• Annual Conference of KIPS
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• 2004.05a
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• pp.1557-1560
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• 2004

무선 Ad-Hoc 네트워크를 위한 대부분의 라우팅 프로토콜들은 모든 링크가 양방향임을 가정하고 설계되었다. 그러나 현실적으로 Ad-Hoc 네트워크에 참여하는 노드들은 서로 다른 전송 파워를 가질 수 있고, 잡음 또는 간섭 등으로 인하여 지역적이고 일시적인 단 방향 링크를 야기시킬 수 있다. 기존 Ad-Hoc 라우팅 프로토콜 중의 하나인 AODV 프로토콜은 단 방향 링크 존재 시에 이를 경로에서 제거함으로써 양방향 링크만으로 구성된 경로를 다시 탐색하게 된다. 본 논문에서는 단방향 링크 존재 시에 야기될 수 있는 AODV 프로토콜의 문제점과 이를 해결하기 위한 기존 연구들을 소개하고, 나아가서 단?향 링크 존재 시에 적용할 수 있는 AODV 프로토콜을 변형한 라우팅 알고리즘을 제안한다.

• Annual Conference of KIPS
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• 2004.05a
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• pp.1067-1070
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• 2004

우리는 갈수록 지능화, 분산화, 자동화 되어 가고 있는 침입에 대해 효과적으로 대처하기 위해 신경망을 적용한 침입탐지 시스템을 설계 하였다. 본 논문은 신경망을 학습시키기 위해 학습 견본과 신경망 적용 인자를 정의 하였으며 학습 기법으론 MLP(Multi Layer Perceptron)을 이용 하였다. 새롭게 설계된 침입탐지 시스템의 탐지 모듈은 기존의 패턴 매치 방식의 모듈과 신경망 모듈이 적용되어 보다 정확한 침입 탐지가 가능하다.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.17-26
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• 2019

The Constrained Application Protocol (CoAP) is a specialized web transfer protocol proposed by the IETF for use in IoT environments. CoAP was designed as a lightweight machine-to-machine protocol for resource constrained environments. Due to the strength of low overhead, the number of CoAP devices is expected to rise rapidly. When CoAP runs over UDP for wireless sensor networks, CoAP needs to support congestion control mechanisms. Since the default CoAP defines a minimal mechanism for congestion control, several schemes to improve the mechanism have been proposed. To keep CoAP lightweight, the majority of the schemes have been focused mainly on how to measure RTT accurately and how to set RTO adaptively according to network conditions, but other approaches such as rate-based congestion control were proposed more recently. In this paper, we survey the literature on congestion control for CoAP and discuss the future research directions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.565-581
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• 2019

This paper investigates the wireless powered two way relay network (WPTWRN), where two single-antenna users and one single-antenna relay firstly harvest energy from signals emitted by a multi-antenna power beacon (PB) and then two users exchange information with the help of the relay by using their harvested energies. In order to improve the energy transfer efficiency, energy beamforming at the PB is deployed. For such a network, to explore the performance limit of the presented WPTWRN, an optimization problem is formulated to obtain the achievable rate region bounds by jointly optimizing the time allocation and energy beamforming design. As the optimization problem is non-convex, it is first transformed to be a convex problem by using variable substitutions and semidefinite relaxation (SDR) and then solve it efficiently. It is proved that the proposed method achieves the global optimum. Simulation results show that the achievable rate region of the presented WPTWRN architecture outperforms that of wireless powered one way relay network architecture. Results also show that the relay location has significant impact on achievable rate region of the WPTWRN.

• International Journal of Contents
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• v.15 no.2
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• pp.29-37
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• 2019

This study investigates how adolescents' smartphone attachment, social anxiety, and offline and smartphone network sizes are related to their friendship in regards to maintaining either a strong or weak bond. Based on an online survey involving 402 adolescent smartphone users in South Korea, this study found that smartphone attachment was positively related to a strong ties friendship maintenance and negatively related to weak ties friendships. Similarly, social anxiety was found to be negatively associated with friendship maintenance for both strong and weak - tie relationships. More importantly, this study revealed that the types and size of social networks moderated the relationships among adolescents with smartphone attachment, social anxiety, and friendship maintenance through smartphones.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.138-140
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• 2020

As the IoT is applied to home and industrial networks, data generated by the IoT is being processed at the cloud edge. Intrusion detection function is very important because it can be operated by invading IoT devices through the cloud edge. Data delivered to the edge network in the cloud environment is traffic at the application layer. In order to determine the intrusion of the packet transmitted to the IoT, the intrusion should be detected at the application layer. This paper proposes the intrusion detection function at the application layer excluding normal traffic from IoT intrusion detection function. As the proposed method, we obtained the intrusion detection result by decision tree method and explained the detection result for each feature.

• 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.13 no.3
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• pp.42-49
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• 2021

As cyber attacks become more intelligent, there is difficulty in detecting advanced attacks in various fields such as industry, defense, and medical care. IPS (Intrusion Prevention System), etc., but the need for centralized integrated management of each security system is increasing. In this paper, we collect big data for intrusion detection and build an intrusion detection platform using deep learning and CNN (Convolutional Neural Networks). In this paper, we design an intelligent big data platform that collects data by observing and analyzing user visit logs and linking with big data. We want to collect big data for intrusion detection and build an intrusion detection platform based on CNN model. In this study, we evaluated the performance of the Intrusion Detection System (IDS) using the KDD99 dataset developed by DARPA in 1998, and the actual attack categories were tested with KDD99's DoS, U2R, and R2L using four probing methods.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.23-29
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• 2022

To improve spectrum and energy efficiency in the fifth generation (5G) wireless channels, intelligent reflecting surface (IRS) transmissions have been envisioned, possibly towards the sixth generation (6G) networks. In this paper, we analyze the bit-error rate (BER) performance of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. First, we derive a closed-form expression of the BER in terms of Q functions. Then we analyze the BER improvement of the IRS NOMA system over the conventional NOMA system with respect to the power allocation. Furthermore, we also demonstrate numerically the BER improvement of the IRS NOMA network over the conventional NOMA network in respect of the number of reflecting devices.