• Smart Structures and Systems
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• v.33 no.3
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• pp.227-241
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• 2024

Safety and structural integrity of civil structures, like bridges and buildings, can be substantially enhanced by employing appropriate structural health monitoring (SHM) techniques for timely diagnosis of incipient damages. The information gathered from health monitoring of important infrastructure helps in making informed decisions on their maintenance. This ensures smooth, uninterrupted operation of the civil infrastructure and also cuts down the overall maintenance cost. With an early warning system, SHM can protect human life during major structural failures. A real-time online damage localization technique is proposed using only the vibration measurements in this paper. The concept of the 'Degree of Scatter' (DoS) of the vibration measurements is used to generate a spatial profile, and fractal dimension theory is used for damage detection and localization in the proposed two-phase algorithm. Further, it ensures robustness against environmental and operational variability (EoV). The proposed method works only with output-only responses and does not require correlated finite element models. Investigations are carried out to test the presented algorithm, using the synthetic data generated from a simply supported beam, a 25-storey shear building model, and also experimental data obtained from the lab-level experiments on a steel I-beam and a ten-storey framed structure. The investigations suggest that the proposed damage localization algorithm is capable of isolating the influence of the confounding factors associated with EoV while detecting and localizing damage even with noisy measurements.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.185-195
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• 2018

In the Seoul Metropolitan City, the PM(pariculate matter) application used by the citizens provides the PM concentration of the nearest monitoring stations located on the PM monitoring stations. Currently, the selecting method of the PM monitoring network considered by the Ministry of Environment is based on considering the monitoring station distribution and population density only. In this study, we analyzed the distance between PM monitoring station and the administrative center point in addition to the above considerations. The number of test sites was verified and the range of coverage of each monitoring stations was indicated by using the Voronoi algorithm and hexagon grid. The spatial position problem of the PM monitoring station was suggested by spatial data analysis. The variables of spatial data analysis are single-family houses, apartments, $1^{st}$ class neighborhood, $2^{nd}$ class neighborhood, garbage disposal plant, hazardous material disposal facility, factory, and the density map. The analysis result of the selection criterion considering the additional variables for new PM monitoring stations was presented, in addition to the selection criteria provided by the Ministry of Environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.437-443
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• 2013

This paper was developed a monitoring and control system to use reactive power control algorithm. This algorithm could be improved voltage stability in power system. This method was controlled the voltage for stability improvement, effective usage of reactive power, and the increase of the power quality. PMS(Power Management System) has been calculate voltage sensitivity, and control reactive power compensation device. The voltage control was used to the FACTS, MSC/MSR(Mechanically Switched Capacitors/Reactors), and tap of transformer in power system. The reactive power devices in power system were control by voltage sensitivity ranking of each bus. Also, to secure momentary reactive power, it had been controlled as the rest of reactive power in the each bus. In here, reactive power has been MSC/MSR. The simulation result, First control was voltage control as fast response control of FACTS. Second control was voltage control through the necessary reactive power calculation as slow response control of MSR/MSR. Third control was secured momentary reactive reserve power. This control was method by cooperative control between FACTS and MSR/MSC. Therefore, the proposed algorithm was had been secured the suitable reactive reserve power in power system.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.1
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• pp.97-109
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• 2007

Vertical distribution and optical properties of atmospheric aerosols above the Korean peninsula are quite important to estimate effects of aerosol on atmospheric environment and regional radiative forcing. For the first time in Korea, vertical microphysical properties of atmospheric aerosol obtained by inversion algorithm were analyzed based on optical data of multi-wavelength Raman lidar system developed by the Advanced Environmental Monitoring Research Center (ADEMRC), Gwangju Institute Science and Technology (GIST). Data collected on 14 June 2004 at Gwangju ($35.10^{\circ}N,\;126.53^{\circ}E$) and 27 May 2005 at Anmyeon island ($36.32^{\circ}N,\;126.19^{\circ}E$) were used as raw optical data for inversion algorithm. Siberian forest fire smoke and local originated haze were observed above and within the height of PBL, respectively on 14 June 2004 according to NOAA/Hysplit backstrajectory analysis. The inversion of lidar optical data resulted in particle effective radii around $0.31{\sim}0.33{\mu}m$, single scattering albedo between $0.964{\sim}0.977$ at 532 nm in PBL and effective radii of $0.27{\mu}m$ and single scattering albedo between $0.923{\sim}0.924$ above PBL. In the case on 27 May 2005, biomass burning from east China was a main source of aerosol plume. The inversion results of the data on 27 May 2005 were found to be particle effective radii between $0.23{\sim}0.24{\mu}m$, single scattering albedo around $0.924{\sim}0.929$ at 532 nm. Additionally, the inversion values were well matched with those of Sun/sky radiometer in measurement period.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1231-1244
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• 2020

Landsat satellites are representative optical satellites that have observed the Earth's surface for a long-term, and are suitable for long-term changes such as disaster preparedness/recovery monitoring, land use change, change detection, and time series monitoring. In this paper, clouds and cloud shadows were detected using QA bands to detect and remove clouds simply and efficiently. Then, the missing area of the experimantal image is restorated through the SSG algorithm, which does not directly refer to the pixel value of the reference image, but performs restoration to the pixel value in the Experimental image. Through this study, we presented the possibility of utilizing the modified SSG algorithm by quantitatively and qualitatively evaluating information on variousl and cover conditions in the thermal wavelength band as well as the visible wavelength band observing the surface.

