• 한국정보시스템학회지:정보시스템연구
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• 제21권3호
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• pp.163-186
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• 2012

With the development of Internet and transition to information society, social capital is expanding to online from the traditional offline context. Especially with the widespread of social network service(SNS) the number of SNS users is increasing sharply and the importance of online social capital has been more and more significant. Most studies on social capital focused on organizational aspects but few studies have payed attention to personal aspect. Empirical studies on the relation between SNS and social capital were seldom conducted in previous studies. Based on the theory of social capital this study targets on the relationship formed through SNS and analyzes on how the relationship affects the perceived social capital. In this study 'self-presentation', 'playfulness' and 'critical mass' are posited as the antecedent factors of 'SNS usage'. This study proposes a research model to examine the effect of 'SNS usage' on 'relationship reinforcement', 'relationship building' and 'perceived social capital'. According to the results of empirical analysis, 'self-presentation', 'playfulness' and 'critical mass' can generate significant positive influence on 'SNS usage'. It also confirms not only the effect of 'relationship reinforcement' and 'relationship building' formed through SNS on 'perceived social capital' but also relationship between the social capital formation and SNS usage. The outcome obtained in this study can be applied in developing SNS services.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 춘계학술발표대회
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• pp.212-213
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• 2015

Data aggregation is a fundamental problem in wireless sensor networks that has attracted great attention in recent years. On constructing a robust algorithm for minimizing data aggregation delay in wireless sensor networks, we consider limited transmission range sensors and approximate the minimum-delay data aggregation tree which can only be built in networks of unlimited transmission range sensors. The paper proposes an adaptive method that can be applied to maintain the network structure in case of a sensor node fails. The data aggregation tree built by the proposed scheme is therefore self-healing and robust. Intensive simulations are carried out and the results show that the scheme could adapt well to network topology changes compared with other approaches.

• 한국음향학회지
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• 제41권3호
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• pp.342-350
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• 2022

본 논문에서는 재난 환경과 같은 환경에서의 음성 처리를 위해 실제 드론 소음 데이터를 수집하여 오염 음성 데이터베이스를 구축하고 음성 향상 기법인 스펙트럼 차감법과 심층 신경망을 이용한 마스크 기반 음성 향상 기법을 적용하여 성능을 평가한다. 기존의 심층 신경망 기반의 음성 향상 모델인 VoiceFilter(VF)의 성능 향상을 위해 Self-Attention 연산을 적용하고 추정한 잡음 정보를 Attention 모델의 입력으로 이용한다. 기존 VF 모델 기법과 비교하여 Source to Distortion Ratio(SDR), Perceptual Evaluation of Speech Quality(PESQ), Short-Time Objective Intelligibility(STOI)에 대해 각각 3.77 %, 1.66 %, 0.32 % 향상된 결과를 나타낸다. 인터넷에서 수집한 오염 음성 데이터를 75 % 혼합하여 훈련한 경우, 실제 드론 소음만을 사용한 경우에 비해 상대적인 성능 하락률 평균이 SDR, PESQ, STOI에 대해 각각 3.18 %, 2.79 %, 0.96 %를 나타낸다. 이는 실제 데이터를 취득하기 어려운 환경에서 실제 데이터와 유사한 데이터를 수집하여 음성 향상을 위한 모델 훈련에 효과적으로 활용할 수 있음을 확인해준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.587-608
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• 2022

The European Union recently established the General Data Protection Regulation (GDPR) for secure data use and personal information protection. Inspired by this, South Korea revised their Personal Information Protection Act, the Act on Promotion of Information and Communications Network Utilization and Information Protection, and the Credit Information Use and Protection Act, collectively known as the "Three Data Bills," which prescribe safe personal information use based on pseudonymous data processing. Based on these bills, the personal data store (PDS) has received attention because it utilizes the MyData service, which actively manages and controls personal information based on the approval of individuals, and it practically ensures their rights to informational self-determination. Various types of PDS models have been developed by several countries (e.g., the US, Europe, and Japan) and global platform firms. The South Korean government has now initiated MyData service projects for personal information use in the financial field, focusing on personal credit information management. There is also a need to verify the efficacy of this service in diverse fields (e.g., medical). However, despite the increased attention, existing MyData models and frameworks do not satisfy security requirements of ensured traceability, transparency, and distributed authentication for personal information use. This study analyzes primary PDS models and compares them to an internationally standardized framework for personal information security with guidelines on MyData so that a proper PDS model can be proposed for South Korea.

