• 디지털융복합연구
• /
• 제19권9호
• /
• pp.75-81
• /
• 2021

본 연구의 목적은 기업에서 클라우드 컴퓨팅 서비스를 채택하기 전에 판단 해야하는 중요한 요인들에 대한 우선 순위를 도출하고 평가한다. 연구 방법으로 전문가들에 대한 의사결정 사항을 반영하기 위해서 AHP 분석기법을 활용하였다. AHP는 복잡한 의사결정 문제를 계층적으로 표현하고 그 계층의 항목 간의 쌍대비교(Pairwise comparison)를 통하여 최선의 대안을 도출해 내는 의사결정 기법이다. 기존의 통계적 의사결정 기법들과 비교해 의사결정과정이 체계적이며 간단하여 이해하기가 쉽다. 또한 분석과정에서 의사결정자의 일관성을 판단할 수 있는 지표를 제공하여 절차 또한 합리적이다. 본 연구의 분석 결과 중요도 우선순위 항목으로 보안성, 신뢰성, 경제성 순으로 나타났다. 보안성의 하위 항목요인 중 제1순위는 접근권한의 통제성, 2순위는 외부위협의 안전성으로 도출되었다. 연구 결과가 추후 실무에서 기준으로 활용될 수 있는 방안으로 활용되는데 이바지할 수 있으며, 향후 클라우드 컴퓨팅 서비스 채택을 한 기업의 만족도를 평가하여 비교·분석하는 연구를 진행할 필요가 있다.

• 한국농공학회논문집
• /
• 제63권5호
• /
• pp.1-11
• /
• 2021

Soil moisture plays a critical role in hydrological processes, land-atmosphere interactions and climate variability. It can limit vegetation growth as well as infiltration of rainfall and therefore very important for agriculture sector and food protection. Recently, due to the increased damage from drought caused by climate change, there is a frequent occurrence of shortage of agricultural water, making it difficult to supply and manage stable agricultural water. Efficient water management is necessary to reduce drought damage, and soil moisture management is important in case of upland crops. In this study, soil moisture was calculated based on the water balance model, and the suitability of soil moisture data was verified through the application. The regional soil moisture was calculated based on the meteorological data collected by the meteorological station, and applied the Runs theory. We analyzed the spatiotemporal variability of soil moisture and drought impacts, and analyzed the correlation between actual drought impacts and drought damage through correlation analysis of Standardized Precipitation Index (SPI). The soil moisture steadily decreased and increased until the rainy season, while the drought size steadily increased and decreased until the rainy season. The regional magnitude of the drought was large in Gyeonggi-do and Gyeongsang-do, and in winter, severe drought occurred in areas of Gangwon-do. As a result of comparative analysis with actual drought events, it was confirmed that there is a high correlation with SPI by each time scale drought events with a correlation coefficient.

• Smart Structures and Systems
• /
• 제23권6호
• /
• pp.695-702
• /
• 2019

The number of cable-stayed bridges has been increasing worldwide, causing issues in maintaining the structural safety and integrity of bridges. The stay cable, one of the most critical members in cable-stayed bridges, is vulnerable to wind-induced vibrations owing to its inherent low damping capacity. Thus, vibration mitigation of stay cables has been an important issue both in academia and practice. While a semi-active control scheme shows effective vibration reduction compared to a passive control scheme, real-world applications are quite limited because it requires complicated equipment, including for data acquisition, and power supply. This study aims to develop an Arduino-based integrated cable vibration control system implementing a semi-active control algorithm. The integrated control system is built on the low-cost, low-power Arduino platform, embedding a semi-active control algorithm. A MEMS accelerometer is installed in the platform to conduct a state feedback for the semi-active control. The Linear Quadratic Gaussian control is applied to estimate a cable state and obtain a control gain, and the clipped optimal algorithm is implemented to control the damping device. This study selects the magnetorheological damper as a semi-active damping device, controlled by the proposed control system. The developed integrated system is applied to a laboratory size cable with a series of experimental studies for identifying the effect of the system on cable vibration reduction. The semi-active control embedded in the integrated system is compared with free and passive mode cases and is shown to reduce the vibration of stay-cables effectively.

• Nuclear Engineering and Technology
• /
• 제53권2호
• /
• pp.657-665
• /
• 2021

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

• 한국간호교육학회지
• /
• 제27권1호
• /
• pp.16-28
• /
• 2021

Purpose: The purpose of this study was to develop nurses' core competencies and sub-competencies and to verify the validity and importance-performance of core competencies. Methods: The core competencies of nurses were derived through an analysis of strengths, weaknesses, opportunities, and threats, as well as a literature analysis of domestic and foreign accreditation institutions. Validity and importance-performance analyses were conducted on the core competencies derived from nursing colleges nationwide. Results: Six core competencies of nurses were revealed: integration of knowledge and nursing skills, critical thinking, communication, leadership, safety management, and global competency. Further, eighteen sub-competencies were derived. The content validity ratio values for the core competencies were higher than 0.74. Communication skills among multidisciplinary teams and communication skills among nursing teams were shown to be the most important competencies to be improved. Conclusion: The results of this study are meaningful in terms of how the core competencies of nurses were derived and evaluated for the fourth cycle of nursing education accreditation according to the changes of time and culture.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제10권4호
• /
• pp.115-124
• /
• 2021

