• Advances in concrete construction
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• 제13권5호
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Smart Structures and Systems
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• 제29권3호
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• pp.499-512
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• 2022

This work proposes a novel contactless vibration damping and thermal isolation tripod platform based on Superconducting Magnetic Levitation (SML). This prototype is suitable for cryogenic environments, where classical passive, semi active and active vibration isolation techniques may present tribological problems due to the low temperatures and/or cannot guarantee an enough thermal isolation. The levitating platform consists of a Superconducting Magnetic Levitation (SML) with inherent passive static stabilization. In addition, the use of Operational Modal Analysis (OMA) technique is proposed to characterize the transmissibility function from the baseplate to the platform. The OMA is based on the Stochastic Subspace Identification (SSI) by using the Expectation Maximization (EM) algorithm. This paper contributes to the use of SSI-EM for SML applications by proposing a step-by-step experimental methodology to process the measured data, which are obtained with different unknown excitations: ambient excitation and impulse excitation. Thus, the performance of SSI-EM for SML applications can be improved, providing a good estimation of the natural frequency and damping ratio without any controlled excitation, which is the main obstacle to use an experimental modal analysis in cryogenic environments. The dynamic response of the 510 g levitating platform has been characterized by means of OMA in a cryogenic, 77 K, and high vacuum, 1E-5 mbar, environment. The measured vertical and radial stiffness are 9872.4 N/m and 21329 N/m, respectively, whilst the measured vertical and radial damping values are 0.5278 Nm/s and 0.8938 Nm/s. The first natural frequency in vertical direction has been identified to be 27.39 Hz, whilst a value of 40.26 Hz was identified for the radial direction. The determined damping values for both modes are 0.46% and 0.53%, respectively.

• 한국BIM학회 논문집
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• 제12권4호
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• pp.61-69
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• 2022

Earthmoving operations account for approximately 25% of construction cost, generally executed prior to the construction of buildings and structures with heavy equipment. For the successful completion of earthwork projects, it is crucial to constantly monitor earthwork equipment (e.g., trucks), estimate productivity, and optimize the construction process and equipment on a construction site. Traditional methods however require time-consuming and painstaking tasks for the manual observations of the ongoing field operations. This study proposed the use of a GPS sensor embedded in a smartphone for the tracking and visualization of equipment locations, which are in turn used for the estimation and simulation of cycle times and production rates of ongoing earthwork. This approach is implemented into a digital platform enabling real-time data collection and simulation, particularly in a 2D (e.g., maps) or 3D (e.g., point clouds) virtual environment where the spatial and temporal flows of trucks are visualized. In the case study, the digital platform is applied for an earthmoving operation at the site development work of commercial factories. The results demonstrate that the production rates of various equipment usage scenarios (e.g., the different numbers of trucks) can be estimated through simulation, and then, the optimal number of tucks for the equipment fleet can be determined, thus supporting the practical potential of real-time sensing and simulation for onsite equipment management.

• 한국정보통신학회논문지
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• 제26권2호
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• pp.207-213
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• 2022

최근 IoT 기술과 AI의 성능향상에 따라 폭넓은 분야에서 자동화와 무인화가 진행되고 있으며, 사물인식과 객체분류 등 자동화의 기반이 되는 영상처리에 대한 관심이 높아지고 있다. 영상의 잡음 제거는 영상에 기반한 시스템에서 전처리 단계로 사용하는 중요한 과정으로 다양한 연구가 진행되었으나, 대부분의 경우 에지와 같은 고주파 성분에서 스무딩 효과에 의해 디테일한 정보를 보존하기 어렵다는 단점이 있다. 본 논문은 AWGN(additive white Gaussian noise)에 훼손된 영상을 가우시안 분포에 기반한 퍼지 가중치를 사용하여 복원하는 알고리즘을 제안한다. 제안한 알고리즘은 필터링 마스크와 잡음 추정치를 서로 비교하여 필터링 과정을 스위칭하였으며, 영상의 저주파 및 고주파 성분에 따라 퍼지 가중치를 계산하여 영상을 복원하였다.

• Smart Structures and Systems
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• 제29권3호
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• pp.445-455
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• 2022

Steel anchor bolts are installed in concrete using a variety of methods. One of the most common methods of anchor bolt installation is using epoxy resin as an infill material injected into the drilled hole to act as a bonding material between the steel bolt and the surrounding concrete. Typical design standards assume uniform stress distribution along the length of the anchor bolt accompanied with single crack leading to pull-out failure. Experimental evidence has shown that the steel anchor bolts fail owing to the multiple failure patterns, hence these design assumptions are not realistic. In this regard, the presented research work details the analytical model that takes into consideration multiple micro cracks in the infill material induced via impact loading. The impact loading from the Schmidt hammer is used to evaluate the bond condition bond condition of anchor bolt and the epoxy material. The added advantage of the presented analytical model is that it is able to take into account the various type of end conditions of the anchor bolts such as bent or U-shaped anchors. Through sensitivity analysis the optimum stiffness and shear strength properties of the epoxy infill material is achieved, which have shown to achieve lower displacement coupled with reduced damage to the surrounding concrete. The accuracy of the presented model is confirmed by comparing the simulated deformational responses with the experimental evidence. From the comparison it was found that the model was successful in simulating the experimental results. The proposed model can be adopted by professionals interested in predicting and controlling the deformational response of anchor bolts.