• 로봇학회논문지
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• 제18권1호
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• pp.88-92
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• 2023

Lithium-ion batteries are actively used in various industrial sites such as field robots, drones, and electric vehicles due to their high energy efficiency, light weight, long life span, and low self-discharge rate. When using a lithium-ion battery in a field, it is important to accurately estimate the SoC (State of Charge) of batteries to prevent damage. In recent years, SoC estimation using data-based artificial neural networks has been in the spotlight, but it has been difficult to deploy in the embedded board environment at the actual site because the computation is heavy and complex. To solve this problem, neural network lightening technologies such as network pruning have recently attracted attention. When pruning a neural network, the performance varies depending on which layer and how much pruning is performed. In this paper, we introduce an optimized pruning technique by improving the existing pruning method, and perform a comparative experiment to analyze the results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1213-1237
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• 2019

Vehicular Ad-hoc Network (VANET) facilities envision future Intelligent Transporting Systems (ITSs) by providing inter-vehicle communication for metrics such as road surveillance, traffic information, and road condition. In recent years, vehicle manufacturers, researchers and academicians have devoted significant attention to vehicular communication technology because of its highly dynamic connectivity and self-organized, decentralized networking characteristics. However, due to VANET's high mobility, dynamic network topology and low communication coverage, dissemination of large data packets (e.g. multimedia content) is challenging. Clustering enhances network performance by maintaining communication link stability, sharing network resources and efficiently using bandwidth among nodes. This paper proposes a mobility-based, multi-hop clustering algorithm, (MBCA) for multimedia content broadcasting over an IEEE 802.11p-LTE-enabled hybrid VANET architecture. The OMNeT++ network simulator and a SUMO traffic generator are used to simulate a network scenario. The simulation results indicate that the proposed clustering algorithm over a hybrid VANET architecture improves the overall network stability and performance, resulting in an overall 20% increased cluster head duration, 20% increased cluster member duration, lower cluster overhead, 15% improved data packet delivery ratio and lower network delay from the referenced schemes [46], [47] and [50] during multimedia content dissemination over VANET.

• Smart Structures and Systems
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• 제30권6호
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.

• International Journal of Contents
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• 제17권4호
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• Asian Pacific Journal of Cancer Prevention
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• 제14권11호
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• pp.6631-6636
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• 2013

Through years of effort, researchers have made notable progress in gene and microRNA fields about retinoblastoma morbidity. However, experimentally validated data for genes, microRNAs (miRNAs) and transcription factors (TFs) can only be found in a scattered form, which makes it difficult to conclude the relationship between genes and retinoblastoma systematically. In this study, we regarded genes, miRNAs and TFs as elements in the regulatory network and focused on the relationship between pairs of examples. In this way, we paid attention to all the elements macroscopically, instead of only researching one or several. To show regulatory relationships over genes, miRNAs and TFs clearly, we constructed 3 regulatory networks hierarchically, including a differentially expressed network, a related network and a global network, for analysis of similarities and comparison of differences. After construction of the three networks, important pathways were highlighted. We constructed an upstream and downstream element table of differentially expressed genes and miRNAs, in which we found self-adaption relations and circle-regulation. Our study systematically assessed factors in the pathogenesis of retinoblastoma and provided theoretical foundations for gene therapy researchers. In future studies, especial attention should be paid to the highlighted genes and miRNAs.

• 디지털융복합연구
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• 제8권1호
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• pp.71-79
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• 2010

최근 사이버 커뮤니티의 온라인 사회네트워크 관한 관심이 증대되고 있다. 사회네트워크의 최대의 과제는 인간 자본이 형성으로 이어질 수 있는가 이다. 사람들은 다른 사람들과 가상 커뮤니티 안에서 계속적인 접촉을 바탕으로 서로 관계를 형성하고 이것이 다시 사회네트워크로 이어져 인적네트워크를 형성한다. 사회 네트워크는 자신이 동원 할 수 있는 인적 네트워크의 총합이라 말할 수 있다. 본 연구의 목적은 사이버 커뮤니티 안에서의 사회 네트워크의 형성에 영향을 주는 변수를 찾기 위함이다. 우리는 연구를 위해 이용동기, 자기개방성, 상호작용성을 독립변수로 선정하고 종속변수로 지식공유, 관계강화 선정하여 다중회귀 분석을 실시하였다. 데이터는 대학생들을 대상으로 설문지를 이용하여 수집하였고, 표본으로 256명을 사용하였다. 그 결과 독립변수들이 지식공유에 정의 영향을 미치는 것을 확인 하였다. 하지만 관계 강화에는 정의 영향을 미치지 못하는 것으로 분석 되었다. 지식공유가 가지는 시사점으로는 현대의 전문화된 사회에서 자신의 분야가 아닌 타 영역에 관한 보다 쉽고 안정적으로 지식획득을 할 수 있고 타 영역에 접근할 수 있는 점이라 하겠다.