기능 블록 다이어그램(Function Block Diagram, FBD) 프로그램이 안전 필수 시스템 구현에 널리 사용되면서 FBD 프로그램에 대한 효과적인 테스트가 중요해졌다. 뮤테이션 테스팅은 오류 기반 테스팅 기술로, 오류 탐지에 매우 효과적이지만 비용이 많이 든다. 본 연구에서는 FBD 프로그램 테스터를 지원하기 위한, FBD 프로그램 대상 자동 뮤턴트 생성기를 제안한다. MuGenFBD 도구는 뮤턴트 생성 비용과 동등 뮤턴트 문제를 고려하여 설계되었다. MuGenFBD 도구의 성능을 평가하기 위해 실제 산업 사례에 대한 실험을 수행한 결과, MuGenFBD를 활용하여 뮤턴트 생성 시 동등 뮤턴트를 생성할 비율이 낮으며 적은 비용으로 FBD 프로그램 대상 뮤턴트를 효과적으로 자동 생성할 수 있음을 확인하였다. 제안하는 도구는 FBD 프로그램에 대한 뮤테이션 분석 및 뮤테이션 충분성 기준을 만족시키는 테스트 생성을 효과적으로 지원할 수 있다.

• Journal of Dental Anesthesia and Pain Medicine
• /
• 제22권2호
• /
• pp.129-143
• /
• 2022

Background: Patient-controlled analgesia (PCA) has been widely used as an effective medical treatment for pain and for postoperative analgesia. However, improper dose errors in intravenous (IV) administration of narcotic analgesics from a PCA infusion pump can cause patient harm. Furthermore, opioid overdose is considered one of the highest risk factors for patients receiving pain medications. Therefore, accurate delivery of opioid analgesics is a critical function of PCA infusion pumps. Methods: We designed a microbalance method that consisted of a closed acrylic chamber containing a layer and an oil layer with an electronic balance. A commercially available infusion analyzer (IDA-5, Fluke Co., Everett, WA, USA) was used to measure the accuracy of the infusion flow rate from a commercially available smart PCA infusion pump (PS-1000, UNIMEDICS, Co., Ltd., Seoul, Korea) and compared with the results of the microbalance method. We evaluated the uncertainty of the flow rate measurement using the ISO guide (GUM:1995 part3). The battery life, delay time of the occlusion alarm, and bolus function of the PCA pump were also tested. Results: The microbalance method was good in the short-term 2 h measurement, and IDA-5 was good in the long-term 24 h measurement. The two measurement systems can complement each other in the case of the measurement time. Regarding battery performance, PS-1000 lasted approximately 5 days in a 1 ml/hr flow rate condition without recharging the battery. The occlusion pressure alarm delays of PS-1000 satisfied the conventional alarm threshold of occlusion pressure (300-800 mmHg). Average accuracy bolus volume was measured as 63%, 95%, and 98.5% with 0.1 ml, 1 ml, and 2 ml bolus volume presets, respectively. A 1 ml/hr flow rate measurement was evaluated as 2.08% of expanded uncertainty, with a 95% confidence level. Conclusion: PS-1000 showed a flow accuracy to be within the infusion pump standard, which is ± 5% of flow accuracy. Occlusion alarm of PS-1000 was quickly transmitted, resulting in better safety for patients receiving IV infusion of opioids. PS-1000 is sufficient for a portable smart PCA infusion pump.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.251-266
• /
• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.237-250
• /
• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• 한국지진공학회논문집
• /
• 제27권5호
• /
• pp.203-211
• /
• 2023

For fast-built and safe precast concrete (PC) construction, the dry mechanical splicing method is a critical technique that enables a self-sustaining system (SSS) during construction with no temporary support and minimizes onsite jobs. However, due to limited experimental evidence, traditional wet splicing methods are still dominantly adopted in the domestic precast industry. For PC beam-column connections, the current design code requires achieving emulative connection performances and corresponding structural integrity to be comparable with typical reinforced concrete (RC) systems with monolithic connections. To this end, this study conducted the standard material tests on mechanical splices to check their satisfactory performance as the Type 2 mechanical splice specified in the ACI 318 code. Two PC beam-column connection specimens with dry mechanical splices and an RC control specimen as the special moment frame were subsequently fabricated and tested under lateral reversed cyclic loadings. Test results showed that the seismic performances of all the PC specimens were fully comparable to the RC specimen in terms of strength, stiffness, energy dissipation, drift capacity, and failure mode, and their hysteresis responses showed a mitigated pinching effect compared to the control RC specimen. The seismic performances of the PC and RC specimens were evaluated quantitatively based on the ACI 374 report, and it appeared that all the test specimens fully satisfied the seismic performance criteria as a code-compliant special moment frame system.