• Smart Structures and Systems
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• 제29권1호
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• pp.251-266
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• 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
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• 제29권1호
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• 한국전자통신학회논문지
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• 제17권4호
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• pp.617-624
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• 2022

V2X를 탑재한 소형 전기운송차는 기존 자동차의 운전시스템에 더 많은 정보와 기능을 제공할 수 있다. V2X 기술의 주요 요소는 V2V(자동차 대 자동차), V2N(자동차 대 네트워크), V2I(자동차 대 인프라) 등이 있다. 본 연구는 외부장비와 연계되는 VI형 E-PTO를 설계하고 구현하는 것으로 E-PTO는 DC/DC 변환기, DC/AC 변환기, 배터리 양방향 충전시스템 등으로 구성된다. 또한 구동을 위한 기기와 제어시스템을 구현하였다. 기동/정지 및 정상상태 운전 통한 VI형 E-PTO 구성부품에 대한 시험결과는 100ms 이내 회복시간과 순간 전압변동율 10%의 허용 가능한 요건을 충족하였다.

• 한국ITS학회 논문지
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• 제21권5호
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• pp.233-252
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• 2022

자율주행자동차 상용화를 위해 자율주행자동차 안전성 제고를 위한 다양한 연구가 수행되고 있으며, 그 중 시나리오 연구가 안전성 평가에 직접적으로 연관되어 필수적으로 고려되고 있다. 그러나 기존 시나리오 제시의 경우 데이터 부재 및 전문가 개입으로 인해 객관성 및 설명력이 보완될 필요가 있다는 의견이 제시되고 있다. 이에 본 연구에서는 실제 사고 데이터 및 설명력 있는 인공지능 방법론인 ViT 모델을 활용하여 확장된 자율주행자동차 안전성 평가 시나리오를 제시한다. 활용 데이터에 최적화시킨 ViT 모델 학습 결과, 94% 정확도가 확인되었으며 Attention Map을 추가적으로 활용하여 설명력 있는 시나리오를 제시하였다. 본 연구를 통해 기존 시나리오 접근법의 한계를 보완하고 인공지능을 활용하여 새로운 안전성 평가 시나리오 수립 프레임워크를 제시할 수 있을 것으로 기대된다.

• 자원환경지질
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• 제56권2호
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• pp.185-199
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• 2023

This study aimed to predict the effects of water ecology on the supply of hydrothermal energy to model a housing complex in Eco Delta Smart Village in Busan. Based on the results, engineering measures were recommended to minimize problems due to possible temperature variations on the supply of hydrothermal energy from the river. The current distribution of fish, benthic macroinvertebrates, and phytoplankton in the Pyeonggang Stream was monitored to determine their effects on water ecology. In the research area, five species and three families of fish were observed. The dominant species was Lepomis macrochirus, and the subdominant species was Carassius auratus. Twenty-five species and 21 families of benthic macroinvertebrates were found. The distribution of aquatic insects was poor in this area. The dominant species were Chironomidae sp., Lymnaea auricularia, Appasus japonicus, and Caridina denticulata denticulata in February, May, July, and October. Dominant phytoplankton were Aulacoseira ambigua and Nitzschia palea in February and May. Microcystis sp. was dominant in July and October. The health of the ecology the Pyeonggang Stream was assessed as D (bad) according to the benthic macroinvertebrate index (BMI). Shifts in the location of the discharge point 150 m downstream from intake points and discharge through embedded rock layer after adding equal amounts of stream water as was taken at the beginning were suggested to minimize water temperature variations due to the application of hydrothermal energy. When the scenario (i.e., quantity of water intake and dilution water, 1,600 m³/d and water temp. difference ±5 ℃) was realized, variations in water temperature were assessed at -0.19 ℃ and 0.59 ℃ during cooling and heating, respectively, at a point 10 m downstream. Water temperatures recorded at -0.20 ℃ and 0.68 ℃ during cooling and heating, respectively, at a point 10 m upstream. All stream water temperatures after the application of hydrothermal energy recovered within 24 hours. Future work on the long-term monitoring of ecosystems is suggested, particularly to analyze the influence of the water environment on hydrothermal energy supply operations.