• 디지털융복합연구
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• 제18권8호
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• pp.37-48
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• 2020

본 연구는 텍스트 마이닝을 활용하여 리빙랩 연구의 동향을 파악하고 연구 방향 정립에 필요한 함의를 도출하고자 하였다. 리빙랩 관련 연구가 발표되기 시작한 2011년부터 2019년 11월까지의 논문 166편의 키워드와 초록을 대상으로 네트워크 분석 및 토픽 모델링 기법을 사용하여 분석하였다. 키워드 중 혁신, 지역, 사회, 기술, 스마트시티 등의 출현빈도가 높았고, 중심도 분석결과 현재까지 리빙랩 연구가 혁신, 사회, 기술, 개발, 사용자 등의 키워드를 중심으로 이루어짐을 파악하였다. 토픽 모델링 결과 지역혁신과 사용자지원, 정부 사회정책사업, 스마트시티 플랫폼구축, 기업기술혁신모델 및 시스템전환 참여 등 5개 토픽을 추출하였으며 토픽을 이어주는 키워드는 혁신, 기술, 사용자, 참여인것으로 분석하였다. 2017년 KNoLL 출범 후 토픽별 비중은 고른 분포로 연구 주제가 다양화됨을 확인하였다. 텍스트마이닝을 이용한 리빙랩 연구동향 분석과 방향 제시는 연구와 정책방향 수립에 유용한 자료를 제공할 수 있다.

• Smart Structures and Systems
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• 제24권6호
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.641-659
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• 2010

Structural Health Monitoring (SHM) gradually becomes a technique for ensuring the health and safety of civil infrastructures and is also an important approach for the research of the damage accumulation and disaster evolving characteristics of civil infrastructures. It is attracting prodigious research interests and the active development interests of scientists and engineers because a great number of civil infrastructures are planned and built every year in mainland China. In a SHM system the sheer number of accompanying wires, fiber optic cables, and other physical transmission medium is usually prohibitive, particularly for such structures as offshore platforms and long-span structures. Fortunately, with recent advances in technologies in sensing, wireless communication, and micro electro mechanical systems (MEMS), wireless sensor technique has been developing rapidly and is being used gradually in the SHM of civil engineering structures. In this paper, some recent advances in the research, development, and implementation of wireless sensors for the SHM of civil infrastructures in mainland China, especially in Dalian University of Technology (DUT) and Harbin Institute of Technology (HIT), are introduced. Firstly, a kind of wireless digital acceleration sensors for structural global monitoring is designed and validated in an offshore structure model. Secondly, wireless inclination sensor systems based on Frequency-hopping techniques are developed and applied successfully to swing monitoring of large-scale hook structures. Thirdly, wireless acquisition systems integrating with different sensing materials, such as Polyvinylidene Fluoride(PVDF), strain gauge, piezoresistive stress/strain sensors fabricated by using the nickel powder-filled cement-based composite, are proposed for structural local monitoring, and validating the characteristics of the above materials. Finally, solutions to the key problem of finite energy for wireless sensors networks are discussed, with future works also being introduced, for example, the wireless sensor networks powered by corrosion signal for corrosion monitoring and rapid diagnosis for large structures.

• Smart Structures and Systems
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• 제24권1호
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• pp.127-139
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• 2019

Residual drifts after an earthquake can incur huge repair costs and might need to replace the infrastructure because of its non-reparability. Proper functioning of bridges is also essential in the aftermath of an earthquake. In order to mitigate pounding and unseating damage of bridges subjected to earthquakes, a self-adaptive Ni-Ti shape memory alloy (SMA)-cable-based frictional sliding bearing (SMAFSB) is proposed considering self-adaptive centering, high energy dissipation, better fatigue, and corrosion resistance from SMA-cable component. The developed novel bearing is associated with the properties of modularity, replaceability, and earthquake isolation capacity, which could reduce the repair time and increase the resilience of highway bridges. To evaluate the super-elasticity of the SMA-cable, pseudo-static tests and numerical simulation on the SMA-cable specimens with a diameter of 7 mm are conducted and one dimensional (1D) constitutive hysteretic model of the SMAFSB is developed considering the effects of gap, self-centering, and high energy dissipation. Two types of the SMAFSB (i.e., movable and fixed SMAFSBs) are applied to a two-span continuous reinforced concrete (RC) bridge. The seismic vulnerabilities of the RC bridge, utilizing movable SMAFSB with the constant gap size of 60 mm and the fixed SMAFSBs with different gap sizes (e.g., 0, 30, and 60 mm), are assessed at component and system levels, respectively. It can be observed that the fixed SMAFSB with a gap of 30 mm gained the most retrofitting effect among the three cases.

