• 한국농공학회논문집
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• 제51권3호
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• pp.9-14
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• 2009

Flow and pollution load were monitored at 2 combined sewer outlets (C-1 and C-2) of urban watersheds during dry weather from September, 2004 to April, 2006 for 20 months. The objectives were to investigate the diurnal variation of flow and pollutant load and to find the proper sampling time that could measure representative flow and pollutant load. Pollution load closed to the average daily load at C-1 could be measured at 00:00 hour and by the mean of 15:00 and 18:00 hour measures, and 15:00 and 21:00 hour measures, respectively. In addition at C-2, it was 21:00 hour and the mean of 15:00 and 18:00 hour measures. This study concluded that arbitrary sampling of flow and water quality could cause large errors in the estimation of urban pollution load and recommended that urban combined sewers should be monitored when flow and water quality showed daily average and concentration.

• 한국터널지하공간학회 논문집
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• 제16권1호
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• pp.1-11
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• 2014

본 논문은 단선터널 라이닝의 최적 계측 측점수를 산정하기 위해 단순보 형태로 모델링하여 터널 라이닝 역해석 프로그램에 적용한 결과와 상용 프로그램에 적용한 결과를 상호 비교하여 평가하였다. 단선터널을 대상으로 터널 라이닝에 대칭 분포하중이 작용하는 경우와 비대칭 분포하중이 작용하는 경우로 가정하여 터널해석 시 널리 사용되는 상용 프로그램에 하중조건을 입력시켜 터널 라이닝의 단면 위치별 변위와 응력을 산출하였다. 산출된 변위를 계측 측점수 3점, 5점, 7점으로 터널 라이닝 역해석 프로그램에 입력시켜서 구한 응력과 변위를 비교하여 최적 계측 측점수를 산정하였다. 연구결과 실무적으로 계측 수행의 경제성과 터널 계측의 손망실률을 고려한 계측 측점수가 최소 5점은 되어야 할 것으로 판단된다.

• Smart Structures and Systems
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• 제28권6호
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Structural Engineering and Mechanics
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• 제50권1호
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• pp.105-119
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• 2014

This study presents a steel container crane movement analysis and assessment based on structural health monitoring (SHM). The accelerometers are used to monitor the dynamic crane behavior and a 3-D finite element model (FEM) was designed to express the static displacement of the crane under the different load cases. The multi-input single-output nonlinear autoregressive neural network with external input (NNARX) model is used to identify the crane dynamic displacements. The FEM analysis and the identification model are used to investigate the safety and the vibration state of the crane in both time and frequency domains. Moreover, the SHM system is used based on the FEM analysis to assess the crane behavior. The analysis results indicate that: (1) the mean relative dynamic displacement can reveal the relative static movement of structures under environmental load; (2) the environmental load conditions clearly affect the crane deformations in different load cases; (3) the crane deformations are shown within the safe limits under different loads.

• 대한기계학회논문집A
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• 제30권5호
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• pp.560-567
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• 2006

The exact load measurements for the mechanical parts of a wind turbine are important step both fur the evaluation of a specific wind turbine design and for a certification process. A common method for a mechanical load measurement is using a strain gauge sensing. Two main problems ought to be answered in order for this method to be applied to the wind turbine project. These are strain gauge calibration and non-contact signal transmission from the strain gauge output to a load monitoring system. This paper suggests reliable solutions fer these two problems. A Bluetooth, a short range wireless data communication technology, is used to solve the second problem. The first one, the strain gauge calibration methodology for a load measurement in a wind turbine application, is fully explained in this paper. Various mechanical loadings for a strain gauge calibration in a wind turbine load measurement are introduced and analyzed. Initial experimental results which are obtained from a 1 kW small size wind turbine are analyzed, and the uncertainty problem in estimating mechanical loads using a calibration matrix is fully covered in this paper.

• Steel and Composite Structures
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• 제46권6호
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• 전기학회논문지
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• 제59권9호
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• pp.1530-1539
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• 2010

Recent technology advancement related to computer & communication and measuring devices allows system operators to adopt more intelligent monitoring and control systems to their power systems in order to prevent massive system blackout. Among them, wide-area monitoring and control(WAMAC) system based on synchro-phasor technology has been widely applied to power systems for their own purposes. In this paper, the study on the development of load shedding scheme to improve voltage stability in KEPCO system is introduced. Summary of WAMAC technology being developed and applied in the world through extensive literature survey is proposed. And methodology to develop voltage stability index and multi-step load shedding scheme based on synchro-phasor data is also presented.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.1221-1226
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• 2015

This paper proposes a multiple switch mode power supply (SMPS) system based on the wireless network which controls variable load. The system enables power supply of up to 600W using 200W SMPS as a unit module and provides a controlling function of output power based on variable load and a monitoring function based on wireless network. The controlling function for output power measures the variation of output power and facilitates efficient power supply by controlling output power based on the measured variation value. The monitoring function guarantees a stable power supply by observing the multiple SMPS system in real time via wireless network. The performance of the proposed system was examined by various experiments. In addition, it was verified through standardized test of Korea Testing Certification. The results were given and discussed.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제16권4호
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• pp.447-451
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• 2010

유비쿼터스 지능공간 모니터링 시스템은 유비쿼터스 지능공간 내에서의 다양한 스마트 객체의 기능, 성능 및 상태를 모니터링하고 분석함으로써 사용자 지수, 서비스의 통합 동작 상황, 서비스의 상태를 판단할 수 있는 정보를 실시간으로 제공해준다. 또한 최적화 및 자기 관리를 통하여 서비스 성능을 향상시킬 수 있는 기능을 제공한다. 이러한 실시간 모니터링 시스템의 적용범위를 확장하기 위해서는 유동적인 대량의 데이터 처리가 필요하다. 본 논문에서는 모니터링 시스템을 구성하는 스마트 객체의 데이터 발생에 따라 유발되는 에이전트들의 부하를 해결하기 위해 부하를 예측하여 미리 분산시키는 기법을 제안한다. 제안된 기법이 적용된 시스템은 실험을 통해 기존의 시스템에 비해 전체 데이터 마감시간 초과율이 매개변수가 1일 경우, 80%이상 감소됨을 보여준다.

• 지질공학
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• 제14권4호
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• pp.429-442
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• 2004

1999년 부산광역시에서 발생한 황령산 산사태는 평면파괴 형태였다 그 후, 황령산 절개사면은 재시공 되었으며 최종 구배는 1:2.0이었다. 수평변위, 간극수압 및 축력을 측정하기위하여 공사중인 2000년 8월부터 2002년 2월까지 파괴사면에 대한 계측이 실시되었다. 계측결과를 종합 고려하면, 강우량에 비례해서 간극수압이 증가되지만 강우 후 지반의 압력수두가 신속히 감소하여, 지표에서 유입된 강우가 사면을 따라 신속히 배출되는 것으로 사료된다. 또한, 경사계 및 하중계 계측자료의 분석결과, 지반의 거동은 거의 없으므로, 사면도 안정된 것으로 사료된다. 특히 본 연구지역과 같이 강우와 관련된 산사태 지역의 경우 자동화 계측을 통해서 간극수압의 신속한 증감을 알 수 있었으며, 추후 이러한 주요 시설 사면에서 자동화 방식에 의한 계측관리가 사면의 안정성을 판단하는데 더욱 효과적일 것으로 사료된다.