토큰의 레퍼런스 인코더 부분을 잔여 블록(residual block)과 컴퓨터 비전 분야에서 사용되는 AlexNet으로 대체하였다. AlexNet은 5개의 함성곱 신경망(convolutional neural networks) 으로 구성되어 있지만, 본 논문에서는 1개의 신경망을 제외한 4개의 레이어만 사용했다. 청취 평가(Mean Opinion Score)를 통해 제시된 방법으로 감정 세기의 조절 가능성을 보여준다.

• Korea Trade Review
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• v.47 no.4
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• pp.35-52
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• 2022

Air transport is at the core of global economic growth. There is a close relationship between the flow of regional elements and the growth of the regional economy. It is easy to misestimate the impact of airports on the regional economy without considering spatial spillover effects. This study attempted to establish an asymmetric economic geographical weight matrix by applying the Spatial Durbin Model with cargo volume and passenger numbers as indicators. The influence of spillover effects on the regional economy, including direct spillover effects and indirect spillover effects are studied. The results revealed that passenger numbers and cargo volume have significant positive spillover effects on the regional economy. The driving effect of the airport on the regional economy was considered from a wider space scope. This study contributes to the scientific evaluation of the aviation economy.