• 한국정보과학회논문지:시스템및이론
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• 제32권7호
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• pp.333-340
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• 2005

네트워크 기반 컴퓨터 시스템이 외부 침입이나 혹은 내부 침입에 의해 부분적으로 손상 (Partially Compromised)이 되더라도 최소한의 필수 서비스를 지속적으로 제공할 수 있게 해주는 침입감내시스템(Intrusion Tolerance System) 설계에 요구되는 중요한 요소 기술 중의 하나는 컴퓨터 시스템의 정량적 신인도(Dependability) 분석이라 할 수 있다. 본 논문에서는 침입감내시스템의 방어능력을 확보하기 위해 자율컴퓨팅(Autonomic Computing)의 핵심 기술인 자가 치유(Self-healing) 메커니즘을 적용하였다. 주 서버와 보조서버로 구성된 침입감내시스템의 상태천이(State Transition)를 자가치유 메커니즘의 두 가지 요소(결함모델 및 시스템반응)를 활용하여 분석하였으며, 시뮬레이션 실험을 통해 침입감내시스템의 가용도(Availability)를 계산한 후, 두 가지 경우의 취약성(Vulnerability) 공격에 대한 사례 연구를 진행하였다.

• Journal of Mechanical Science and Technology
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• 제20권3호
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• pp.310-318
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• 2006

In this study, we investigate the feasibility of in-situ crack propagation by using a double cleavage drilled compression (DCDC) specimen showing a slow crack velocity down to 0.03 mm/s under 0.01 mm/s of displacement control. Finite element analysis predicted that the DCDC specimens would show at least 4.3 fold delayed crack initiation time than conventional tensile fracture specimens under a constant loading speed. Using DCDC specimens, we were able to observe the in-situ crack propagation process in a particle reinforced transparent polymer composite. Our results confirmed that the DCDC specimen would be a good candidate for the in-situ observation of the behavior of particle reinforced composites with slow crack velocity, such as the self-healing process of micro-particle reinforced composites.

• 한국건설순환자원학회논문집
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• 제10권4호
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• pp.561-568
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• 2022

본 연구에서는 무기계 재료인 클링커 바인더, Calcium sulfoaluminate(CSA), 그리고 sodium sulfate(Na₂SO₄)가 포함된 시멘트 모르타르의 물리적 특성 및 자기치유성능을 평가하였다. 압축강도 및 휨강도 측정을 통해 시멘트 모르타르의 물리적 강도를 알아보고, 정수위 투수시험 및 기체확산시험을 통해 치유성능을 평가하였다. 또한 광학현미경과 주사전자현미경을 통해 균열부 내 침전된 치유생성물을 시각적으로 관찰하였다. 그 결과, 클링커 바인더 기반 무기계 혼합재의 혼입은 시멘트 모르타르의 초기 및 28일 재령 강도를 약 20 % 증진시켰다. 치유성능 평가방법에 따라 치유율의 편차가 존재하고 과소평과 되는 경향을 나타냈으나, 무기계 혼합재의 시멘트 모르타르 혼입 시 0.3 mm 균열 내부에서 CaCO₃가 주요 치유생성물로 침전되어 치유성능을 향상시켰다.

• Smart Structures and Systems
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• 제14권4호
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• pp.517-539
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• 2014

The combined use of smart materials, complementing each others' characteristics and resulting in devices with optimised features, is providing new solutions in many industries. The use of ingenious combinations of smart materials has led to improvements in actuation speed and force, signal-to-noise ratio, sensor precision and unique capabilities such as self-sensing self-healing systems and energy autonomy. This may all give rise to a revival for numerous families of smart materials, for which application proposals had already reached a stationary situation. It may also provide the boost needed for the definitive industrial success of many others. This study focuses on reviewing the proposals, preliminary studies and success cases related to combining smart materials to obtain multifunctional, improved systems. It also examines the most outstanding applications and fields for the combined use of these smart materials. We will also discuss related study areas which warrant further research for the development of novel approaches for demanding applications.

• Smart Structures and Systems
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• 제25권3호
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• pp.337-343
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• 2020

In this paper, we focused on the self-healing concrete using new nano-capsules. Three types of nano-capsules with respect to availability, high strength and temperature tolerance are used; type 1 is URF and polyethylene (PE) as shell and nano titanium oxide (TiO₂) as core, type 2 is URF and PE as shell and nano silica oxide (SiO₂) as core, type 3 is PE as shell and nano silica oxide (SiO₂) as core. The concrete samples mixed by nano-capsules with three percents of 0.5, 1 and 1.5. Based on experimental tests and the compressive strength of samples, the URF-PE-SiO₂ is selected for additional tests of compressive strength before and after recovery, ultrasonic test, ion chlorine and water penetration depths. After careful investigation, it is concluded that the optimum value of URF-PE-SiO₂ nano-capsules is 0.5% since leads to higher compressive strength, ultrasonic test, ion chlorine and water penetration depths.