• 정보보호학회논문지
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• 제21권4호
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• pp.115-124
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• 2011

국제적으로 통용되는 전자여권은 스마트카드, 공개키 기반 암호화, 바이오인식 등 최신의 보안기술이 융합되어 전자여권의 위조 복제 방지를 위해 사용되고 있다. 특히 전자여권의 사용되는 바이오정보는 개인의 가장 민감한 정보로써 위조 복제 되었을 때 가장 큰 피해가 예상되며 바이오정보의 복제 여부를 검증하는 보안기술이 필요하다. 본 논문에서는 전자여권내의 바이오정보 복제를 방지하기 위해 바이오정보 워터마킹을 이용한 전자여권 보안기술을 제안한다. 제안하는 바이오정보 워터마킹은 전자여권의 발급과 전자여권의 판독하는 과정에서 워터마크를 이용하여 바이오정보의 복제 여부를 검출한다. 본 논문은 국내 전자여권의 환경에서 발급 및 판독을 통해 실험결과를 제시하였으며 전자여권의 선택적인 보안기능으로 활용이 가능하다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제11권2호
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• pp.51-58
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• 2022

블록체인 기술은 블록체인 네트워크에 참여하는 사용자의 데이터가 분산 처리되어 저장되는 시스템이다. 비트코인과 이더리움을 필두로 세계적으로 관심을 받고 있으며, 블록체인의 활용성은 무궁무진한 것으로 예측되고 있다. 하지만 블록체인의 모든 데이터를 네트워크 참여자에게 공개하는 투명성으로 인해 블록체인 데이터 프라이버시 보호에 대한 필요성이 개인정보를 처리하는 각종 금융, 의료, 부동산 분야에서 떠오르고 있다. 기존 블록체인 데이터 프라이버시 보호를 위해서 스마트 컨트랙트, 동형암호화, 암호학 키 방식을 사용하는 연구들이 주를 이루었으나, 본 논문에서는 기존의 논문들과 차별화된 매트릭스 문자 재배치 기법을 사용한 데이터 프라이버시 보호를 제안한다. 본 논문에서 제안하는 접근방안은 원본 데이터를 매트릭스 문자 재배치 하는 방법, 배치된 데이터를 다시 원본으로 되돌리는 방법, 크게 두 가지로 구성이 되어있다. 정성적인 실험을 통해 본 논문에서 제안하는 접근방안의 안전성을 평가하였으며, 매트릭스 문자 재배치가 적용된 데이터를 원본 데이터로 되돌릴 때 걸리는 시간을 측정하여 프라이빗 블록체인 환경에서도 충분히 적용이 가능할 것이라는 것을 증명하였다.

• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.173-182
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• 2023

The UAE government has set its sights on creating a smart, electronic-based government system that utilizes AI. The country's collaboration with India aims to bring substantial returns through AI innovation, with a target of over $20 billion in the coming years. To achieve this goal, the UAE launched its AI strategy in 2017, focused on improving performance in key sectors and becoming a leader in AI investment. To ensure public safety as the role of AI in government grows, the country is working on developing integrated cyber security solutions for SCADA systems. A questionnaire-based study was conducted, using the AI IQ Threat Scale to measure the variables in the research model. The sample consisted of 200 individuals from the UAE government, private sector, and academia, and data was collected through online surveys and analyzed using descriptive statistics and structural equation modeling. The results indicate that the AI IQ Threat Scale was effective in measuring the four main attacks and defense applications of AI. Additionally, the study reveals that AI governance and cyber defense have a positive impact on the resilience of AI systems. This study makes a valuable contribution to the UAE government's efforts to remain at the forefront of AI and technology exploitation. The results emphasize the need for appropriate evaluation models to ensure a resilient economy and improved public safety in the face of automation. The findings can inform future AI governance and cyber defense strategies for the UAE and other countries.

