• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3935-3949
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• 2021

With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.877-893
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• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.

• 한국정보통신학회논문지
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• 제17권10호
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• pp.2281-2287
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• 2013

네트워크 코딩은 통신의 새로운 패러다임이다. 네트워크 코딩에서 중간 노드는 입력 패킷을 조합하여 새로운 패킷을 생성하여 이웃 노드에게 방송한다. 네트워크 코딩은 실제 네트워크에 폭넓게 적용될 수 있기 때문에 정보 이론의 주요 연구 분야로 빠르게 떠오르고 있다. 네트워크 코딩은 무선 멀티홉 네트워크에서 처리량과 채널 효율을 향상시킬 것으로 예상된다. 관련 선행연구들이 무선 Ad-hoc 네트워크 분야에서 활발히 이루어지고 있다. 우리의 연구에서 중간노드는 네트워크 코딩을 하기위해서 단일홉 양방향 패킷을 식별한다. 우리는 제안된 기법이 네트워크 패킷의 디코딩 성공률을 증가시킬 수 있을 것으로 기대한다. 컴퓨터 시뮬레이션에서 제안된 네트워크 코딩기법은 코딩 이득과 패킷 전송률에서 기존의 네트워크 코딩기법보다 나은 성능을 얻을 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제22권4호
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• pp.41-49
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• 2017

Word sense disambiguation(WSD) that determines the exact meaning of homonym which can be used in different meanings even in one form is very important to understand the semantical meaning of text document. Many recent researches on WSD have widely used NNLM(Neural Network Language Model) in which neural network is used to represent a document into vectors and to analyze its semantics. Among the previous WSD researches using NNLM, RNN(Recurrent Neural Network) model has better performance than other models because RNN model can reflect the occurrence order of words in addition to the word appearance information in a document. However, since RNN model uses only the forward order of word occurrences in a document, it is not able to reflect natural language's characteristics that later words can affect the meanings of the preceding words. In this paper, we propose a WSD scheme using Bidirectional RNN that can reflect not only the forward order but also the backward order of word occurrences in a document. From the experiments, the accuracy of the proposed model is higher than that of previous method using RNN. Hence, it is confirmed that bidirectional order information of word occurrences is useful for WSD in Korean language.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.245-265
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• 2022

In response to problems such as insufficient extraction information, low detection accuracy, and frequent misdetection in the field of Thangka image defects, this paper proposes a YOLOv5 prediction algorithm fused with the attention mechanism. Firstly, the Backbone network is used for feature extraction, and the attention mechanism is fused to represent different features, so that the network can fully extract the texture and semantic features of the defect area. The extracted features are then weighted and fused, so as to reduce the loss of information. Next, the weighted fused features are transferred to the Neck network, the semantic features and texture features of different layers are fused by FPN, and the defect target is located more accurately by PAN. In the detection network, the CIOU loss function is used to replace the GIOU loss function to locate the image defect area quickly and accurately, generate the bounding box, and predict the defect category. The results show that compared with the original network, YOLOv5-SE and YOLOv5-CBAM achieve an improvement of 8.95% and 12.87% in detection accuracy respectively. The improved networks can identify the location and category of defects more accurately, and greatly improve the accuracy of defect detection of Thangka images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1187-1208
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• 2022

One of the main goals of wireless sensor networks (WSNs) is to utilize the energy of sensor nodes effectively and maximize the network lifetime. Thus, this paper proposed a routing protocol for WSNs based on virtual force disturbing mobile Sink node (VFMSR). According to the number of sensor nodes in the cluster, the average energy and the centroid factor of the cluster, a new cluster head (CH) election fitness function was designed. At the same time, a hexagonal fixed-point moving trajectory model with the best radius was constructed, and the virtual force was introduced to interfere with it, so as to avoid the frequent propagation of sink node position information, and reduce the energy consumption of CH. Combined with the improved ant colony algorithm (ACA), the shortest transmission path to Sink node was constructed to reduce the energy consumption of long-distance data transmission of CHs. The simulation results showed that, compared with LEACH, EIP-LEACH, ANT-LEACH and MECA protocols, VFMSR protocol was superior to the existing routing protocols in terms of network energy consumption and network lifetime, and compared with LEACH protocol, the network lifetime was increased by more than three times.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2000년도 가을 학술발표논문집 Vol.27 No.2 (3)
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• pp.183-185
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• 2000

본 논문에서는 SNMP를 이용한 Nerwork Management System(NMS)이 Network을 사용하는 Application에 영향을 주지 않도록 하는 Polling 전략과 Model을 설계하였다. 제안된 System은 Network의 각 Client 정보를 처리하는 Agent와 Data 수집 및 제어를 담당하는 Server로 구성된다. Agent는 SNMP Agent 부분과 Network 상태를 Monitoring 하는 SubManager로 구성되어, Server는 SNMP Agent와의 Polling 및 Polling 정책을 결정하는 부분으로 구성된다. 제안 Model은 SNMP를 이용한 NMS를 도입할 경우, 기존 Network Service에 영향을 주지 않도록 하는 것이 목적이다. 제안된 System에 대한 성능평가를 위해 실존하는 Network을 대상으로 SNMP의 Polling 및 Service의 부하량을 측정하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 춘계학술발표대회
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• pp.16-18
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• 2018

최근 네트워크 상에 새롭고 다양한 어플리케이션들이 생겨나면서 이에 따른 적절한 어플리케이션별 서비스 제공을 위한 패킷 분류 방법이 요구되고 있다. 이로 인하여 딥 러닝 기술이 발전 하면서 이를 이용한 네트워크 트래픽 분류 방법들이 제안되고 있다. 따라서, 본 논문에서는 딥 러닝 기술 중 Convolution Neural Network 와 Recurrent Neural Network 를 동시에 활용한 네트워크 패킷 분류 방법을 제안한다.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.801-817
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• 2021

Network virtualization technologies have played efficient roles in deploying cloud, Internet of Things (IoT), big data, and 5G network. We have conducted a survey on network virtualization technologies, such as software-defined networking (SDN), network functions virtualization (NFV), and network virtualization overlay (NVO). For each of technologies, we have explained the comprehensive architectures, applied technologies, and the advantages and disadvantages. Furthermore, this paper has provided a summarized view of the latest research works on challenges and solutions of security issues mainly focused on DDoS attack and encryption.

• 인터넷정보학회논문지
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• 제18권2호
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• pp.1-11
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• 2017

스마트 디바이스를 이용하여 특정 공간에서 미리 설정된 무선랜에 접속하는 방법은 개방형 방식과 사용자 인증 방식으로 나뉠 수 있다. 개방형 방식은 무선랜 접속을 위한 인증이 없이 접속하는 방법이다. 스마트 디바이스 사용자가 자신이 사용하려는 무선랜에 대한 정보를 SSID(Service Set IDentifier)를 통해 공공 무선랜 표기 형식에 따라 제공받아야 하지만, 모든 개방형 무선랜이 이러한 방식을 수동으로 입력하는 방식에는 무리가 있다. 반면에 사용자 인증 방식은 SSID와 PW(PassWord) 설정을 통해 사용자에게 무선랜 접속을 제공하는 방식이다. 따라서 SSID를 통해 공공 무선랜 표기 형식을 따를 수는 있지만, AP 접속을 위해서는 일일이 사용자가 수동으로 패스워드 입력을 통해 AP에 접속해야만 한다. 따라서 본 논문에서는 사용자 인증방식과 공공 무선랜 표기형식을 iBeacon 메시지 수신을 통해 자동적으로 AP에 접속할 수 있는 방안을 제안한다.