• Smart Structures and Systems
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• 제24권2호
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• pp.157-171
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• 2019

This study investigated the effects of the geometric parameters of superelastic shape memory alloy (SE SMA) fibers on the pullout displacement recovering and self-healing capacity of reinforced cementitious composites. Three diameters of 0.5, 0.7 and 1.0 mm and two different crimped lengths of 5.0 and 10.0 mm were considered. To provide best anchoring action and high bond between fiber and cement mortar, the fibers were crimped at the end to create spear-head shape. The single fiber cement-based specimens were manufactured with the cement mortar of a compressive strength of 84 MPa with the square shape at the top and a dog-bone shape at the bottom. The embedded length of each fiber was 15 mm. The pullout test was performed with displacement control to obtain monotonic or hysteretic behaviors. The results showed that pullout displacements were recovered after fibers slipped and stuck in the specimen. The specimens with fiber of larger diameter showed better displacement recovering capacity. The flag-shaped behavior was observed for all specimens, and those with fiber of 1.0 mm diameter showed the clearest one. It was observed that the length of fiber anchorage did not have a significant effect on the displacement recovery, pullout resistance and self-healing capacity.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (1)
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• pp.646-648
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• 2004

네트워크 기반 컴퓨터 시스템이 각종 악의적 공격에 의해 손상되더라도, 지속적인 서비스를 제공할 수 있게 해주는 침입 감내시스템(Intrusion Tolerance Systems) 설계기법의 중요한 요소 기술 중의 하나는 컴퓨터 시스템의 정량적 신인도(Dependability) 분석이라 할 수 있다. 본 논문에서는 침입 감내시스템의 신인도를 분석하기 위해 자율컴퓨팅(Autonomous Computing)의 핵심 기술인 자가 치유(Self-healing) 메커니즘을 적용하였다. 즉, 주 서버와 보조서버로 구성된 이중계 침입 감내시스템의 상태천이(State Transition)를 자가치유 메커니즘의 두 가지 요소(결함모델 및 시스템반응)를 활용하여 분석하였으며, 시뮬레이션 실험을 통해 침입 감내시스템의 가용도(Availability)를 정량적으로 정의하였다.

• Smart Structures and Systems
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• 제17권1호
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• pp.135-147
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• 2016

This paper describes a smart structural system, which uses smart materials for real-time monitoring and active control of bolted-joints in steel structures. The goal of this research is to reduce the possibility of failure and the cost of maintenance of steel structures such as bridges, electricity pylons, steel lattice towers and so on. The concept of the smart structural system combines impedance based health monitoring techniques with a shape memory alloy (SMA) washer to restore the tension of the loosened bolt. The impedance-based structural health monitoring (SHM) techniques were used to detect loosened bolts in bolted-joints. By comparing electrical impedance signatures measured from a potentially damage structure with baseline data obtained from the pristine structure, the bolt loosening damage could be detected. An outlier analysis, using generalized extreme value (GEV) distribution, providing optimal decision boundaries, has been carried out for more systematic damage detection. Once the loosening damage was detected in the bolted joint, the external heater, which was bonded to the SMA washer, actuated the washer. Then, the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. Additionally, temperature variation due to the heater was compensated by applying the effective frequency shift (EFS) algorithm to improve the performance of the diagnostic results. An experimental study was conducted by integrating the piezoelectric material based structural health monitoring and the SMA-based active control function on a bolted joint, after which the performance of the smart 'self-monitoring and self-healing bolted joint system' was demonstrated.

• 한국정밀공학회지
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• 제31권4호
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• pp.287-295
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• 2014

Smart manufacturing (SM) considered as a new trend of modern manufacturing helps to meet objectives associated with the productivity, quality, cost and competiveness. It is characterized by decentralized, distributed, networked compositions of autonomous systems. The model of SM is inherited from the organization of the living systems in biology and nature such as ant colony, school of fish, bee's foraging behaviors, and so on. In which, the resources of the manufacturing system are considered as biological organisms, which are autonomous entities so that the manufacturing system has the advanced characteristics inspired from biology such as self-adaptation, self-diagnosis, and self-healing. To prove this concept, a cloud machining system is considered as research object in which internet of things and cloud computing are used to integrate, organize and allocate the machining resources. Artificial life tools are used for cooperation among autonomous elements in the cloud machining system.

• 전자통신동향분석
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• 제35권4호
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• pp.115-134
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• 2020

Several studies on machine learning (ML) based self-organizing networks (SONs) have been conducted, specifically for LTE, since studies to apply ML to optimize mobile communication systems started with 2G. However, they are still in the infancy stage. Owing to the complicated KPIs and stringent user requirements of 5G, it is necessary to design the 5G SON engine with intelligence to enable users to seamlessly and unlimitedly achieve connectivity regardless of the state of the mobile communication network. Therefore, in this study, we analyze and summarize the current state of machine learning studies applied to SONs as solutions to the complicated optimization problems that are caused by the unpredictable context of mobile communication scenarios.