• Structural Engineering and Mechanics
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• 제86권3호
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• pp.291-309
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• 2023

This paper addresses the finite element modeling of functionally graded porous (FGP) beams for free vibration and buckling behaviour cases. The formulated finite element is based on simple and efficient higher order shear deformation theory. The key feature of this formulation is that it deals with Euler-Bernoulli beam theory with only three unknowns without requiring any shear correction factor. In fact, the presented two-noded beam element has three degrees of freedom per node, and the discrete model guarantees the interelement continuity by using both C⁰ and C¹ continuities for the displacement field and its first derivative shape functions, respectively. The weak form of the governing equations is obtained from the Hamilton principle of FGP beams to generate the elementary stiffness, geometric, and mass matrices. By deploying the isoparametric coordinate system, the derived elementary matrices are computed using the Gauss quadrature rule. To overcome the shear-locking phenomenon, the reduced integration technique is used for the shear strain energy. Furthermore, the effect of porosity distribution patterns on the free vibration and buckling behaviours of porous functionally graded beams in various parameters is investigated. The obtained results extend and improve those predicted previously by alternative existing theories, in which significant parameters such as material distribution, geometrical configuration, boundary conditions, and porosity distributions are considered and discussed in detailed numerical comparisons. Determining the impacts of these parameters on natural frequencies and critical buckling loads play an essential role in the manufacturing process of such materials and their related mechanical modeling in aerospace, nuclear, civil, and other structures.

• Spatial Information Research
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• 제20권5호
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• pp.99-109
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• 2012

최근 소셜 미디어의 발달과 스마트폰의 확산으로 SNS(Social Network Service)가 활성화가 되면서 데이터양이 폭발적으로 증가하였다. 이에 맞춰 빅데이터 개념이 새롭게 대두되었으며, 빅데이터를 활용하기 위한 많은 방안이 연구되고 있다. 여러 기업이 보유한 빅데이터의 가치창출을 극대화하기 위해 기존 데이터와의 융합이 필요하며, 물리적, 논리적 저장구조가 다른 이기종 데이터 소스를 통합하고 관리하기 위한 시스템이 필요하다. 빅데이터를 처리하기 위한 시스템인 맵리듀스는 분산처리를 활용하여 빠른게 데이터를 처리한다는 이점이 있으나 모든 키워드에 대해 시스템을 구축하여 저장 및 검색 등의 과정을 거치므로 실시간 처리에 어려움이 따른다. 또한, 이기종 데이터를 처리하는 구조가 없어 복합 이벤트를 처리하는데 추가 비용이 발생할 수 있다. 이를 해결하는 방안으로 기존에 연구된 복합 이벤트 처리 시스템을 활용하여 실시간 복합 이벤트 탐지를 위한 기법을 제안하고자 한다. 복합 이벤트 처리 시스템은 서로 다른 이기종 데이터 소스로부터 각각의 데이터들을 통합하고 이벤트들의 조합이 가능하며 스트림 데이터를 즉시 처리할 수 있어 실시간 처리에 유용하다. 그러나 SNS, 인터넷 기사 등 텍스트 기반의 비정형 데이터를 텍스트형으로 관리하고 있어 빅데이터에 대한 질의가 요청될 때마다 문자열 비교를 해야 하므로 성능저하가 발생할 여지가 있다. 따라서 복합 이벤트 처리 시스템에서 비정형 데이터를 관리하고 질의처리가 가능하도록 문자열의 논리적 스키마를 부여하고 데이터 통합 기능을 제안한다. 그리고 키워드 셋을 이용한 필터링 기능으로 문자열의 키워드를 정수형으로 변환함으로써 반복적인 비교 연산을 줄인다. 또한, 복합 이벤트 처리 시스템을 활용하면 인 메모리(In-memory)에서 실시간 스트림 데이터를 처리함으로써 디스크에 저장하고 불러들이는 시간을 줄여 성능 향상을 가져온다.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.