• Smart Structures and Systems
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• v.16 no.3
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• pp.415-433
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• 2015

Tendon failures in bonded post-tensioned bridges over the last two decades have motivated ongoing investigations on various aspects of unbonded tendons and their monitoring methods. Recent research shows that change of strain distribution in anchor heads can be useful in detecting wire breakage in unbonded construction. Based on this strain variation, this paper develops a damage detection model that enables an automated tendon monitoring system to identify and locate wire breaks. The first part of this paper presents an experimental program conducted to study the strain variation in anchor heads by generating wire breaks using a mechanical device. The program comprised three sets of tests with fully populated 19-strand anchor head and evaluated the levels of strain variation with number of wire breaks in different strands. The sensitivity of strain variation with wire breaks in circumferential and radial directions of anchor head in addition to the axial direction (parallel to the strand) were investigated and the measured axial strains were found to be the most sensitive. The second part of the paper focuses on formulating the wire breakage detection framework. A finite element model of the anchorage assembly was created to demonstrate the algorithm as well as to investigate the asymmetric strain distribution observed in experimental results. In addition, as almost inevitably encountered during tendon stressing, the effects of differential wedge seating on the proposed model have been analyzed. A sensitivity analysis has been performed at the end to assess the robustness of the model with random measurement errors.

• Journal of Biomedical Engineering Research
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• v.35 no.4
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• pp.87-94
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• 2014

Recent technological advances have increased interest in personal health monitoring. Electrocardiogram(ECG) monitoring is a basic healthcare activity and can provide decisive information regarding cardiovascular system status. In this study, we developed a capacitive ECG measurement system that can be included within a cloth mattress pad. The device permits ECG data to be obtained during sleep by using capacitive electrodes. However, it is difficult to detect R-wave peaks automatically because signals obtained from the system can include a high level of noise from various sources. Because R-peak detection is important in ECG applications, we developed an algorithm that can reduce noise and improve detection accuracy under noisy conditions. Algorithm reliability was evaluated by determining its sensitivity(Se), positive predictivity(+P), and error rate(Er) by using data from the MIT-BIH Polysomnographic Database and from our capacitive ECG system. The results showed that Se = 99.75%, +P = 99.77%, and Er = 0.47% for MIT-BIH Polysomnographic Database while Se = 96.47%, +P = 99.32%, and Er = 4.34% for our capacitive ECG system. Based on those results, we conclude that our R-peak detection method is capable of providing useful ECG information, even under noisy signal conditions.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.675-680
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around the world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance. CMS(Condition Monitoring System) can be used to aid plant operator in achieving these goals. Its aim is to provide operators with information regarding th e health of their machine, which in turn, can help them improve operation efficiency. In this work, wind turbine fault diagnostic algorithm which can diagnose the mass unbalance and aerodynamic asymmetry of the blades is proposed. Proposed diagnostic algorithm utilizes both FFT(Fast Feurier Transform) of the signal from accelerometers installed inside of nacelle and simple diagnostic logic. Furthermore, to verify the applicability of the proposed system, 3W small sized wind turbine system is tested and physical experiments are carried out.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.187-199
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• 2022

Kriging metamodel, as a flexible machine learning method for approximating deterministic analysis models of an engineering system, has been widely used for efficiently estimating slope reliability in recent years. However, the autocorrelation function (ACF), a key input to Kriging that affects the accuracy of reliability estimation, is usually selected based on empiricism. This paper proposes an adaption of the Kriging method, named as Genetic Algorithm optimized Whittle-Matérn Kriging (GAWMK), for addressing this issue. The non-classical two-parameter Whittle-Matérn (WM) function, which can represent different ACFs in the Matérn family by controlling a smoothness parameter, is adopted in GAWMK to avoid subjectively selecting ACFs. The genetic algorithm is used to optimize the WM model to adaptively select the optimal autocorrelation structure of the GAWMK model. Monte Carlo simulation is then performed based on GAWMK for a subsequent slope reliability analysis. Applications to one explicit analytical example and two slope examples are presented to illustrate and validate the proposed method. It is found that reliability results estimated by the Kriging models using randomly chosen ACFs might be biased. The proposed method performs reasonably well in slope reliability estimation.